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
- Build an End-to-End ML Pipeline.
- Track Experiments using MLflow.
- Version Data using DVC.
- Containerize ML Models with Docker.
- Deploy Models on Kubernetes.
- Create CI/CD Pipelines using GitHub Actions.
- Deploy to AWS SageMaker or Azure ML.
- Implement Model Monitoring.

📌 Trainer MLops (Telangana)
🏢 Galactix Solutions
📍 Telangana