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scraping/output/2221413558556643221.txt
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scraping/output/2221413558556643221.txt
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MLOps Natural language processing πŸš€ Unleash the Power of Large Language Models and Foundation Models - Read our insights and expertise on these trends in AI! Discover more Services Navigate intelligence Activate intelligence Build intelligence AI Solutions Custom solutions Solutions catalogue Domains of expertise References Client cases Customer testimonials Resources Resource library Blog Video & demo Press Events Open Source Our DNA How we work Mission & ambition Responsible AI Partners ML6 For Good Careers Jobs Team Working at ML6 Taal NLEN Contact us en defrNL Contact us Blogposts All Posts October 17, 2022 # Webinar | Hybrid AI: successfully combining expert knowledge with ML models Contributors Matthias Feys Q / CTO No items found. Subscribe to newsletter Sign up By clicking Sign Up you're confirming that you agree with our Terms and Conditions. Thank you! Your submission has been received!
scraping/output/7371013274892921836.txt
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Q / CTO No items found. Subscribe to newsletter Sign up By clicking Sign Up you're confirming that you agree with our Terms and Conditions. Thank you! Your submission has been received! Oops! Something went wrong while submitting the form. Share this post Machine learning opened up new ways of solving technical challenges by training models on data instead of directly implementing rules & logic. This offers a lot of new opportunities for solving difficult problems. However, sometimes it can also be useful to combine these machine learning models with (expert) rules, to get the best possible outcome and leverage the benefits of both expert knowledge as well as machine learning models. Hybrid AI is the name of this field, and focuses on combining non-symbolic AI (eg. machine learning), with symbolic AI (eg. expert rules). Our speakers, Prof. Sofie Van Hoecke (PreDiCT) and Matthias Feys (ML6), will give you an overview of this field by tackling the following topics:
scraping/output/7371013274892921836.txt
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* Why and when Hybrid AI is relevant for your situation. * An overview of different ways to combine rules with machine learning models. * Concrete examples where hybrid AI was implemented. ‍ #### Get access to the webinar by filling in the form below. ‍ ## Related posts View all No results found. There are no results with this criteria. Try changing your search. Large Language Model Large Language Model Foundation Models Foundation Models Corporate Corporate People People Structured Data Structured Data Chat GPT Chat GPT Sustainability Sustainability Voice & Sound Voice & Sound Front-End Development Front-End Development Data Protection & Security Data Protection & Security Responsible/ Ethical AI Responsible/ Ethical AI Infrastructure Infrastructure Hardware & sensors Hardware & sensors MLOps MLOps Generative AI Generative AI Natural language processing Natural language processing Computer vision Computer vision
scraping/output/7371013274892921836.txt
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scraping/output/7371013274892921836.txt
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πŸš€ Unleash the Power of Large Language Models and Foundation Models - Read our insights and expertise on these trends in AI! Discover more Services Navigate intelligence Activate intelligence Build intelligence AI Solutions Custom solutions Solutions catalogue Domains of expertise References Client cases Customer testimonials Resources Resource library Blog Video & demo Press Events Open Source Our DNA How we work Mission & ambition Responsible AI Partners ML6 For Good Careers Jobs Team Working at ML6 Taal NLEN Contact us en defrNL Contact us # Structured Data Keep your knowledge in safe hands Learn moreContact us ## Driven by data Many business processes are recorded in tabular datasets like Excel sheets, relational databases, and time series. Thanks to our Machine Learning expertise, we turn your structured data into valuable insights that help you solve a variety of problems. ### Regression & forecasting
scraping/output/-4005684865848025300.txt
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### Regression & forecasting Predicting the future is hard, but with the right tools, we can forecast trends in e.g. energy consumption or sales volume with precision. We do this by using external data sources and taking advantage of the latest model improvements. ### Classification & clustering Labeling data records adds value to large data sets and automates actions. It involves creating different labels (clustering) and assigning them to new data (classification). This technique can be used for detecting machine failures, sales abandonment, and clustering e-commerce users. ### Anomaly detection We are experts in detecting anomalies in machine and process behavior. Our strategy is to separate "abnormal" events from "normal" behavior to gain insights into causality and explainability and use them to optimize your systems. ### Operational research & optimization
scraping/output/-4005684865848025300.txt
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### Operational research & optimization Even if a process works well, it can always be improved. This is where our expertise comes in. We specialize in tackling complex problems such as production planning, job scheduling, vehicle routing, box packing and more. ## Client cases Discover how our expertise in Hardware & Sensors leads businesses to success Placeholder tag The AI-Driven NGO : A data-driven approach to creating a better future for children By gaining insights into the donor journey, efficiency and effectiveness of fundraising could be enhanced. November 19, 2021 By This is some text inside of a div block. Public & Professional Services This is some text inside of a div block. Placeholder tag Building a recommendation engine for March Real Estate Ml6 built a proactive recommendation tool resulting in a 7x boost in the number of leads from the matching engine. May 28, 2021 By This is some text inside of a div block. Public & Professional Services
scraping/output/-4005684865848025300.txt
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May 28, 2021 By This is some text inside of a div block. Public & Professional Services This is some text inside of a div block. Previous 3 / 3 Large Language Model Foundation Models Corporate People Structured Data Chat GPT Sustainability Voice & Sound Front-End Development Data Protection & Security Responsible/ Ethical AI Infrastructure Hardware & sensors MLOps Generative AI Natural language processing Computer vision ## Typical challenges With our expertise, we can help you overcome structured data challenges in AI. ## Aligning the technical problem formulation with business problem
scraping/output/-4005684865848025300.txt
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Computer vision ## Typical challenges With our expertise, we can help you overcome structured data challenges in AI. ## Aligning the technical problem formulation with business problem Before starting machine learning (ML) model training, you need to understand the business requirements and available data. This includes deciding whether the problem should be approached as a regression or classification task, or whether ranking or recommendation is required. The success of the technical implementation is ultimately determined by meeting business expectations, which we always strive to achieve. ## Validation is hard
scraping/output/-4005684865848025300.txt
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## Validation is hard Unsupervised learning uses tools like clustering to identify data patterns, but the results can be difficult to interpret. That’s why domain experts during development need to make sure that the outcome is accurate. It’s also tough to identify causal relationships between variables and labels may not be available in situations like fraud or predicting machine failures. However, once you overcome these challenges with our help, unsupervised learning is a powerful tool for uncovering hidden patterns and gaining insights from data. ## Data engineering and change management
scraping/output/-4005684865848025300.txt
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## Data engineering and change management Building a successful solution for structured data requires a lot of data engineering and change management effort. Moreover, machine learning system development can lead to hidden technical problems such as poor data quality, model complexity and deployment challenges. To create long-term value, it’s not enough to simply train an ML model. Validation, integration into existing systems, ongoing monitoring and updating are essential to deliver real value over time. We help you do just that. ## High level outline of the solution Data collection The first step in any structured data solution is data collection. This can be done from a variety of sources, including internal databases, APIs, and third- party data providers. Data cleaning and preprocessing
scraping/output/-4005684865848025300.txt
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Data cleaning and preprocessing Once data has been collected, it must be cleaned and preprocessed to make sure that it is of high quality. This includes tasks like removing missing values, handling outliers, and converting data types. Exploratory Data Science (EDA) is essential. Feature engineering Feature engineering involves selecting and transforming the features in the data that are most relevant to the problem at hand. This can include creating new features, selecting important features, and scaling or normalizing features. Model training and selection A machine learning model can be trained after data has been preprocessed and features have been engineered. The selection of a specific model is based on how well it performed on a holdout dataset. Deployment and monitoring Once the model has been trained and evaluated, it must be deployed into production. This involves integrating the model into existing systems and workflows and monitoring its performance over time.
