MLOps aka Operational AI (Part-3)

  1. Model Training (Data Scientist Domain)
  2. Model Serving (Data Engineer and DevOps Domain)
  1. Feature Engineering (Custom Feature Store with Versioning using Deltalake time travel feature)
  2. Model Training, Evaluation, and Validation
  3. Model Management (Model Registry, Experimentation, Versioning, Artifacts, and Metadata tracking)
  4. Model Explainability.
  5. Model Deployment and Serving (Packaging, Scaling, throughput, and Monitoring)
Diagram1 — MLOps Flow
  1. Storage: Any object or block storage like HDFS, S3, or Azure Blob
  2. Database: Deltalake (Cloud agnostic)
  3. Data Quality: Deequ/ Great Expectation (Cloud agnostic)
  4. Feature Store: Custom Build on Deltalake (Cloud agnostic)/ FEAST
  5. Model Management: MLflow (Cloud agnostic)/ Weights & Biases
  6. Versioning: Git, GitLab
  7. CICD: Jenkins/ Github Action
  8. Model Serving on Kubernetes — Seldon (Recommended)/ KFServing (Cloud agnostic)
  9. Model Monitoring — Seldon Alibi Detect (Cloud agnostic)

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