MLOps: Machine Learning Operations Course – 3 Weeks

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

This program is designed to equip PhD scholars, data scientists, and AI professionals with advanced knowledge of MLOps practices, enabling the integration of machine learning models into production environments. The course emphasizes scaling, automating, and managing machine learning (ML) workflows, ensuring seamless deployment, monitoring, and continuous improvement of models in production.

Program Objectives

Understand key MLOps principles: for the deployment and management of machine learning models.
CI/CD for ML: Set up and implement CI/CD pipelines tailored for ML workflows.
Model Monitoring: Learn strategies for monitoring and automatic retraining.
Scalable Deployment: Gain hands-on experience with automation tools for scalable ML deployment.
Advanced ML Pipelines: Explore tools and methodologies for scaling ML pipelines in production environments.

Program Structure

Module 1: Introduction to MLOps

Overview of MLOps: Bridging Data Science and Operations
Key Differences Between MLOps, DevOps, and DataOps
Benefits of MLOps: Scalability, Automation, and Model Management
MLOps Lifecycle: From Development to Production

Module 2: CI/CD Pipelines for Machine Learning

Building Continuous Integration (CI) Pipelines for ML
Automating ML Model Testing and Validation
Implementing Continuous Deployment (CD) for AI Models
Tools for CI/CD: Jenkins, GitLab CI, and GitHub Actions

Module 3: Model Monitoring and Maintenance

Monitoring Model Performance in Production Environments
Detecting Model Drift: Data and Concept Drift
Implementing Automated Retraining Pipelines
Logging, Metrics, and Alerting for ML Model Health

Module 4: Scaling ML Pipelines

Automating Data Pipelines for Large-Scale ML Operations
Distributed Computing for ML Workloads: Hadoop, Spark, and Dask
Managing Infrastructure with Docker, Kubernetes, and Terraform
Case Study: Scaling ML Workflows in Production

Module 5: Deployment Tools for ML Models

Deploying ML Models with Docker and Kubernetes
Cloud Deployment Solutions: AWS SageMaker, Google AI Platform, and Azure ML
Model Serving Frameworks: TensorFlow Serving, FastAPI, and Flask
Real-Time vs. Batch Inference: When to Use What?

Module 6: Model Versioning and Experiment Tracking

Best Practices for Versioning ML Models
Tools for Experiment Tracking: MLflow, Weights & Biases
Managing Model Repositories and Rollbacks
Ensuring Consistency Across Development and Production

Module 7: Security and Compliance in MLOps

Securing ML Pipelines and Protecting Sensitive Data
Managing Compliance in AI/ML Workflows (GDPR, HIPAA, etc.)
Model Audits: Ensuring Model Fairness and Reducing Bias
Case Studies on Security Breaches and ML Vulnerabilities

Module 8: Final Project

Design and Implement a Full MLOps Pipeline
Automate Data Preprocessing, Model Training, and Deployment
Focus on Real-World Scenarios (e.g., Drift Detection, Retraining, and Model Scaling)
Present a Comprehensive Solution and Documentation

Participant’s Eligibility

Data scientists, machine learning engineers, AI researchers, and DevOps professionals aiming to operationalize machine learning workflows and scale AI in production environments.

Program Outcomes

Master Scalable ML Workflows: Design and automate ML pipelines for scalable workflows.
CI/CD Pipelines: Proficiency in setting up CI/CD pipelines for continuous integration and deployment.
Model Monitoring: Monitor, retrain, and manage models in production environments.
Scaling and Automation: Use modern tools to scale, automate, and secure ML operations.

Program Deliverables

e-LMS Access: All study materials and lectures available online.
Real-Time Project: Hands-on project on building MLOps pipelines.
Project Guidance: Receive mentoring from industry experts.
Certification: Upon successful completion of exams and assignments.

Future Career Prospects

MLOps Engineer
AI Infrastructure Architect
Machine Learning Engineer
DevOps Specialist for AI Workflows
Data Engineer
Cloud AI Engineer

Job Opportunities

AI-driven organizations integrating machine learning models in production.
Cloud computing providers offering ML solutions.
Startups focused on building scalable AI solutions.
Data science teams needing operational ML model management.

MODE	Virtual (Google Meet)
TYPE	Self Paced
LEVEL	Moderate
DURATION	3 Weeks

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MLOps: Machine Learning Operations Course – 3 Weeks

Aim

Program Objectives