scraping/output/-4005684865848025300.txt
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Once the model has been trained and evaluated, it must be deployed into production. This involves integrating the model into existing systems and workflows and monitoring its performance over time. ## Related posts No results found. There are no results with this criteria. Try changing your search. Large Language Model Foundation Models Corporate People Structured Data Chat GPT Sustainability Voice & Sound Front-End Development Data Protection & Security Responsible/ Ethical AI Infrastructure Hardware & sensors MLOps Generative AI Natural language processing Computer vision contact us ## Connect with our AI experts in Structured Data Contact us to turn your structured data into valuable insights that help you solve a variety of problems Name * Company * Email * How did you hear about us?
scraping/output/-4005684865848025300.txt
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scraping/output/-4005684865848025300.txt
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scraping/output/-4005684865848025300.txt
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scraping/output/-4422453358527678687.txt
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Contributors No items found. Subscribe to newsletter Sign up By clicking Sign Up you're confirming that you agree with our Terms and Conditions. Thank you! Your submission has been received! Oops! Something went wrong while submitting the form. Share this post In this blog, we will uncover some pressing challenges within the Life Science manufacturing industry and how breakthroughs in Machine Learning techniques offer measurable returns. ### From batch to continuous manufacturing and pharma 4.0
scraping/output/-4422453358527678687.txt
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Pharmaceutical companies are often in a race against time. Although patents protect companies intellectual property, most of this time is spent turning an idea into a marketable product. Traditionally medicines are produced in the old-fashioned way by a batch process [3]. This traditional batch process has proven to have high lead times, because after each process step the production is stopped to test for quality assurance. Sometimes these materials are stored in containers or shipped to other facilities before they continue in the process [5]. Each stop increases the lead time and can cause defects and scrap [6]. As an indication, lead times can be up to 365 days of which 228 are dedicated to drug substance production, 75 to drug product formulation and 41 to packaging. Inventories including raw-material storage can last 250 days [1]. Reducing these times is essential to recover the billions spent in drug
scraping/output/-4422453358527678687.txt
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to packaging. Inventories including raw-material storage can last 250 days [1]. Reducing these times is essential to recover the billions spent in drug development given the fact that there only a few years left before the patents are expired.
scraping/output/-4422453358527678687.txt
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The pharmaceutical industry is often compared to the semiconductor industry due to the high costs and the need for high throughput, volume and yield in a clean environment with high consistency [2]. The semiconductor industry is already quite matured when it comes to implementing industry 4.0 and this has resulted in major advancement in technology (i.e. smaller chips with greater capabilities in computers, phones, etc.). But what is the state of Pharma 4.0? How is this industry moving towards the future? When compared to the semiconductor industry there is a difference in the demanding regulations enforced by authorities like the US FDA and the EU Commission to ensure the quality. Changes to production in the form of digitization can result in changes to machines, processes and even the product itself. It is these strict regulations which are a possible cause of the conservative character of the industry when compared to the semiconductor industry.
scraping/output/-4422453358527678687.txt
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Today, next to the stringent manufacturing requirements, the industry is entering an era of smaller batches and personalized medicine. Medicine is designed with more unique features and needs to be delivered quicker to patients in need [6]. In other words, drug production requires very small batches often measuring in sub-liters tuned to individualized genomes. [1].
scraping/output/-4422453358527678687.txt
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To allow smaller batches and more cost effective production of drugs, the industry is changing to continuous flow manufacturing as shown in Figure 1. Small amounts of chemical ingredients flow without disruption from raw ingredients to tablet. In 2016 the FDA encouraged manufacturers to transition from batch to continuous manufacturing due to its many benefits [6]. This new method has the potential to cut drug manufacturing times by 90% and costs by 30-50% according to Novartis [9] or gain an additional $50 billion in annual revenue [1]. However, it required to have a seamless process integration and full process control, which can be achieved by operational data and automation. Leading pharma giants have already been working for multiple years on continuous manufacturing e.g. Johnson & Johnson Janssen won the FDA’s approval to switch from batch to continuous manufacturing [10] and Novartis entered a 10-year research collaboration program with the Massachusetts
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approval to switch from batch to continuous manufacturing [10] and Novartis entered a 10-year research collaboration program with the Massachusetts Institute of Technology (MIT) in 2007 [11].
scraping/output/-4422453358527678687.txt
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Figure 1: Conceptual continuous manufacturing process compared to a typical batch process for the pharmaceutical industry by Lee et al. [8] left and right is a Novartis vision of continuous manufacturing in cooperation with MIT [11] ‍ ### So how can AI help with drug manufacturing? No matter if you have a batch process or a continuous manufacturing process, AI projects require data and a process which is repetitive, has a measurable outcome and a certain uncertainty in order to be successful, as discussed in ML6s blog on how to boost your manufacturing process with AI [12]. Data can be in several forms, from microscopic lab data, to visual inspection data to IOT data gathered from the machine and visualized real-time in dashboards. Tips and tricks on the latter can be found in the following blogs [13] and [14].
scraping/output/-4422453358527678687.txt
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As discussed above, continuous manufacturing processes require ultimate process and quality control. One of the advantages AI can offer is connecting the sensor data to lab results as depicted in Figure 2. Thanks to the use of open standards, such as OPC-UA on PLCs, incorporating machines and lines from different brands allows for easy access to your machine data. The data can be stored in a suitable database such as InfluxDB or Prometheus. Instead of manually testing a few samples per X hours, predictive algorithms can predict lab-results using real-time data for every single batch or drug. Figure 2: Use machine learning to predict the quality of your drug for every single batch. ‍
scraping/output/-4422453358527678687.txt
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Figure 2: Use machine learning to predict the quality of your drug for every single batch. ‍ Next to quality control by IOT data, one can use machine learning algorithms to automate visual inspection of medicine foil strips checking for container and closure, information which is written on the label (such as brand name, ingredient names, manufacturer name and logo, batch/lot number, expire data etc.), and physical characteristics of the tablets/capsules (such as uniformity of shape, size, colour, texture, breaks, cracks, splits, markings, empty capsules etc.). Even on a microscopic scale you can e.g. classify microorganisms in Pharmaceutical Microbiology or particle size detection. A common technique used in Machine Learning is image segmentation to distinguish common objects or microscopic particles as shown in Figure 3.
scraping/output/-4422453358527678687.txt
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Figure 3: Image segmentation techniques for common objects shown in the left by Lin et al. [16] and applied on an image of a Scanning Electron Microscope (SEM) ‍ The first step of improving efficiencies is to create insight in your manufacturing machine data. An example is the Bosch Pharma i 4.0 Starter Edition [17] which offers condition monitoring, event tracking and measuring Overall Equipment Effectiveness (OEE). Similar dashboards can be created customized using open source technologies such as InfluxDB and Grafana [13]. Historical data provides the possibility to do anomaly detection or predictive maintenance. Note that analysis on track and trace data for traceability purposes can also be performed to make sure you are fully compliant to regulations. Next predictive algorithms can be developed to create foresight to operators or higher management.
