MLOps: Machine Learning Operations
Operationalize AI: Streamline and Scale Machine Learning Workflows with MLOps
Early access to e-LMS included
About This Course
MLOps is the practice of integrating machine learning workflows into production environments efficiently. This program covers best practices for deploying, monitoring, and maintaining machine learning models in real-world applications. Participants will explore tools for continuous integration, versioning, and model management, focusing on scalability and automation.
Aim
To equip PhD scholars, data scientists, and AI professionals with advanced knowledge of MLOps practices, integrating machine learning models into production environments. This course focuses on scaling, automating, and managing ML workflows, ensuring continuous deployment and monitoring.
Program Objectives
- Understand MLOps principles for deploying and managing machine learning models.
- Set up CI/CD pipelines for ML workflows.
- Implement model monitoring and retraining strategies.
- Gain hands-on experience with deployment automation tools.
- Explore tools for scaling ML pipelines in production environments.
Program Structure
Module 1: Introduction to MLOps
- What is MLOps?
- Definition and importance
- Comparison with DevOps and DataOps
- Benefits and challenges
- The MLOps Lifecycle
- Model development
- Deployment
- Monitoring and maintenance
- MLOps Tools Overview
- CI/CD for Machine Learning
- Tools like MLflow, Kubeflow, and TensorFlow Extended (TFX)
Module 2: Fundamentals of Machine Learning
- Supervised, Unsupervised, and Reinforcement Learning
- Feature Engineering
- Data preprocessing
- Feature scaling and selection
- Model Training and Validation
- Train-test split
- Cross-validation
- Hyperparameter tuning
Module 3: DevOps Essentials for MLOps
- Version Control Systems
- Git and GitHub/GitLab
- CI/CD Pipelines
- Continuous integration for ML
- Continuous deployment for ML
- Infrastructure as Code (IaC)
- Tools like Terraform and Ansible
- Docker and Containerization
- Building and managing ML container images
- Kubernetes for Orchestration
- Deploying ML models on Kubernetes
Module 4: Data Engineering in MLOps
- Data Pipelines
- Building reproducible data pipelines
- Tools like Apache Airflow and Prefect
- Data Storage and Versioning
- Using DVC (Data Version Control)
- Managing data lakes and warehouses
- Real-Time Data Processing
- Kafka and Spark Streaming
- Data Security and Governance
- Ensuring data privacy and compliance
Module 5: Model Deployment
- Deployment Strategies
- Batch, online, and hybrid inference
- A/B testing and canary deployments
- Serving Models in Production
- Tools like TensorFlow Serving, FastAPI, and Flask
- Scaling and Load Balancing
- Using Kubernetes and auto-scaling
- Edge Deployment
- Deploying models on IoT and edge devices
Module 6: Monitoring and Maintenance
- Model Monitoring
- Drift detection (data and model drift)
- Performance metrics tracking
- Logging and Alerting
- Using tools like Prometheus, Grafana, and ELK Stack
- Automated Retraining
- Implementing feedback loops for continuous improvement
- Incident Response
- Debugging and rollback strategies
Module 7: MLOps Tools in Practice
- Introduction to Key Tools
- MLflow: Experiment tracking and model registry
- Kubeflow: End-to-end pipeline orchestration
- TFX: TensorFlow Extended ecosystem
- Hands-On Labs
- Setting up and using MLOps pipelines with these tools
Module 8: Ethics and Governance in MLOps
- Responsible AI Practices
- Fairness, accountability, and transparency
- Regulatory Compliance
- GDPR, CCPA, and other regulations
- Auditability and Explainability
- Building explainable and auditable ML systems
Module 9: Advanced Topics in MLOps
- Automated Machine Learning (AutoML)
- Tools and frameworks like H2O.ai, Google AutoML
- Federated Learning
- Concepts and applications
- Serverless MLOps
- Serverless architectures for ML workflows
- MLOps in the Cloud
- AWS, Azure, and GCP MLOps offerings
Module 10: Capstone Project
- End-to-End Implementation
- Building a complete MLOps pipeline
- Integration of CI/CD, data pipelines, and monitoring
- Evaluation and Presentation
- Peer review and expert feedback
Who Should Enrol?
Data scientists, machine learning engineers, AI researchers, DevOps professionals focusing on operationalizing machine learning workflows.
Program Outcomes
- Proficiency in building and managing scalable ML workflows.
- Hands-on experience with CI/CD pipelines for machine learning models.
- Skills in deploying and monitoring models in production.
- Ability to automate ML retraining and model versioning processes.
Fee Structure
Discounted: ₹8,499 | $112
We accept 20+ global currencies. View list →
What You’ll Gain
- Full access to e-LMS
- Real-world dry lab projects
- 1:1 project guidance
- Publication opportunity
- Self-assessment & final exam
- e-Certificate & e-Marksheet
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