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