scraping/output/-4422453358527678687.txt
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Countless parameters in a manufacturing process can be mapped and optimized. Doing this manually is a tedious job and basically impossible due to the amount of possibilities and varying conditions. Using machine learning, you can implement a full autonomous parameter optimizer using a self-learning system to find the optimal solution in every circumstance to increase efficiency. Sample use cases are the improvement of a powertrain on an electrical bike and reducing the energy consumption of a datacenter, and this can be applied to drug manufacturing as well [12]. Improving production, quality and safety days while reducing consumption of raw materials. A schematic outline is shown in Figure 4 of such a self-learning algorithm. Figure 4: Use machine learning to improve manufacturing efficiencies and optimize the production process ‍
scraping/output/-4422453358527678687.txt
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Medicine often have different lead times and specific transport or storage requirements. Specific requirements are: drugs which require temperature control resulting in limited shelf life; flammable or explosive drugs which need to be handled carefully; narcotics or psychotropic drugs which require close monitoring due to stringent regulations etc. These requirements can cause complexities when it comes to storing and transportation or finding the optimal planning and demand forecasting. Using machine learning techniques one can optimize the planning and forecasting both inside and outside manufacturing facilities. An example of a self-learning algorithm can be found in Figure 5 where the optimal route is found for a single driver in an environment with no obstacles (left) or a multi driver optimization problem with multiple obstacles. The transport/storage requirements act as the obstacles in this example. Note that similar AI techniques can be used for
scraping/output/-4422453358527678687.txt
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with multiple obstacles. The transport/storage requirements act as the obstacles in this example. Note that similar AI techniques can be used for demand forecasting in your supply chain.
scraping/output/-4422453358527678687.txt
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Figure 5: Route optimization using machine learning techniques can be applied both for optimizing planning inside manufacturing facilities and for supply chain purposes such as demand forecasting ‍ More information on how to apply AI in drug manufacturing can be found in this video. ‍ ### On a final note... To enhance time to market and quicken development time of AI solutions, a comparison can be made with the semiconductor industry where β€œASML boosts engineering speed, strengthens security, accelerates time to market, and enhances competitive advantage by adding Google Cloud to its on-premises machine learning solutions”. Examples like these show how the pharmaceutical industry can catch up on the semiconductor industry and move to the industry 4.0 which can result in great technological achievements, increased profit and reduced time to markets. ## Related posts View all Placeholder tag
scraping/output/-4422453358527678687.txt
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## Related posts View all Placeholder tag Leveraging Artificial Intelligence for insight-driven commercial models in life sciences In this blogpost, we’ll explain how AI can help solve typical challenges in the commercial model of life sciences companies. April 26, 2021 By Sven Rymenans Life Sciences & Healthcare Large Language Model Large Language Model Foundation Models Foundation Models Corporate Corporate People People Structured Data Structured Data Chat GPT Chat GPT Sustainability Sustainability Voice & Sound Voice & Sound Front-End Development Front-End Development Data Protection & Security Data Protection & Security Responsible/ Ethical AI Responsible/ Ethical AI Infrastructure Infrastructure Hardware & sensors Hardware & sensors MLOps MLOps Generative AI Generative AI Natural language processing Natural language processing Computer vision Computer vision Accelerating businesses with AI technology & experts Contact:
scraping/output/-4422453358527678687.txt
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scraping/output/-4422453358527678687.txt
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Structured Data πŸš€ Unleash the Power of Large Language Models and Foundation Models - Read our insights and expertise on these trends in AI! Discover more Services Navigate intelligence Activate intelligence Build intelligence AI Solutions Custom solutions Solutions catalogue Domains of expertise References Client cases Customer testimonials Resources Resource library Blog Video & demo Press Events Open Source Our DNA How we work Mission & ambition Responsible AI Partners ML6 For Good Careers Jobs Team Working at ML6 Taal NLEN Contact us en defrNL Contact us Blogposts All Posts Life Sciences & Healthcare April 26, 2021 # Leveraging Artificial Intelligence for insight-driven commercial models in life sciences Contributors Sven Rymenans Sales Consultants No items found. Subscribe to newsletter Sign up By clicking Sign Up you're confirming that you agree with our Terms and Conditions. Thank you! Your submission has been received!
scraping/output/5022103063578893462.txt
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No items found. Subscribe to newsletter Sign up By clicking Sign Up you're confirming that you agree with our Terms and Conditions. Thank you! Your submission has been received! Oops! Something went wrong while submitting the form. Share this post ### Introduction In an Accenture survey (1), more than 90% of life sciences executives recognized artificial intelligence as important in driving innovation and achieving outcomes such as hyper-personalized experiences, new sources of growth, and new levels of efficiency. As their market pivots to the patient with more individualized treatments and value-based models, life sciences companies must also shift their commercial models.
scraping/output/5022103063578893462.txt
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Traditionally, the lion’s share of pharma promotional strategy and investment has been focused on the interactions between the HCP (HealthCare Practitioner) and sales representative. Other promotional channels are meetings and events, service team calls, inside sales, digital, educational activities, etc. For sales organisations, it’s hard to measure the impact of each of these channels and how they influence each other. Also, defining the most effective channel mix for a specific HCP is not easy. Through segmentation and targeting, commercial teams aim to tailor their efforts to groups of similar HCPs but this is often based on limited information, leading to inaccurate and too broadly defined HCP segments. No surprise that 1 out of 2 life sciences commercial leads said they don’t have a good understanding of what their customers need and want.
scraping/output/5022103063578893462.txt
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The AI revolution on the commercial side of the pharma business has been slower on the uptake than on the R&D side, but we see great opportunities to improve the commercial model through AI in many ways, of which 2 of them we’ll detail in this blogpost. ### Segmentation & Targeting
scraping/output/5022103063578893462.txt
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### Segmentation & Targeting Commercial organisations in Life Sciences often use HCP information like the number of patients treated for a specific disease, or the % of adoption to its product as a way to segment HCPs. A classical segmentation could be Gold- Silver-Bronze, with Gold referring to HCPs that treat more than 20 patients with a specific disease per week. Silver means 10 to 20 patients with that disease per week, and bronze means 0 to 10 patients. As a consequence, sales representatives are incentivized to visit Gold HCPs more frequently than Silver HCPs which they visit more frequently than bronze HCPs. This example shows that only a limited amount of information is used to segment the HCP population, and that a one-size fits all approach (at least per segment) is used. With AI, we can radically change this approach, and create a segment-of- one for each HCP. Curious to know how we do that?
scraping/output/5022103063578893462.txt
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Well, in order to address this we use what is called an embedding space. For the non-techies reading this post, please bear with me for just a few seconds. An embedding is a relatively low-dimensional space into which you can translate high-dimensional vectors. Embeddings make it easier to do machine learning on large inputs like sparse vectors representing words. Okay, so far for the definition, but how can this help me improve my segmentation and targeting you might ask. In essence this technique allows the use of all available data about HCPs, in any format (full text, database, time series, image, spoken text, etc.). Think of conversations, interactions with your HCP portal, click behaviour on your website, specialty, potential, adoption, interviews, medical facility, age, hobbies, geography, etc. For each of these β€˜dimensions’ the embedding space maps the values (e.g. age number) on an axes to distinguish between HCPs. The illustration below gives 2 examples of what
scraping/output/5022103063578893462.txt
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β€˜dimensions’ the embedding space maps the values (e.g. age number) on an axes to distinguish between HCPs. The illustration below gives 2 examples of what this looks like for a combination of 2 dimensions (e.g. gender and royalty in the left example).
