ML Ops on AWS – Enabling Faster Time to Market

Key Highlights

  • MLOps – Benefits and Challenges: MLOps (Machine Learning Operations) is a practice that integrates DevOps practices for Machine Learning projects. It offers benefits like improved productivity for data scientists, repeatability, reliability, and auditability of models. However, challenges include integrating different teams and building data models.
  • CI/CD in MLOps: CI/CD (Continuous Integration and Continuous Delivery) is an important aspect of MLOps. It involves versioning of source data, source code, and ML models. It also involves testing and monitoring of models during training and deployment phases.
  • AWS MLOps Framework: The whitepaper discusses AWS MLOps Framework as a solution to facilitate integrating ML with DevOps. It offers a three-layered architecture with services for different skillsets. It helps automate training/retraining of models, pipeline orchestration, and model monitoring.
  • Indium – A Partner for AWS MLOps Implementation: Indium is an AWS partner that offers consulting services for implementing MLOps on AWS. They have expertise in integrating development and data science teams for successful MLOps adoption.