+91 96452 47165

Courses > MLOps for Developers & Data Teams – Zoom

MLOps for Developers & Data Teams – Zoom

Students

Last Updated: Sep 02, 2025

About Course

This 2-week intensive MLOps program is designed for developers, data scientists, and ML engineers who want to move machine learning models from experimentation to scalable, reliable production systems. The course takes a hands-on, project-driven approach using modern tools and workflows such as MLflow, DVC, Docker, Kubernetes, CI/CD pipelines, and cloud-native platforms like AWS Sagemaker and HuggingFace Spaces.

Bridge the gap between ML model development and production deployment
Learn to build robust ML pipelines and automate deployments
Gain hands-on experience with CI/CD, Docker, and Kubernetes for ML
Monitor live models and detect data/model drift in real-time
Learn industry-standard practices in security, governance, and compliance
Build a full MLOps project with infrastructure diagram and deployment

Course Content

Day 1

Introduction to MLOps

00:20:00
ML Project Lifecycle

00:20:00
Hands-on: Set Up a Git-Driven ML Project Repository

00:20:00

+91 96452 47165

About Course

What Will You Learn?

Course Content

Day 1

Introduction to MLOps

ML Project Lifecycle

Hands-on: Set Up a Git-Driven ML Project Repository

Day 2

Experiment Tracking & Reproducibility

Versioning Code, Data & Models

Hands-on: Track and Compare Experiments with MLflow + DVC

Day 3

Model Training Pipelines

Pipeline Orchestration

Hands-on: Build a Training Pipeline with Prefect or Airflow

Day 4

Model Deployment Strategies

Serving Models with REST APIs

Hands-on: Deploy a Model as a REST API using FastAPI

Day 5

CI/CD for Machine Learning

ML Deployment with Docker & Kubernetes

Hands-on: Build a CI/CD Pipeline for ML with GitHub Actions + Docker

Day 6

Model Monitoring & Drift Detection

Tools for Monitoring

Hands-on: Set Up Drift Detection & Monitoring Dashboards

Day 7

Model Registry & Lifecycle Management

Best Practices in Production Model Governance

Hands-on: Register and Serve a Model with MLflow Registry

Day 8

Cloud-Native MLOps Platforms

Deploying Models to HuggingFace or Streamlit Cloud

Hands-on: Deploy an ML App on HuggingFace Spaces or Streamlit Cloud

Day 9

Security, Compliance & Ethics in MLOps

Audit Logs and Access Controls

Hands-on: Add Role-Based Access to MLflow Deployment

Day 10

Capstone Day: Design & Deploy a Full MLOps Project

Final Presentation + Infra Diagram

More Courses

Performance Marketing for eCommerce

KC Jagadeep

HuggingFace for Developers

KC Jagadeep

Other Courses In This Category:

This Course Includes:

Material Includes

Requirements

Share

Audience

Quick Links

Explore

Newsletter