![]() The following representation provides a simplified view of the end-to-end MLOps process. ![]() □ Train Models: Utilize curated data and features for accurate predictions.ġ️⃣1️⃣ Validate Models: Assess model performance on validation data.ġ️⃣2️⃣ Evaluate Models: Measure performance using appropriate metrics.ġ️⃣3️⃣ Revisit 8️⃣: Refine candidate model selection based on evaluation results.ġ️⃣4️⃣ Select Best Model: Determine the highest-performing model aligned with business objectives.ġ️⃣5️⃣ Package Model: Prepare the model for deployment with the necessary files and dependencies.ġ️⃣6️⃣ Register Model: Maintain a central repository for tracking deployed models.ġ️⃣7️⃣ Containerize Model: Use containerisation for portability and easy deployment.ġ️⃣8️⃣ Deploy Model: Release model in a production environment for consumption.ġ️⃣9️⃣ Serve Model: Expose deployed model through APIs for seamless integration.Ģ️⃣0️⃣ Inference Model: Leverage model for real-time predictions and data-driven decisions.Ģ️⃣1️⃣ Monitor Model: Implement robust monitoring for performance and behaviour tracking.Ģ️⃣2️⃣ Retrain or Retire Model: Regularly evaluate and update or retire the model based on performance. Let's explore them together:ġ️⃣ Ingest Data: Capture raw data from diverse sources for further processing.Ģ️⃣ Validate Data: Check data quality, integrity, and consistency.ģ️⃣ Clean Data: Remove inconsistencies, handle missing values, and address quality issues.Ĥ️⃣ Standardize Data: Transform data into a consistent format for seamless processing.ĥ️⃣ Curate Data: Organize and structure data for effective feature engineering and model development.Ħ️⃣ Extract Features: Derive insights and patterns through feature engineering.ħ️⃣ Select Features: Identify impactful features, discarding irrelevant ones.Ĩ️⃣ Identify Candidate Models: Explore ML models suitable for the task.ĩ️⃣ Write Code: Implement code for model training and evaluation. In MLOps, a successful journey from data to machine learning models involves several crucial steps. ![]() □ Machine Learning Operations (MLOps) - End-to-End Process □ ![]()
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