scraping/output/5022103063578893462.txt
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The illustrations show this in a 3 dimensional space, as this is the maximum number of dimensions that can be visually illustrated, but in fact the number of dimensions used in the embedding space can grow to infinity. But let’s stick with the 3D visualisation for simplicity. So by doing this exercise with all data at hand, every HCP will be given specific coordinates in the embedding space. The closer that two HCPs are located in this embedding space, the more similar they are.
scraping/output/5022103063578893462.txt
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So for example, a general practitioner living in location ABC might appear to be very similar to a pneumologist in location XYZ, because they attended the same university together, are of young age so prefer digital channels, practice the same hobbies and both frequently attend conferences. These two HCPs should be targeted in the same way based on the information coming from the embedding space, whereas traditional methods would target them differently based on limited information (e.g. only potential). We at ML6 applied and benchmarked this technique of hyper personalisation at a multinational company and outperformed the other techniques by 150%. ### Commercial Execution
scraping/output/5022103063578893462.txt
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We at ML6 applied and benchmarked this technique of hyper personalisation at a multinational company and outperformed the other techniques by 150%. ### Commercial Execution Analysis of the responsiveness of sales to promotional activities can be done through the smart use of data. Measuring brand sensitivity to promotion prior to investment decisions or considering implementation of new channels is a crucial step to maximize return on investment. Typical questions that arise are: β€œWhich channels contribute significantly to the brand sales? What are my incremental sales per extra unit of investment? What is the optimal point of investment? What are the major sales drivers? How do sales drivers vary across regions or across promotional channels? What is the level of carry-over for a brand (base)? What is the optimal activity mix? etc.”
scraping/output/5022103063578893462.txt
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To answer these questions we make use of the unobserved components (UCM) time series model. This model was first introduced to the econometrics and statistics fields by A.C Harvey (1989). UCM can be considered to be a multiple regression model with time varying coefficients. It is based on the principles that it is useful to view time series as being decomposable into a trend, seasonal and cycle component. Advanced modeling techniques (State Space modeling) are used to isolate, quantify and optimize the short-term impact of promotional activities on Sales. #### Input data: #### Model decomposition:
scraping/output/5022103063578893462.txt
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#### Input data: #### Model decomposition: Our model takes into consideration the carry-over effect (e.g. my weight this year is impacted by my weight at the beginning of the year (starting point) and how it evolved the years before), seasonal trends (e.g. ice cream sells better in summer than winter) and other known parameters (short and long term). It also accounts for the fact that part of the generated sales can be attributed to patients initiating treatment at a hospital continue using the prescribed drug after discharge from hospital, i.e. the hospital spill-over effect. This is all represented in the β€˜Base’ (grey area in the model chart). This technique allows to account for the parameters that have influenced sales which we are not aware of or we do not have an accurate way to track them, e.g. competitor incentives. Therefore, sales impact is not misattributed to other channels such as e.g. traditional calls.
scraping/output/5022103063578893462.txt
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Next to that, the model also accounts for what is called the memory (ad-stock effect). This refers to the impact that marketing activities have over time on sales or brand health: It captures how response to advertising builds and decays in consumer markets. This concept agrees with common sense that the awareness level of a new exposure will be higher if there have been exposures in the fairly recent past and lower if there have not been. Finally, the delay in impact of an activity and diminishing returns over time are accounted for as well. The model shows the impact of each promotional channel on sales, but can also be used to analyse responsiveness to additional investment in a promotional channel, which we will detail in a following blogpost. ### Conclusion
scraping/output/5022103063578893462.txt
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### Conclusion The fact that science and technology are converging to enable more personalized, precise treatments for patients should also trigger sales and marketing professionals to apply similar techniques for more precise targeting and more effective commercial efforts. The current state of modelling techniques and hyper personalisation allows to radically improve sales and marketing operations for Life Sciences companies. More information on how to apply AI in drug sales & marketing can be found in this video. ## Related posts View all Placeholder tag Pharma 4.0 : Impact drug manufacturing with AI in Life Science Industries In this blog, we will uncover some pressing challenges within the Life Science manufacturing industry and how breakthroughs in Machine Learning techniques offer measurable returns. April 28, 2021 By Life Sciences & Healthcare Large Language Model Large Language Model Foundation Models Foundation Models Corporate Corporate
scraping/output/5022103063578893462.txt
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April 28, 2021 By Life Sciences & Healthcare Large Language Model Large Language Model Foundation Models Foundation Models Corporate Corporate People People Structured Data Structured Data Chat GPT Chat GPT Sustainability Sustainability Voice & Sound Voice & Sound Front-End Development Front-End Development Data Protection & Security Data Protection & Security Responsible/ Ethical AI Responsible/ Ethical AI Infrastructure Infrastructure Hardware & sensors Hardware & sensors MLOps MLOps Generative AI Generative AI Natural language processing Natural language processing Computer vision Computer vision Accelerating businesses with AI technology & experts Contact: [email protected] +32 9 265 95 50 Contact us Join our newsletter By subscribing you agree to with our Privacy Policy Thank you! Your submission has been received! Oops! Something went wrong while submitting the form. Services
scraping/output/5022103063578893462.txt
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scraping/output/5022103063578893462.txt
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scraping/output/5921266941805258048.txt
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scraping/output/5921266941805258048.txt
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MLOps πŸš€ Unleash the Power of Large Language Models and Foundation Models - Read our insights and expertise on these trends in AI! Discover more Services Navigate intelligence Activate intelligence Build intelligence AI Solutions Custom solutions Solutions catalogue Domains of expertise References Client cases Customer testimonials Resources Resource library Blog Video & demo Press Events Open Source Our DNA How we work Mission & ambition Responsible AI Partners ML6 For Good Careers Jobs Team Working at ML6 Taal NLEN Contact us en defrNL Contact us Blogposts All Posts August 31, 2021 # Vertex Pipelines β€” Vertex AI vs AI Platform Contributors Liam Campbell Data Engineer No items found. Subscribe to newsletter Sign up By clicking Sign Up you're confirming that you agree with our Terms and Conditions. Thank you! Your submission has been received! Oops! Something went wrong while submitting the form. Share this post
scraping/output/-2930323519026450185.txt
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Share this post Recently, Google unveiled their latest offering in ML Tools on their Google Cloud Platform, Vertex AI. In brief, the new platform seeks to combine the tools offered previously by separate services on GCP, such as AI Platform and AutoML, into a single service. Integrating these previously separate services brings benefits to users by giving them the ability to interact with all these tools using a single set of APIs, SDKs, and Clients. For a more detailed overview of the aims and features of Vertex AI as a whole, check out this previous ML6 Blog Post. In this blog post, we will focus primarily on Vertex AI’s answer to AI Platform Pipelines, Vertex Pipelines. ML Pipeline technologies such as Vertex Pipelines are important to any MLOps Team as tools for orchestrating the many moving parts of complex training and prediction jobs at scale. They are a key piece of the infrastructure that brings Machine Learning capabilities into a production setting. ‍
scraping/output/-2930323519026450185.txt
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‍ ### AI Platform Pipelines ‍ Previously in AI Platform, Google’s former Machine Learning platform, we had AI Platform Pipelines. This was a service aimed at making it easy to deploy Kubeflow Pipelines, the MLOps Pipeline toolkit from Kubeflow, to Google Cloud Platform resources. The workflow for deploying a Kubeflow Pipeline with AI Platform looked something like the following; Steps to deploying Kubeflow on AI Platform ‍ #### 1\. Set Up Kubernetes Cluster with Google Kubernetes Engine ‍ The first step in deploying Pipelines to AI Platform was setting up a cluster on which to host our Kubeflow Pipelines Client. Although here at ML6 would always caution our clients to automate their infrastructure with tools like Terraform, provisioning a cluster with GKE was made very easy via neat user interfaces in the GKE Console. ‍ #### 2\. Deploy Kubeflow Client to GKE Cluster ‍
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‍ #### 2\. Deploy Kubeflow Client to GKE Cluster ‍ With our cluster up and running, we could easily deploy Kubeflow Pipelines instances to it using the AI Platform Pipelines UI in the GCP console. Creating a new deployment was as simple as selecting your GKE Cluster from a drop down list and filling out a few pieces of configuration in a simple form. The default behavior was to use nodes in the Kubernetes cluster to host MySQL and MinIO services, Kubeflow’s default for Artifact and Metadata storage, but by providing connection details in setup GCS and Cloud Storage can be used as more scalable and reliable alternatives. ‍ #### 3\. Develop Pipelines with Notebooks ‍
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‍ #### 3\. Develop Pipelines with Notebooks ‍ With the cluster setup and the Kubeflow instance created we could use the Notebooks of AI Platform as secure development environments for working with the Kubeflow Pipelines SDK to develop our pipelines. In AI Platform we are simply using vanilla Kubeflow Pipelines tools on GCP resources, so all of the standard Kubeflow SDK features would work exactly as if you spun up and Kubeflow Pipelines instance on an on-premises Kubernetes Cluster. ‍ #### 4\. Manage Pipelines and Runs in Kubeflow Client UI The developed pipelines could then be uploaded to the Kubeflow client, where you could see all previously uploaded pipelines, launch runs of these pipelines, and view the DAG and outputs of ongoing and completed Pipeline runs. Pipelines could also be uploaded, and runs started/ monitored via the Kubeflow Client API using functions defined in the python SDK.
