About This Course
Machine Learning Operations (MLOps) is the discipline that brings software engineering best practices to machine learning — enabling organizations to deploy, monitor, and continuously improve ML models in production reliably and at scale. While data scientists can build excellent models, without MLOps those models often sit in notebooks, never delivering business value.
The gap between building an ML model and running it successfully in production is enormous. MLOps bridges this gap by applying DevOps principles — version control, CI/CD, automated testing, monitoring, and infrastructure as code — to the unique challenges of ML systems: data versioning, model drift, feature pipelines, and experiment tracking.
MLOps engineers are among the most in-demand professionals in the AI/ML space. As organizations mature in their AI adoption, they consistently discover that they need MLOps expertise to scale beyond their first few models. This creates exceptional career opportunities for professionals who understand both machine learning and engineering fundamentals.
This MLOps course provides hands-on training in the full ML production lifecycle: from experiment tracking and model versioning through pipeline automation, containerized deployment, model monitoring, and automated retraining. You'll work with the industry-standard MLOps stack used by leading tech companies.
Course Syllabus – 10 Modules (35–40 hours)
Our structured curriculum is designed to take you from foundational concepts to advanced, practical application. Each module builds on the previous one, ensuring comprehensive understanding and skill development.
MLOps Foundations & the ML Lifecycle
Why MLOps? The ML project lifecycle: data collection, feature engineering, training, evaluation, deployment, monitoring, retraining. MLOps maturity levels (Level 0 to Level 3). MLOps vs DevOps: similarities and unique challenges. Introduction to MLOps tools landscape: MLflow, Kubeflow, BentoML, Seldon.
Python for MLOps & Environment Management
Python best practices for production ML: modular code, type hints, docstrings. Virtual environments vs Conda vs Poetry. Code linting (black, flake8, isort), pre-commit hooks. Git for ML projects: what to version, .gitignore for ML. Jupyter to scripts: refactoring notebooks into production code.
Data Versioning & Feature Engineering Pipelines
Data versioning with DVC (Data Version Control): tracking datasets, data pipelines as code. Feature stores: what they are and why they matter. Building feature engineering pipelines with scikit-learn Pipelines, pandas, and Feature-engine. Great Expectations for data validation and quality checks.
Experiment Tracking with MLflow
MLflow components: Tracking, Projects, Models, Model Registry. Logging metrics, parameters, artifacts, and models in MLflow. Comparing experiment runs, visualizing training curves. MLflow Model Registry: model versioning, staging, production transitions. Remote tracking servers. Integrating MLflow with scikit-learn, XGBoost, PyTorch.
Model Packaging & Serving
ML model serialization: pickle, joblib, ONNX. Building REST APIs for models with FastAPI/Flask: request validation, serialization, error handling. Batch inference pipelines vs real-time serving. BentoML for model serving: service definitions, runners, Bentos. Model serving patterns: A/B testing, canary deployments, shadow mode.
Containerizing ML Workloads with Docker
Docker for ML: writing Dockerfiles for ML applications, managing large model files, multi-stage builds for size optimization. Docker Compose for local development with model server + dependencies. GPU container support (NVIDIA Docker). Docker image optimization and security scanning for production ML.
Kubernetes for ML Deployment
K8s fundamentals for MLOps, deploying ML model APIs on Kubernetes. Kubernetes deployments, services, ingress controllers, resource requests/limits for GPU workloads. Horizontal Pod Autoscaler for scaling inference. Helm charts for ML service deployment. Introduction to Kubeflow Pipelines for workflow orchestration.
CI/CD for Machine Learning
ML-specific CI/CD challenges: model quality gates, data validation in CI. GitHub Actions for ML: automated training pipelines, model evaluation, conditional deployment. Model performance regression tests. DVC pipelines in CI. Automating model retraining on data drift detection. Deployment strategies for models.
Model Monitoring & Drift Detection
Production ML monitoring: data drift, concept drift, prediction drift, model performance degradation. Evidently AI for drift detection reports. Prometheus and Grafana for ML metrics. Setting up monitoring dashboards, alerting on drift thresholds. Human-in-the-loop monitoring, feedback loops, and retraining triggers.
Cloud ML Platforms & Capstone
AWS SageMaker: training jobs, model registry, endpoints. Azure ML: compute clusters, pipelines, model deployment. Google Vertex AI overview. Capstone: build a complete MLOps pipeline — experiment tracking, model registry, containerized API deployment, monitoring dashboard, and automated retraining CI/CD.
Career Opportunities After This Course
Upon completing this course, you'll be equipped for a range of rewarding career paths:
- Job Roles: MLOps Engineer, ML Engineer, Data Scientist (with DevOps skills), Platform Engineer (ML), AI Infrastructure Engineer
- Salary Range: ₹6–12 LPA (entry MLOps) to ₹20–45 LPA (Senior ML Engineer/MLOps Lead)
- Industries: IT, manufacturing, banking, healthcare, consulting, government, and more
- Work Options: Full-time employment, consulting, freelancing, remote work
Tools & Technologies Covered
You'll gain hands-on experience with the industry-standard tools that professionals use every day:
Who Should Take This Course?
- Students and fresh graduates looking to build industry-relevant skills
- Working professionals seeking to upskill or change career direction
- Entrepreneurs and business owners wanting to leverage technology
- IT professionals expanding their skill portfolio
- Anyone with a genuine interest in this field and commitment to learning
Training Methodology
Our training is 100% practical and project-based. Each module includes concept explanation, live demonstrations, hands-on exercises, mini-projects, and doubt-clearing sessions. Sessions are available on weekdays (2 hrs/day) and weekends (4 hrs/day), with recordings available for 3 months.
Frequently Asked Questions
Do I need prior experience?
No prior experience is required for beginner-level courses. We start from the absolute basics and build progressively. Students with existing knowledge will benefit from the advanced modules.
What are the batch timings?
We offer weekday batches (Mon–Fri, 2 hours/day) and weekend batches (Sat–Sun, 4 hours/day). Online and hybrid options are available. Contact us for the current batch schedule.
Will I receive a certificate?
Yes, upon successful completion of all modules and the final project assessment, you'll receive an industry-recognized certificate from Optimetrik Digital.
Is placement support available?
Yes, we provide resume building, mock interviews, LinkedIn optimization, and job referrals for top-performing students through our hiring partner network.
Are classes online or offline?
Both options available. Live online sessions via video conferencing and in-person at our Coimbatore center. All sessions are recorded and accessible for 3 months.
