Why is 𝗠𝗼𝗱𝗲𝗹 𝗥𝗲𝗴𝗶𝘀𝘁𝗿𝘆 so important in your 𝗠𝗟𝗢𝗽𝘀 𝗦𝘁𝗮𝗰𝗸? Let's look again into the Machine Learning Model Training Lifecycle. 𝗟𝗲𝘁’𝘀 𝗿𝗲𝘃𝗶𝗲𝘄 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝘀𝘁𝗲𝗽𝘀: 𝟭: Version Control: Machine Learning Training Pipeline is defined in code, once merged to the main branch it is built and triggered. 𝟮: Feature Preprocessing: Features are retrieved from the Feature Store, validated and passed to the next stage. Any feature related metadata that is tightly coupled to the Model being trained is saved to the Experiment Tracking System. 𝟯: Model is trained and validated on Preprocessed Data, Model related metadata is saved to the Experiment Tracking System. 𝟰: If Model Validation passes all checks - Model Artifact is passed to a Model Registry. The model is stored and ready to be packaged. ✅ 𝗧𝗵𝗶𝘀 𝗶𝘀 𝗶𝘁 - regardless of what deployment type will follow, the model is stored in The Model Registry. Model Registry is what glues Training and Deployment Pipelines together and this is where handover of the Model Artifact happens.  𝟱: The same model can be packaged as a container for different deployment types by implementing a respective interface. E.g. 👉 Flink application for Stream Processing Deployment. 👉 gRPC API for Request-Response. 👉 Plain model pointing to the Batch Serving Feature Store API for Batch. ✅ 𝗧𝗵𝗶𝘀 𝗶𝘀 𝗴𝗼𝗼𝗱 𝗻𝗲𝘄𝘀 - as long as you are training your models using historical data the training pipeline is the same for any type of deployment. ❗️ It’s a different story when it comes to online training but more on it in the future episodes so 𝘀𝘁𝗮𝘆 𝘁𝘂𝗻𝗲𝗱 𝗶𝗻! You can find a more detailed explanation of different Model Deployment procedures in one of my previous Newsletter releases. Leave any thoughts in the comment section 👇 -------- Follow me to upskill in #MLOps, #MachineLearning, #DataEngineering, #DataScience and overall #Data space. Also hit 🔔to stay notified about new content. 𝗗𝗼𝗻’𝘁 𝗳𝗼𝗿𝗴𝗲𝘁 𝘁𝗼 𝗹𝗶𝗸𝗲 💙, 𝘀𝗵𝗮𝗿𝗲 𝗮𝗻𝗱 𝗰𝗼𝗺𝗺𝗲𝗻𝘁! Join a growing community of Data Professionals by subscribing to my 𝗡𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿.