Many data scientists don't know how to push ML models to production. Here's the recipe 👇 𝗞𝗲𝘆 𝗜𝗻𝗴𝗿𝗲𝗱𝗶𝗲𝗻𝘁𝘀 🔹 𝗧𝗿𝗮𝗶𝗻 / 𝗧𝗲𝘀𝘁 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 - Ensure Test is representative of Online data 🔹 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 - Generate features in real-time 🔹 𝗠𝗼𝗱𝗲𝗹 𝗢𝗯𝗷𝗲𝗰𝘁 - Trained SkLearn or Tensorflow Model 🔹 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗖𝗼𝗱𝗲 𝗥𝗲𝗽𝗼 - Save model project code to Github 🔹 𝗔𝗣𝗜 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 - Use FastAPI or Flask to build a model API 🔹 𝗗𝗼𝗰𝗸𝗲𝗿 - Containerize the ML model API 🔹 𝗥𝗲𝗺𝗼𝘁𝗲 𝗦𝗲𝗿𝘃𝗲𝗿 - Choose a cloud service; e.g. AWS sagemaker 🔹 𝗨𝗻𝗶𝘁 𝗧𝗲𝘀𝘁𝘀 - Test inputs & outputs of functions and APIs 🔹 𝗠𝗼𝗱𝗲𝗹 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 - Evidently AI, a simple, open-source for ML monitoring 𝗣𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗲 𝗦𝘁𝗲𝗽 𝟭 - 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 Don't push a model with 90% accuracy on train set. Do it based on the test set - if and only if, the test set is representative of the online data. Use SkLearn pipeline to chain a series of model preprocessing functions like null handling. 𝗦𝘁𝗲𝗽 𝟮 - 𝗠𝗼𝗱𝗲𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 Train your model with frameworks like Sklearn or Tensorflow. Push the model code including preprocessing, training and validation scripts to Github for reproducibility. 𝗦𝘁𝗲𝗽 𝟯 - 𝗔𝗣𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 & 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Your model needs a "/predict" endpoint, which receives a JSON object in the request input and generates a JSON object with the model score in the response output. You can use frameworks like FastAPI or Flask. Containzerize this API so that it's agnostic to server environment 𝗦𝘁𝗲𝗽 𝟰 - 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 & 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 Write tests to validate inputs & outputs of API functions to prevent errors. Push the code to remote services like AWS Sagemaker. 𝗦𝘁𝗲𝗽 𝟱 - 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 Set up monitoring tools like Evidently AI, or use a built-in one within AWS Sagemaker. I use such tools to track performance metrics and data drifts on online data. Data Science Resources 👇👇 https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D Like if you need similar content 😄👍