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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 🔬

Does microsoft safety scanner remove malware