I apologize for the incomplete response. It looks like I accidentally submitted the answer prematurely. Here is the complete code with MLflow integration:
```python
import pandas as pd
import numpy as np
import keras
from keras.models import Model
from keras.layers import Input, Dense, Embedding, Conv2D, MaxPooling2D, concatenate, Flatten, Dropout
from sklearn.model_selection import train_test_split
import mlflow
from mlflow.keras import log_model

# Load data
anime = pd.read_csv("anime.csv")
ratings = pd.read_csv("rating_complete.csv")

# Filter out users with less than 10 ratings and anime with less than 100 ratings
user_counts = ratings['user_id'].value_counts()
rating_counts = ratings['anime_id'].value_counts()
ratings = ratings[ratings['user_id'].isin(user_counts[user_counts >= 10].index)]
ratings = ratings[ratings['anime_id'].isin(rating_counts[rating_counts >= 100].index)]

# Merge anime and rating data
data = pd.merge(anime, ratings, on='anime_id')

# Filter out unwanted columns
data = data[['user_id', 'anime_id', 'rating', 'genre', 'type', 'episodes', 'members']]

# Split data into train and test sets
train_data, test_data = train_test_split(data, test_size=0.2, random_state=42)

# Get number of users and items
num_users = len(data['user_id'].unique())
num_anime = len(data['anime_id'].unique())

# Preprocess text data
MAX_WORDS = 10000
MAX_SEQUENCE_LENGTH = 100
tokenizer = keras.preprocessing.text.Tokenizer(num_words=MAX_WORDS, lower=True, split=' ')
tokenizer.fit_on_texts(data['genre'])
train_text = tokenizer.texts_to_sequences(train_data['genre'])
test_text = tokenizer.texts_to_sequences(test_data['genre'])
train_text = keras.preprocessing.sequence.pad_sequences(train_text, maxlen=MAX_SEQUENCE_LENGTH)
test_text = keras.preprocessing.sequence.pad_sequences(test_text, maxlen=MAX_SEQUENCE_LENGTH)

# Preprocess image data
def preprocess_image(filename):
    img = keras.preprocessing.image.load_img(filename, target_size=(64, 64))
    img_array = keras.preprocessing.image.img_to_array(img)
    return img_array / 255.

train_image = np.array([preprocess_image(f"images/{id}.jpg") for id in train_data['anime_id']])
test_image = np.array([preprocess_image(f"images/{id}.jpg") for id in test_data['anime_id']])

# Preprocess numerical data
train_numerical = train_data[['members', 'episodes']].values
test_numerical = test_data[['members', 'episodes']].values

# Preprocess categorical data
train_categorical = pd.get_dummies(train_data['type'])
test_categorical = pd.get_dummies(test_data['type'])

# Define model architecture
user_input = Input(shape=(1,), name='user_input')
anime_input = Input(shape=(1,), name='anime_input')
text_input = Input(shape=(MAX_SEQUENCE_LENGTH,), name='text_input')
embedded_text = Embedding(input_dim=MAX_WORDS, output_dim=100, input_length=MAX_SEQUENCE_LENGTH)(text_input)
text_flattened = Flatten()(embedded_text)
image_input = Input(shape=(64, 64, 3), name='image_input')
conv1 = Conv2D(32, (3, 3), activation='relu')(image_input)
pool1 = MaxPooling2D((2, 2))(conv1)
conv2 = Conv2D(64, (3, 3), activation='relu')(pool1)
pool2 = MaxPooling2D((2,
```
불완전한 답변에 대해 사과드립니다. 실수로 답변을 너무 일찍 제출한 것 같습니다. 다음은 MLflow가 통합된 전체 코드입니다:
'''python
팬더를 PD로 임포트
numpy를 np로 가져 오기
import keras
keras.models에서 Model을 가져옵니다.
keras.layers에서 입력, 밀도, 임베딩, 컨브2D, 맥스풀링2D, 컨케트네이트, 플랫텐, 드롭아웃을 가져옵니다.
sklearn.model_selection에서 train_test_split 가져오기
mlflow 가져오기
mlflow.keras에서 log_model 가져오기

# 데이터 로드
anime = pd.read_csv("anime.csv")
ratings = pd.read_csv("rating_complete.csv")

# 평점 10점 미만의 사용자와 평점 100점 미만의 애니메이션을 필터링합니다.
user_counts = ratings['user_id'].value_counts()
rating_counts = ratings['anime_id'].value_counts()
ratings = ratings[ratings['user_id'].isin(user_counts[user_counts >= 10].index)]
ratings = ratings[ratings['anime_id'].isin(rating_counts[rating_counts >= 100].index)]

# 애니메이션과 평점 데이터 병합
data = pd.merge(anime, ratings, on='anime_id')

# 원하지 않는 열 필터링
data = data[['user_id', 'anime_id', 'rating', 'genre', 'type', 'episodes', 'members']]]

# 데이터를 훈련 세트와 테스트 세트로 분할
train_data, test_data = train_test_split(data, test_size=0.2, random_state=42)

# 사용자 수와 아이템 수 가져오기
num_users = len(data['user_id'].unique())
num_anime = len(data['anime_id'].unique())

# 텍스트 데이터 전처리
max_words = 10000
max_sequence_length = 100
tokenizer = keras.preprocessing.text.Tokenizer(num_words=MAX_WORDS, lower=True, split=' ')
tokenizer.fit_on_texts(data['genre'])
train_text = tokenizer.texts_to_sequences(train_data['genre'])
test_text = tokenizer.texts_to_sequences(test_data['genre'])
train_text = keras.preprocessing.sequence.pad_sequences(train_text, maxlen=MAX_SEQUENCE_LENGTH)
test_text = keras.preprocessing.sequence.pad_sequences(test_text, maxlen=MAX_SEQUENCE_LENGTH)

# 이미지 데이터 사전 처리
def preprocess_image(filename):
    img = keras.preprocessing.image.load_img(filename, target_size=(64, 64))
    img_array = keras.preprocessing.image.img_to_array(img)
    반환 img_array / 255.

train_image = np.array([preprocess_image(f"images/{id}.jpg") for id in train_data['anime_id']])
test_image = np.array([preprocess_image(f"images/{id}.jpg") for id in test_data['anime_id']])

# 숫자 데이터 전처리
train_numerical = train_data[['members', 'episodes']].values
test_numerical = test_data[['members', 'episodes']].values

# 범주형 데이터 전처리
train_categorical = pd.get_dummies(train_data['type'])
test_categorical = pd.get_dummies(test_data['type'])

# 모델 아키텍처 정의
user_input = Input(모양=(1,), 이름='user_input')
anime_input = Input(shape=(1,), name='anime_input')
text_input = 입력(모양=(MAX_SEQUENCE_LENGTH,), 이름='text_input')
embedded_text = 임베딩(입력_dim=MAX_WORDS, 출력_dim=100, 입력_길이=MAX_SEQUENCE_LENGTH)(text_input)
text_flattened = Flatten()(embedded_text)
image_input = Input(shape=(64, 64, 3), name='image_input')
conv1 = Conv2D(32, (3, 3), 활성화='relu')(image_input)
pool1 = MaxPooling2D((2, 2))(conv1)
conv2 = Conv2D(64, (3, 3), activation='relu')(pool1)
pool2 = MaxPooling2D((2,
```