scraping/output/-2930323519026450185.txt
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This workflow made it very easy to work with Kubeflow Pipelines in Google resources, with deployment taking 5 minutes (if you don’t include the time it takes for GCP to spin up the resources in the background). Thanks to GKE, Kubernetes cluster management was as easy as it had ever been, and thanks to AI Platform Pipelines, deploying Kubeflow instances to those clusters was even easier! Despite this, ML Teams still needed to have Kubernetes skills in order to make informed decisions, properly configure their cluster, and generally make the best use of AI Platform pipelines and the GKE Cluster it would be deployed too. ‍ ### Vertex AI & Vertex Pipelines ‍ One of the first things one might notice moving from AI Platform Pipelines to Vertex Pipelines, is that this extrapolation of resource management away from the user has continued, bringing with it the usual reduction in day-to-day hassle managing configuration files.
scraping/output/-2930323519026450185.txt
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A big indicator of this is that users are no longer required to create a dedicated Kubernetes cluster via GKE on which to run their Pipelines. Instead, Vertex AI employs an apparently serverless approach to running Pipelines written with the Kubeflow Pipelines DSL. Instead, the Kubernetes clusters and the pods running on them are managed behind the scenes by Vertex AI. In the screen shot below, which shows the Vertex Pipelines UI, you start to get a sense for this approach. Instead of a store of pipelines and historic runs, as you may be familiar with if you’ve used the Kubeflow Pipelines UI before, we simply have a list of historic runs. Runs can be started by uploading a Job Spec compiled from a pipeline script, either via the UI or the Python Client. Here we start to get a feel for the β€˜pipelines-as-a-service’ approach that Vertex Pipelines seems to be aiming for. ‍ ‍ Vertex Pipelines UI ‍ ‍
scraping/output/-2930323519026450185.txt
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‍ ‍ Vertex Pipelines UI ‍ ‍ This also hints at another key conceptual difference between the two tools; Vertex AI isn’t running an instance of a Kubeflow Client. Instead, Vertex Pipelines is its own version of the kind of infrastructure usually provided by Kubeflow Pipelines (ie, Container Workflow Orchestration), that can run pipelines specified using the Kubeflow SDK. A key benefit of this new approach is that Vertex Pipelines makes great use of GCS for Artifact storage natively, and even employs its own metadata server in the form of Vertex AI Metadata. Having these managed services in place by default is definitely welcome, as in our experience, the default options in AI Platform Pipelines (Kubernetes nodes and PVCs hosting MySQL and MinIO services) don’t scale quite as well as their Google managed counterparts.
scraping/output/-2930323519026450185.txt
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Another benefit that users will welcome with the new approach is the reduction in cost that is provided by the pay-as-you-go model that this β€˜pipelines-as-a- service’ approach is able to deliver. Instead of paying for the continuous uptime of the necessary K8s Cluster, users will now only pay $0.03USD per run, plus whatever Compute resources the pipeline consumes while it is running. ‍ #### Kubeflow in Vertex Pipelines ‍ Given this new approach to implementing Kubeflow Pipelines in Vertex AI, there are some differences to note when developing workflows with the KFP SDK. The first is that Vertex AI requires an entirely new version of the Kubeflow SDK, version 2.0. This SDK comes bundled with versions of Kubeflow Pipelines after v1.6, so with this version installed you are ready to start building SDK v2.0 compliant pipelines.
scraping/output/-2930323519026450185.txt
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This new version of the SDK is designed primarily to make use of the Pipeline Metadata and Artifact tracking tools of ML Metadata, an open source Metadata tracking tool developed by the Tensorflow Extended team. Vertex AI implements its own version of this in Vertex ML Metadata, which makes use of the base TFX ML Metadata tool. Whilst developing with the new version of the SDK will largely be the same as the traditional Kubeflow SDK, there are a few differences that one will need to keep in mind when working with the new standard.
scraping/output/-2930323519026450185.txt
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First, concerning building components, KFP SDK v2.0 mandates that all component parameters be annotated with their data type. In addition, an extra distinction is now made between Component inputs that are parameters, and those that are artifacts. Component Parameters are those that can be passed as string, integer, float, Boolean, dictionary or list types and are therefore usually smaller pieces of data. Artefacts are larger pieces of data, for example datasets or models, and are passed instead as a path referencing the location of the Artifacts. Parameter values and artifact metadata can be viewed in ML Metadata. The difference between Artifacts and Parameters is really specified within the component specification in the component.yaml files of our components. Below we can see a basic component.yaml file as it may have looked with the old version of the SDK. Below that, we have the component.yaml as it would look under the new specification. ‍
scraping/output/-2930323519026450185.txt
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‍ Old style component specification component.yaml file ‍ New style component specification component.yaml file ‍ ‍ ‍ Inspecting these component specifications carefully, one will notice that for input values in the β€˜command’ portion of the β€˜implementation’, we previously would have used `{inputValue: variable_name}` for Artifacts and Parameters. In the new version, we specify Artifacts with `{inputPath: variable_name}` and Parameters with `{inputValue: variable_name}`.
