The MLOps Course is designed to provide participants with the essential skills needed to automate and operationalize machine learning (ML) models into production. The course covers the end-to-end machine learning lifecycle, from model development to deployment, monitoring, and continuous integration and delivery (CI/CD) in machine learning workflows.

High-Level Training Flow – MLOps Course

1. Requirement Gathering & Training Need Analysis (TNA)
   Assess participants' current knowledge of machine learning, DevOps practices, and the challenges of deploying ML models into production. Identify areas where participants need more understanding and ensure the course content aligns with their experience levels.

2. Curriculum Finalization & Agenda Approval
   Finalize course modules, session schedules, and learning outcomes. Core topics will include:

   • Introduction to MLOps and its importance

   • Overview of the ML lifecycle

   • Tools and frameworks for MLOps (e.g., TensorFlow, Kubernetes, Docker)

   • Data versioning and management in ML workflows

   • CI/CD pipelines for machine learning

   • Model deployment (on-prem, cloud, containers)

   • Model monitoring, feedback loops, and model drift management

3. Environment Setup
   Set up the necessary tools and infrastructure for participants to work on ML projects. This includes:

   • Installing ML frameworks (e.g., TensorFlow, PyTorch)

   • Setting up version control tools (e.g., Git, DVC for data versioning)

   • Installing Docker and Kubernetes for containerization and orchestration

   • Setting up cloud environments or local environments (e.g., AWS, GCP, Azure)

4. Content Preparation
   Develop slides, demos, hands-on exercises, and real-world examples covering:

   • Introduction to MLOps principles

   • Automating data pipelines using tools like Apache Airflow

   • Building and training machine learning models

   • Implementing CI/CD for ML workflows

   • Deploying ML models as APIs (using Flask, FastAPI, etc.)

   • Model versioning and experimentation tracking (using tools like MLflow, DVC)

5. Delivery of Training (Live Sessions / Workshops)
   Conduct live sessions combining theory and practical labs. Topics will include:

   • Setting up a basic ML model training pipeline

   • Automating model retraining and deployment using CI/CD tools

   • Using Docker to containerize ML models

   • Deploying models in a cloud environment or Kubernetes

   • Implementing monitoring and logging to track model performance

   • Building feedback loops for model improvement

6. Daily Recap + Assignments + Lab Reviews
   Summarize key takeaways at the end of each session, review completed exercises, and answer participant questions. This ensures that participants fully understand the MLOps pipeline and can apply what they’ve learned in their projects.

7. Assessment / Quiz / Project Submission
   Evaluate participants through quizzes, hands-on exercises, and a final project. The project will typically involve creating an end-to-end MLOps pipeline: building a model, automating the pipeline, deploying it, and setting up monitoring and retraining mechanisms.

8. Feedback Collection
   Gather feedback on course content, pacing, and the practical application of MLOps concepts. This feedback will be used to improve the course content and delivery for future sessions.

9. Post-Training Support (Q&A, Slack/Telegram group)
   Provide ongoing support via Q&A sessions, Slack/Telegram groups, or email for troubleshooting issues, assisting with the implementation of MLOps practices in real-world projects, and answering advanced questions on deploying and maintaining ML models in production.

10. Training Report Submission to Corporate Client
    Document attendance, assessment results, project completion, and participant feedback. The final training report will summarize the training outcomes and provide an overview of participants’ readiness to apply MLOps practices in production environments.