MLOps – Machine Learning Operations Training in Chennai-Edubrights

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MLOps – Machine Learning Operations Course in Chennai

Master the deployment, management, and scaling of machine learning systems with Edubrights’ MLOps – Machine Learning Operations training in Chennai. This course is designed for students, freshers, machine learning engineers, data scientists, DevOps professionals, AI engineers, and working professionals who want to operationalize ML models in production environments.

Gain hands-on experience with MLOps workflows, model deployment, CI/CD pipelines, model monitoring, automation, containerization, cloud integration, and real-world machine learning operations projects through practical industry use cases.

Key Highlights:

✅ Real-Time MLOps Projects & Enterprise AI Deployment Use Cases

✅ Live Instructor-Led Training by AI, MLOps & Cloud Experts

✅ Hands-On Practice with Modern MLOps Tools & Frameworks

✅ End-to-End Machine Learning Lifecycle Management

✅ Model Deployment, Versioning & Reproducibility Best Practices

✅ CI/CD Pipelines for Machine Learning Applications

✅ Model Monitoring, Performance Tracking & Drift Detection

✅ Containerization with Docker & Deployment Automation Techniques

✅ Workflow Orchestration & ML Pipeline Management

✅ Integration with Cloud Platforms & ML Infrastructure Services

✅ Scalable Production-Ready AI & Machine Learning Deployments

✅ Data Management, Governance & Operational Best Practices

✅ Resume Building, Portfolio Development & Mock Interview Preparation

✅ Career Guidance, Placement Assistance & Certification Support

✅ Flexible Online, Classroom & Weekend Training Options

✅ Corporate Training for AI, ML & DevOps Teams

Build practical MLOps expertise, streamline machine learning deployments, and become industry-ready for careers in Machine Learning Engineering, AI Operations, Data Science, and Cloud AI.

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₹30000

₹42000

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Course Objectives

Popular Techniques Covered in This Course

Get Hands-on Knowledge about Real-Time Projects

Curriculum

Module 1: Introduction to MLOps

What is MLOps and why it matters for production ML
MLOps vs DevOps: key differences and similarities
The ML lifecycle: from experimentation to production
MLOps maturity levels and organisational readiness

Module 2: Experiment Tracking and Model Management

Introduction to MLflow: tracking experiments and metrics
Logging parameters, metrics, and artefacts with MLflow
MLflow Model Registry: versioning and stage transitions
Comparing experiments and selecting the best model

Module 3: Data Versioning and Pipeline Management

Data versioning with DVC (Data Version Control)
Building reproducible ML pipelines with DVC stages
Managing datasets, features, and model artefacts
Integrating DVC with Git for full pipeline reproducibility

Module 4: CI/CD for Machine Learning

Applying CI/CD principles to ML workflows
Automated testing for ML: data validation and model tests
Building ML pipelines with GitHub Actions
Continuous training and model retraining pipelines

Module 5: Model Serving and Deployment

Model serving patterns: batch, real-time, and streaming
Deploying models with FastAPI and Docker
ML model serving with MLflow, TorchServe, and TF Serving
Deploying to cloud: AWS SageMaker, Azure ML, and GCP Vertex AI

Module 6: Feature Stores

What is a feature store and why it is critical for MLOps
Introduction to Feast and Tecton feature stores
Offline vs online feature serving
Building and registering features for production ML

Module 7: Model Monitoring and Observability

Types of ML model drift: data, concept, and prediction drift
Monitoring model performance in production
Tools: Evidently AI, WhyLabs, and Arize
Alerting, retraining triggers, and incident response

Module 8: LLMOps and Capstone Project

MLOps for large language models: LLMOps overview
Managing LLM fine-tuning pipelines and model registries
Prompt versioning and evaluation pipelines
Hands-on capstone: end-to-end MLOps pipeline project

Receive Training From Our Skilled and Effective Trainers

Experience in the Industry Gain expertise from MLOps engineers who have designed and operated production ML pipelines, model registries, and monitoring systems for large-scale AI deployments.

Backgrounds at the Top Our MLOps trainers have implemented ML platforms at leading e-commerce, fintech, and cloud-native technology companies using tools like MLflow, Kubeflow, and AWS SageMaker.

Clear & Effective Teaching ML lifecycle management, CI/CD for ML, model monitoring, and drift detection are taught with practical toolchain examples and real pipeline demonstrations.

Hands-On Learning Focus Students build end-to-end MLOps pipelines using MLflow, DVC, GitHub Actions, and cloud ML platforms through comprehensive project-based labs.

Up-to-Date Knowledge Trainers continuously update content with the latest MLOps tooling, LLMOps practices for generative AI, and cloud provider ML platform updates.