scraping/output/-2930323519026450185.txt
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When building Pipelines, the new SDK version brings a couple of changes. The first is that, as with components, pipeline parameter definitions must be annotated with their data types. Second, pipelines must be decorated with the `@kfp.dsl.pipeline` decorator. Within the Pipeline decorator we can specify the pipeline name (The ID used for querying ML Metadata for information about your run), description (which is optional), and pipeline_root, which specifies the location in which to store pipeline outputs. The β€˜pipeline_root’ parameter is optional in Kubeflow Pipelines as it will use MinIO Artifact Storage if a root is not defined. However, given that Vertex Pipelines will use GCS for Artifact storage, it requires that β€˜pipeline_root’ be specified (either within the Pipeline decorator, or when calling the create_run_from_job_spec method of the Python Client). ‍ #### Kubeflow SDK v2.0 Limitations ‍
scraping/output/-2930323519026450185.txt
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‍ #### Kubeflow SDK v2.0 Limitations ‍ In addition to these SDK v2.0 considerations that users must keep in mind when developing Kubeflow Pipelines for Vertex Pipelines, there are some additional constraints given the practicalities of Vertex Pipelines’ implementation. The first is caching of pipeline component executions. In Kubeflow Pipelines we could specify that a cache of a component execution would expire after a given amount of time; before which, components running with identical configurations would use the cached output of previous executions. In Vertex Pipelines, we can’t specify the time frame after which caches will expire, but we can use the β€˜enable_caching’ parameter of the create_run_from_job_spec method of the client to enable/disable the use of caches in Vertex Pipeline executions.
scraping/output/-2930323519026450185.txt
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In addition to caching, recursively called components is another feature of Kubeflow Pipelines that Vertex Pipelines does not currently support. The Google documentation on this does use the same language of β€˜Currently, Vertex Pipelines does not support..’, which would indicate that this is something they are potentially looking to support in the future. Another key difference between Kubeflow Pipelines and Vertex Pipelines is the push to use more Google Managed resources such as GCS within your pipelines. For example, in Vertex Pipelines, users can access GCS directly as though it were a mounted volume of storage using Cloud Storage FUSE. By contrast, previously in Kubeflow Pipelines, users interacted with Kubernetes resources such as Persistent Volume Claims (PVCs). Another indicator of this is the host of Google Cloud specific predefined components that have been released to support interaction of pipelines and Google Cloud/ Vertex AI resources. ‍ ‍ ### Conclusion ‍
scraping/output/-2930323519026450185.txt
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‍ ‍ ### Conclusion ‍ In summary, Vertex AI Pipelines introduces some nice changes over the previous AI Platform Pipelines implementation that will overall make the experience of developing and running MLOps workflows on GCP a lot easier. The move to make the underlying resources more managed than in the previous solution is a welcome one, simultaneously speeding up and simplifying the process of getting up and running with Pipelines in GCP. It is worth noting that this product is still in a kind of preview phase, however the key tools are already there to start using this product and it certainly is a promising improvement on what came before. For those still unsure of whether to use AI Platform or jump straight in with Vertex Pipelines, I would recommend you give the new kid on the block a chance. ‍ ## Related posts View all No results found. There are no results with this criteria. Try changing your search. Large Language Model Large Language Model Foundation Models
scraping/output/-2930323519026450185.txt
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‍ ## Related posts View all No results found. There are no results with this criteria. Try changing your search. Large Language Model Large Language Model Foundation Models Foundation Models Corporate Corporate People People Structured Data Structured Data Chat GPT Chat GPT Sustainability Sustainability Voice & Sound Voice & Sound Front-End Development Front-End Development Data Protection & Security Data Protection & Security Responsible/ Ethical AI Responsible/ Ethical AI Infrastructure Infrastructure Hardware & sensors Hardware & sensors MLOps MLOps Generative AI Generative AI Natural language processing Natural language processing Computer vision Computer vision Accelerating businesses with AI technology & experts Contact: [email protected] +32 9 265 95 50 Contact us Join our newsletter By subscribing you agree to with our Privacy Policy Thank you! Your submission has been received! Oops! Something went wrong while submitting the form.
scraping/output/-2930323519026450185.txt
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scraping/output/-2930323519026450185.txt
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Natural language processing πŸš€ Unleash the Power of Large Language Models and Foundation Models - Read our insights and expertise on these trends in AI! Discover more Services Navigate intelligence Activate intelligence Build intelligence AI Solutions Custom solutions Solutions catalogue Domains of expertise References Client cases Customer testimonials Resources Resource library Blog Video & demo Press Events Open Source Our DNA How we work Mission & ambition Responsible AI Partners ML6 For Good Careers Jobs Team Working at ML6 Taal NLEN Contact us en defrNL Contact us Blogposts All Posts October 5, 2022 # Hybrid Machine Learning: Marrying NLP and RegEx Contributors Matthias Feys Q / CTO Subscribe to newsletter Sign up By clicking Sign Up you're confirming that you agree with our Terms and Conditions. Thank you! Your submission has been received! Oops! Something went wrong while submitting the form. Share this post ### Introduction
scraping/output/2077654063284246761.txt
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Thank you! Your submission has been received! Oops! Something went wrong while submitting the form. Share this post ### Introduction When designing real-world NLP applications, you are often confronted with limited (labeled) data, latency requirements, cost restrictions, etc. that hinder unlocking the full potential of your solution. A hybrid setup where you leverage domain knowledge to improve accuracy, efficiency, reliability and/or interpretability would be perfect. But figuring out how to design such a hybrid solution is far from evident. Let’s browse through some common hybrid NLP design patterns and look at example situations of when you should opt for which pattern. #### (1) RULES VS ML Pure ML-based design pattern Pure rule-based design pattern ‍ The first pattern we’ll consider is the adversarial case: you either choose a pure rule-based or a pure ML-based solution. Let’s consider some examples where these design patterns make a lot of sense: ‍
scraping/output/2077654063284246761.txt
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Let’s consider some examples where these design patterns make a lot of sense: ‍ Named Entity Recognition (NER): the choice for or against an ML-based approach essentially boils down to how contextual the entities are. For example, dates can be structured in a specific way (e.g, β€œDD/MM/YYYY”). If an entity follows this format, it is a date and otherwise it isn’t. Thus, it is a very β€œnon-contextual entity”: i.e., a concrete fixed pattern determines whether or not it is a date, independent of the context. It is straightforward to extract these kinds of entities purely via simple rules. A simple RegEx rule can easily recognize both dates ‍
scraping/output/2077654063284246761.txt
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A simple RegEx rule can easily recognize both dates ‍ However, say you only want to extract dates of birth and not other kinds of dates. Now, we are dealing with a very β€œcontextual entity”: dates of birth and other kinds of dates look exactly the same; without any context, you wouldn’t be able to distinguish between the two. It is very difficult to extract these entities in a rule-based way so a pure ML-based approach is the most appropriate. ‍ A contextual language model predicts that only the first date is a date of birth ‍ ‍ Text classification: in text classification use cases, the underlying features that determine which class a text belongs to are often very latent. As rule- based systems don’t tend to perform well in these scenarios, a pure ML-based design pattern is usually the way to go. The same goes for complex tasks such as keyword extraction, text summarization, etc. ‍ ‍ Some tasks are just too complex for rule-based approaches to have a meaningful impact ‍
scraping/output/2077654063284246761.txt
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‍ ‍ Some tasks are just too complex for rule-based approaches to have a meaningful impact ‍ ‍ #### (2) RULES AFTER/BEFORE ML ‍ Rule-based pre-processing design pattern Rule-based post-processing design pattern ‍ The next pattern we’ll look into has a sequential nature: the business rules either act as a first filter or as a post-processing step for the ML model. Let’s take another look at some examples: ‍ ‍High-pass filter: say you want to extract dates of birth and no other dates, so you might opt for a pure ML approach (see above). However, only a fraction of your data actually contains dates, so running inference on every single instance seems like a bit of a waste. We know that every date of birth is also a date and that dates follow a fixed pattern. Thus, we can first check whether a text contains a date via a simple business rule and then only run inference in the cases that it does. ‍
scraping/output/2077654063284246761.txt
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‍ With one simple rule, we only do inference on 2 passages instead of 7 with no impact on performance ‍ With a few simple rules, you can often drastically reduce the amount of processing power you use with a minimal to non-existent impact on performance. ‍ (Semantic) search: in a very similar fashion to what’s outlined above, you can reduce the amount of data you process to perform a semantic search by first filtering out those results for which you are (almost) certain that they are not going to be relevant (e.g., have a (near) zero TF-IDF score). This kind of setup is referred to as a β€œretrieve and re-rank” architecture. Depending on the data, a double-digit percentage decrease in latency is often attainable with a negligible impact on search performance. ‍
scraping/output/2077654063284246761.txt
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Depending on the data, a double-digit percentage decrease in latency is often attainable with a negligible impact on search performance. ‍ Entity linking: let’s say we want to extract product names along with sales prices and link the two entities together (i.e., figure out which sales price belongs to which product name). We know our data and we make the simple assumption that a sales price belongs to the closest product name. This is a rule-based post-processing (β€œlinking”) step that happens after the ML-based extraction of sales prices and product names. ‍ A simple rule will link the ML-extracted entities together ‍ #### (3) RULES AND ML ‍ ‍ Ensemble design pattern ‍ ‍ This pattern looks to combine the outputs of rules and ML as an ensemble. Again, let’s see some examples: ‍ ‍ More determinism: not all mistakes are equal. Perhaps there are some patterns that you know to be correct and want your solution to get correct every single time.
