A Complete MLops Journey

  • IntermediateLevel

  • 1010+Students Enrolled

  • 2 Hrs Duration

  • 4.6Average Rating

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About this Course

  • Master the complete MLOps Journey from ML experiment tracking to scalable deployment. Learn mlops fundamentals like data versioning, CI/CD, containerization, and drift monitoring.
  • Gain hands-on MLOps experience by building Level 0 and Level 1 architectures. Automate pipelines using GitHub Actions and deploy models on AWS or GCP for practical learning.
  • Learn industry-grade MLOps fundamentals such as setting up feature stores, real-time dashboards, and alerting systems to detect data or model drifts efficiently.
  • Earn the best MLOps certification free while developing end-to-end MLOps skills, testing, deployment, and performance optimization for production-scale system.

Learning Outcomes

End-to-End MLOps

Design ML pipelines from data ingestion to deployment and monitoring

CI/CD Automation

Automate testing and release of models via Git actions and containers

Drift Monitoring

Detect data and model drift with dashboards and alerting loops

Who Should Enroll

  • Data scientists ready to productionize notebooks into version-controlled, continuously deployed ML services.
  • ML engineers tasked with automating pipelines, containerizing models, and setting up cloud monitoring for critical apps.
  • Backend developers entering ML who need a clear roadmap to add CI/CD, Docker, and MLOps workflows to existing projects.

Course Curriculum

Explore a comprehensive curriculum covering Python, machine learning models, deep learning techniques, and AI applications.

tools

  1. 1. Course Introduction

  2. 2. The Challenges in ML Workflow

  3. 3. General Challenges

  4. 4. Blueprint of Level 0 Architecture

  5. 5. Hands-on with Level 0 Architecture Part - I

  6. 6. Hands-on with Level 0 Architecture Part - II

  1. 1. Real-time Prediction and Batch Time Prediction

  2. 2. Model Deployment in Streamlit

  3. 3. Understanding Data Drift and Concept Drift

  4. 4. Drawbacks of Level 0 Architecture

  1. 1. Introduction to Cloud Platform

  2. 2. ML Framework

  3. 3. Blueprint of Level 1 Architecture Part I

  4. 4. Blueprint of Level 1 Architecture Part II

  5. 5. Best Practices for MLOps Mastery!

Meet the instructor

Our instructor and mentors carry years of experience in data industry

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Apoorv Vishnoi

Training Head, Analytics Vidhya

Apoorv is a seasoned AI professional with over 14 years of experience. He has founded companies, worked at start-ups and mentored start-ups at incubation cells.

Get this Course Now

With this course you’ll get

  • 2 Hours

    Duration

  • Apoorv Vishnoi

    Instructor

  • Intermediate

    Level

Certificate of completion

Earn a professional certificate upon course completion

  • Industry-Recognized Credential
  • Career Advancement Credential
  • Shareable Achievement
certificate

Frequently Asked Questions

Looking for answers to other questions?

MLOps combines Machine Learning and DevOps practices to streamline model development, testing, and deployment. It ensures reproducibility, automation, and scalability, making ML systems more reliable, efficient, and production-ready.

Basic Python knowledge helps, but the course explains MLOps concepts step-by-step. It blends theory and practice so even beginners can grasp pipelines, automation, and deployment workflows easily.

CI/CD automates testing, validation, and deployment of ML models. It ensures code and data consistency, reduces errors, and speeds up model updates, making MLOps pipelines scalable and production-ready for real-world applications.

Data versioning tracks changes in datasets across experiments, ensuring reproducibility and traceability. In MLOps fundamentals, tools like DVC or Git help maintain structured records of data used in model training.

MLOps uses monitoring dashboards and alerting systems to detect data or concept drift. When models deviate in accuracy or performance, retraining workflows are triggered automatically, keeping systems consistent and reliable.

You’ll build real-world projects covering CI/CD automation, model tracking, containerization, and cloud deployment. Each lab is designed to reinforce practical MLOps fundamentals through guided, hands-on experience.

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