I have had basic experiences in Machine Learning before in my classes at school. However, I have never dealt with a large amount of log data before.In my PaaS company, we have customers who buy our product. After the purchase, they go through an implementation phase where they set up our product to uniquely fit their needs (can take many months). Once this is done, they enter the production phase where their employees can start logging on and using our product.For each customer, I current have timestamped data from the logs for each day, and firmographic information such as #employees, company size, etc for each customer. The log data in particular contains information for each day for each customer such as memory used, tasks performed etc.Goal: To predict when the date a new customer will go into production phase. This will allow us to allocate our servers and hardware more efficiently if we know the date they will go into production phase.I currently have labels for all our customers of the dates they went into production phase. I am not sure how I should deal with features, and what algorithms to use when I have a bunch of logs for each customer. Thank you!
Machine Learning from log files and firmographics
machine learning;bigdata