Freelance Corporate Trainer – MLOps role, focus on both technical expertise and training delivery skills.

Key Skills Required

- Machine Learning Lifecycle Management

- Model Training, Validation, and Deployment

- CI/CD for ML Pipelines

- Docker & Kubernetes

- Git, GitHub, GitLab

- Jenkins, Azure DevOps, GitHub Actions

- MLflow, Kubeflow, Airflow

- Data Versioning (DVC)

- Feature Stores

- Monitoring & Observability

- AWS, Azure, or GCP MLOps Services

- Python, SQL, Linux

Topics to Cover in Corporate Training

Module 1: Introduction to MLOps

- What is MLOps?

- MLOps vs DevOps vs DataOps

- MLOps Architecture

- ML Lifecycle

Module 2: Data Management

- Data Collection and Ingestion

- Data Validation

- Data Versioning with DVC

- Feature Engineering Pipelines

Module 3: Model Development

- Experiment Tracking

- Hyperparameter Tuning

- Model Registry

- MLflow Integration

Module 4: CI/CD for Machine Learning

- Git Workflows

- Automated Testing for ML Models

- Jenkins/GitHub Actions Pipelines

- Deployment Automation





Module 5: Containerization & Orchestration

- Docker Fundamentals

- Building ML Containers

- Kubernetes Basics

- Deploying ML Applications on Kubernetes

Module 6: Model Deployment

- Batch Inference

- Real-Time Inference

- REST APIs using FastAPI

- Blue-Green & Canary Deployments

Module 7: Cloud MLOps

- AWS SageMaker

- Azure Machine Learning

- Google Vertex AI

- Infrastructure as Code

Module 8: Monitoring & Governance

- Model Drift Detection

- Data Drift Monitoring

- Logging and Alerting

- Model Governance and Compliance

Hands-On Labs

1. Build an End-to-End ML Pipeline.

2. Track Experiments using MLflow.

3. Version Data using DVC.

4. Containerize ML Models with Docker.

5. Deploy Models on Kubernetes.

6. Create CI/CD Pipelines using GitHub Actions.

7. Deploy to AWS SageMaker or Azure ML.

8. Implement Model Monitoring.

📌 Trainer MLops (Telangana)
🏢 Galactix Solutions
📍 Telangana