scraping/output/2077654063284246761.txt
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‍ ‍ More determinism: not all mistakes are equal. Perhaps there are some patterns that you know to be correct and want your solution to get correct every single time. In this scenario, you can have a restrictive rule-based system that ensures that these critical situations are covered and in parallel a more generalizable ML-based system that aims to capture the other (complex) cases. For example, you can have a curated gazetteer of names that you know to be clean and correct. These names will always be recognized. The (uncommon) names that fall outside this list will be captured by the ML model. A gazetteer can capture common names and an NER model picks up the more niche ones ‍ ‍ Optimization for recall/precision: since you are essentially combining multiple predictions, you can optimize for recall or precision by choice of the β€œvoting scheme” (i.e., how you go from multiple individual predictions to one final prediction). ‍ ‍ #### (4) ML-INFORMED RULES
scraping/output/2077654063284246761.txt
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‍ ‍ #### (4) ML-INFORMED RULES ML-informed rules design pattern ‍ ‍ A more niche situation could be that your use case really requires a rule- based system β€” be it for regulatory reasons (e.g., GDPR’s β€œRight to explanation”) or for other reasons β€” but that these rules are very difficult to determine. In this scenario, you could use machine learning to generate optimal (RegEx) rules. There are actually multiple ways to achieve this β€” ranging from natural language-to-RegEx Seq2Seq models like SemRegex to models that are trained on labeled data like the evolutionary RegexGenerator algorithm and models like TransRegex that use both natural language and labeled examples. ‍ #### (5) RULE-INFORMED ML Rule-informed ML design pattern ‍ This pattern also looks to combine rules and ML but it does so by finding an appropriate representation of RegEx results and truly integrating the domain knowledge into the model architecture. ‍
scraping/output/2077654063284246761.txt
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‍ This pattern also looks to combine rules and ML but it does so by finding an appropriate representation of RegEx results and truly integrating the domain knowledge into the model architecture. ‍ Theoretically, this is a very clean solution but in practice, we don’t see (widespread) adoption of such architectures. Or at least not yet. If you want to get some intuition as to what this would look like, check out this paper. But at the time of writing, we wouldn’t recommend such a design pattern. ‍ ‍ ### Conclusion ‍ In conclusion, hybrid NLP has the potential to drastically improve the accuracy, efficiency, reliability and/or interpretability of your solution, especially in low labeled data settings. That is, if you do it right. Choosing the right setup is inherently very data- and problem-specific but hopefully the examples above have given you some intuition into which approach to take. ‍ ‍ ## Related posts View all No results found.
scraping/output/2077654063284246761.txt
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scraping/output/2077654063284246761.txt
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scraping/output/2077654063284246761.txt
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scraping/output/7518757031055141065.txt
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Thank you! Your submission has been received! Oops! Something went wrong while submitting the form. Share this post In 2020, we have trained and open-sourced the first Dutch GPT2 model, in various sizes. Of course we wanted to share this with the world by open- sourcing the models, the code and a nice application that showcases its use. But this nice application comes at a cost, literally… ### As-is: HuggingFace model powering a Python app Currently, a HF model is hosted inside a Python Flask app, which uses the pipeline API from the HF library. A routing microservice routes to the correct model serving microservice depending on the user request if he wants to address the 117M parameter GPT2-small model or the 345M parameter GPT2-medium model. PS: if you’re curious how we trained this Dutch GPT2 model: we outlined it perfectly (if we say so ourselves) in this blogpost. If you want to get freaky with these Dutch models yourself, you can find them on our HF Hub page.
scraping/output/7518757031055141065.txt
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The final user-facing application looks as follows: Try it for yourself at https://gpt2.ml6.eu/nl The current setup has some difficulties though: The responses take some time to generate, especially with the medium-size model, reducing the user experience. Second, the container is quite big because of the large models, so we either have to: * autoscale it to zero to keep the cost down, but then have a large startup time from a cold start * let it run continuously, burning cash So, in this blogpost we’re going to improve this model serving component by quantizing it to make it run smoother, hopefully without losing too much expressive quality. ### Quantization to reduce the footprint We’re not going to go into detail on what quantization is. If you wanna get a great primer on this: we wrote a blogpost on this and other model efficiency aspects here.
scraping/output/7518757031055141065.txt
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We’re not going to go into detail on what quantization is. If you wanna get a great primer on this: we wrote a blogpost on this and other model efficiency aspects here. TDLR: by reducing the precision of the weights in the Linear and Embedding layers from fp32 to int8 through a mapping action, the memory footprint of a model is greatly reduced! source: https://rasa.com/blog/compressing-bert-for-faster-prediction-2/ Quantization is quite an active field, so a number of libraries offer options to quantize your model: * PyTorch, though only for quantizing the Linear layers * HuggingFace Optimum: an up and coming solution leveraging Intel NC and ONNX Runtime * ONNX Runtime (ORT) itself Even though we’re huge fans of where Optimum is heading, in this post, we used the last solution, because of the great support for GPT2 quantization through examples and dedicated helpers. If you’re just here for the code goodies, you can find all of the code for this blogpost link !
scraping/output/7518757031055141065.txt
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If you’re just here for the code goodies, you can find all of the code for this blogpost link ! Quantization using ORT only involves three simple steps: #### 1\. Convert the PyTorch model to an ONNX model All the upcoming transformations happen through the ONNXRuntime (ORT) library, so it’s only logical that these steps will require an ONNX binary. This can easily be done using HF + ORT: #### 2\. Optimize the model Model optimization involves a few operations to make the model graph more streamlined. One such example is fusing sequential operations into a single step. #### 3\. Quantize the model This is where the actual quantization happens, or in other words: the mapping of the FP32 weights values to the INT8 value range. #### Run it using ORT To actually use the model artifact (ONNX binary file), we of course need a runtime to host it. What better runtime for ONNX than ONNXRuntime
scraping/output/7518757031055141065.txt
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#### Run it using ORT To actually use the model artifact (ONNX binary file), we of course need a runtime to host it. What better runtime for ONNX than ONNXRuntime To do this, you can easily create an ORT session, which can be fed with the typical inputs otherwise required in a HF model (token id’s, attention masks, etc.) to produce the output logits: Easy-peasy right? Well, there are a few aspects around ORT sessions to make it work well: * IO-binding to avoid data copy * Post-processing the logits to enable top_k and top_p sampling, beam search, temperature, etc. Instead of plain greedy decoding * Including past inputs to improve the performance * EOS special tag detection and processing We won’t go into detail on all of the code needed for each of these aspects, but you can find them all in the notebook (link again) where they are implemented. ### Evaluation
scraping/output/7518757031055141065.txt
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We won’t go into detail on all of the code needed for each of these aspects, but you can find them all in the notebook (link again) where they are implemented. ### Evaluation So we coded up all these extra aspect to get nice predictions, and our model is running happily on a Cloud Run instance, inside a Python app that hosts the ORT session. Happy days! But is it any good… ? #### Generation quality Of course, we want to make sure our models don’t produce garbage, so we will look at the generation quality from a couple of angles: The difference in output logits A first quick check we can do is comparing the output logits of the language modelling heads of the two models. If the quantized model is indeed a credible stand-in for the normal model, then the output logits should roughly follow the same value distribution point-by-point. So by measuring the average, median and max difference in logit values, we can get a first idea on the quality of the potential output:
scraping/output/7518757031055141065.txt
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So by measuring the average, median and max difference in logit values, we can get a first idea on the quality of the potential output: We can see that the logit values can differ quite a bit. We can also see that the impact is less for the 345M parameter GPT2-medium than for the 117M GPT2-small model. Though this is a first indication that we might lose some quality, it doesn’t speak for the true expressive capabilities of the quantized models. So let’s continue: The perplexity Lucky for us, a nice metric to measure the generation quality in a more meaningful fashion exists: perplexity! The ever-lovely peeps at HuggingFace wrote a very nice page about it, what it does, and how to code it up (you can find our implementation in our notebook). We followed their approach, and measured the perplexity on the first 1000 documents of the Dutch Partition of the OSCAR corpus. This is a wide collection of various crawled Dutch webpages.
scraping/output/7518757031055141065.txt
[ 0.018422041088342667, -0.05265058949589729, -0.06246810778975487, 0.024772178381681442, 0.057627249509096146, -0.01484629511833191, 0.06466400623321533, 0.04145282506942749, 0.08364500105381012, -0.022968726232647896, 0.04160688444972038, 0.029453082010149956, 0.05587177723646164, -0.06931480765342712, -0.028256487101316452, 0.051360372453927994, 0.04364285618066788, -0.032282449305057526, -0.071159727871418, -0.04504061117768288, 0.04534041881561279, -0.01079615205526352, -0.05556861683726311, 0.06581403315067291, 0.046547405421733856, 0.026633115485310555, -0.03970213979482651, 0.029121600091457367, 0.008906870149075985, -0.07575827091932297, -0.04498235136270523, -0.013028468936681747, 0.05645223706960678, -0.045129917562007904, 0.036111995577812195, 0.0037402729503810406, -0.09418825805187225, -0.011882152408361435, 0.08066994696855545, -0.07844247668981552, -0.0415283739566803, 0.01266955491155386, 0.030538303777575493, 0.04095479100942612, 0.053705260157585144, 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We followed their approach, and measured the perplexity on the first 1000 documents of the Dutch Partition of the OSCAR corpus. This is a wide collection of various crawled Dutch webpages. Interestingly, the perplexity increase is less high for the medium GPT2 model compared to the small GPT2 model. Meaning the GPT2-medium model seems to suffer less degradation from the quantization process. In line with what we observed from the logit comparison! The human evaluation The kicker, the champ, the true test of generative quality! Here are some example generations by the non-quantized and quantized model side by side, where we ask each model to produce the next 20 tokens. Both models generate through sampling, with top_p=0.95, top_k=50 and temperature=0.95 Comparison in expressive quality ‍ From the look of it, both seem to do very okay! Well enough for the online demo, where only a few next tokens are predicted each time. But is it any fast… ? ### Latency
scraping/output/7518757031055141065.txt
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‍ From the look of it, both seem to do very okay! Well enough for the online demo, where only a few next tokens are predicted each time. But is it any fast… ? ### Latency Now that we know the quantized models are usable, we can start to measure the first annoyance with the as-is deployment: the startup time and request latency. Here we want to measure two items: the startup time when the service experiences a cold start When a serverless Cloud Run instance, that is scaled to 0, starts receiving requests, it needs to perform what is called a β€œcold start” by deploying and running your container application to an available machine instance, fetch the models from Cloud Storage, and load them in to start serving requests. This of course takes a bit of time. Let’s compare this β€œwarmup time” between a service serving the non-quantized versions and the quantized versions: the request latency
scraping/output/7518757031055141065.txt
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Let’s compare this β€œwarmup time” between a service serving the non-quantized versions and the quantized versions: the request latency To measure the response timing for each deployed model, we send a barrage of a few hundred sequential requests to the deployed microservice. Meaning this latency involves network latency, service overhead and model prediction time. We repeat this a number of times, each for a string of varying sequence length, because self-attention computational complexity scaled quadratically with the sequence length ! ‍ ‍ Again a solid performance from the quantized models! The latency seems to be reduced by a factor of 3–4. But is it any cheap… ? ### Cost Since cloud storage is basically free, we mainly look at the costs of hosting and running the model in a microservice on Google Cloud Run. We can easily use the Cloud Run pricing documentation to get a price estimate:
scraping/output/7518757031055141065.txt
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We can easily use the Cloud Run pricing documentation to get a price estimate: * The quantized gpt2-small + gpt2-medium model image fits on a 2GB, 1vCPU machine, totaling to πŸ’²57.02 * The non-quantized gpt2-small + gpt2-medium model image fits on a 8GB, 2vCPU (because you can’t have a 1vCPU machine for that amount of memory), totaling to πŸ’²134.78 Meaning we can reduce our cloud bill for the serving part by a factor of 2.4! And even if the cost of the reworked deployed would be too large, we have clearly shown that the smaller quantized container has a much lower warm-up time, making autoscale-to-zero a valid option. ### So long! Leveraging quantization and ORT clearly results in a nice speedup and cost reduction!
scraping/output/7518757031055141065.txt
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### So long! Leveraging quantization and ORT clearly results in a nice speedup and cost reduction! Enjoy all the money you just saved! And stay tuned for upcoming blogposts where we leverage Triton Inference Server for full transformer hosting enlightenment, since this is a more recommended approach for mature model serving deployment than the presented Flask option. ‍ ## Related posts View all No results found. There are no results with this criteria. Try changing your search. Large Language Model Large Language Model Foundation Models Foundation Models Corporate Corporate People People Structured Data Structured Data Chat GPT Chat GPT Sustainability Sustainability Voice & Sound Voice & Sound Front-End Development Front-End Development Data Protection & Security Data Protection & Security Responsible/ Ethical AI Responsible/ Ethical AI Infrastructure Infrastructure Hardware & sensors Hardware & sensors MLOps MLOps Generative AI Generative AI
scraping/output/7518757031055141065.txt
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scraping/output/7518757031055141065.txt
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scraping/output/-1004585763649522392.txt
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scraping/output/-1004585763649522392.txt
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scraping/output/-1004585763649522392.txt
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