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MLOps and LLMOps BootCamp

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Become an MLOps Engineer in 6-Weeks

A hands-on MLOps and LLMOps bootcamp to build, automate, deploy, and monitor machine learning systems

60 hours of 100% live instructor-led training with hands-on labs
Get job-ready for roles like MLOps Engineer, ML Platform Engineer
Get hands-on 15+ projects and an end-to-end capstone to strengthen your portfolio
Career Support — mock interviews, resume refining, GitHub & LinkedIn profile enhancement

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4.9

Google Rating

16k+

Learners

150+

Cohorts

300+

Enterprises

Course Overview

MLOps Engineer is one of the fastest-growing roles in tech today, with salaries ranging from $130K to $180K globally. Companies across cloud, fintech, and enterprise AI are actively hiring, and the supply of engineers who can bridge ML and production infrastructure is still far behind demand.

Most companies are sitting on ML models that never make it to production. Building a model is the easy part — keeping it reliable, scalable, and monitored in production is where most teams fail. Engineers who can own the full production stack are becoming essential. This bootcamp teaches you how to build and operate production-ready ML systems.

Program Highlights

Build end-to-end ML pipelines from data ingestion to production deployment

Automate model training, testing, and deployment using CI/CD with GitHub Actions

Track and reproduce ML experiments using MLflow, DVC, and DagsHub

Containerize and orchestrate ML applications using Docker and Kubernetes

Deploy production-ready ML models on GCP using Vertex AI

Monitor model performance, detect data drift, and set up alerts using Grafana and Prometheus

Orchestrate automated ML workflows and retraining pipelines using Apache Airflow

Build, deploy, and monitor production-ready LLM applications using LLMOps best practices

Apply real-world use cases — fraud detection and product recommendation — as capstone projects

Productize ML systems and prepare for enterprise-grade MLOps roles

11+ Tools Covered

Get hands-on with industry-leading tools trusted by AI & ML professionals worldwide.

MLOps Job Statistics

Cloud & SaaS Companies - 35%
Enterprise AI & Digital Transformation - 25%
FinTech, Banking & Risk Analytics - 15%
E-commerce, Retail & Recommendation Systems - 15%
Healthcare, Manufacturing & Others - 10%

$4.5B → $17B+

Global MLOps market growth expected by 2030

55%+

ML models fail to reach production due to lack of MLOps

2× Faster

AI teams with MLOps deploy models vs teams without it

$150K – $220K

Average global salary for MLOps Engineers

MLOps and LLMOps BootCamp Course Content

Download Syllabus

Module 1 Python Programming Foundations

Subtopics:

Variables & Data Types
Operators
Conditional Statements (if-else)
Loops (for, while)
Functions
Lists, Tuples, Dictionaries

Learning Outcomes:

Write structured Python programs and develop logical thinking for ML workflows.

Hands-on/Lab:

Mini programs like Bank Account Management System, Inventory Management system

Module 2 Python Libraries for Data Science

Subtopics:

NumPy (arrays, vectorization)
Pandas (DataFrames, data cleaning)
Data Visualization (Matplotlib, Seaborn)

Learning Outcomes:

Manipulate, analyze, and visualize structured datasets efficiently.

Hands-on/Lab:

Perform EDA on realworld dataset and create visual dashboards.

Module 3 Statistics for Data Science

Subtopics:

Mean, Median, Mode
Variance & Standard Deviation
Probability Basics
Normal Distribution
Correlation & Covariance

Learning Outcome:

Understand descriptive statistics required for ML model building.

Hands-on/Lab:

Compute statistical measures using Python.

Module 4 Machine Learning (Supervised & Unsupervised)

Subtopics:

Regression Algorithms (Linear Regression, Decision Tree, Random Forest etc.)
Classification Algorithms (Naïve Bayes, Ensemble learning etc.)
Clustering (KMeans)
Model Evaluation Metrics

Learning Outcome:

Build and evaluate ML models using scikitlearn.

Hands-on/Lab:

Train classification and regression models in Python

Module 5 Deployment using VSCode, GitHub & Streamlit Cloud

Subtopics:

VSCode Project Structure
GitHub Repository Setup
Streamlit App Development

Learning Outcome:

Deploy ML models as interactive web applications.

Hands-on/Lab:

Deploy a trained ML model using Streamlit.

Module 6 Code Versioning with Git

Subtopics:

Git Basics
Branching & Merging
GitHub Collaboration

Learning Outcome:

Manage and version ML code efficiently.

Hands-on/Lab:

Create repository, manage branches, resolve merge conflicts.

Module 7 CI/CD with GitHub Actions

Subtopics:

CI/CD Principles
GitHub Actions Workflow YAML
Automated Testing

Learning Outcome:

Automate ML workflows triggered by Git commits.

Hands-on/Lab:

Create CI pipeline to autotest and build ML project.

Module 8 Experiment Tracking with MLflow

Subtopics:

MLflow Tracking
Logging Parameters & Metrics
Model Registry
Google Kubernetes Engine (GKE)

Learning Outcome:

Track and manage ML experiments professionally.

Hands-on/Lab:

Log experiments and compare multiple model runs.

Module 9 Data Versioning with DVC

Subtopics:

DVC Basics
Remote Storage Setup
Reproducible Pipelines

Learning Outcome:

Version datasets and reproduce ML pipelines.

Hands-on/Lab:

Track dataset using DVC and reproduce experiment.

Module 10 Dagshub Integration (Git + DVC + MLflow)

Subtopics:

Centralized ML Collaboration
Integrating Code, Data & Experiments

Learning Outcome:

Collaborate on ML projects with unified version control and tracking.

Hands-on/Lab:

Push integrated ML project to Dagshub.

Module 11 Docker & Containerization

Subtopics:

Docker Architecture
Writing Dockerfiles
Containerizing ML Apps

Learning Outcome:

Create portable ML environments using containers.

Hands-on/Lab:

Containerize ML inference application.

Module 12 Apache Airflow

Subtopics:

DAG Concepts
Scheduling ML Pipelines
Automating Retraining

Learning Outcome:

Orchestrate ML workflows with Airflow.

Hands-on/Lab:

Build automated ML training DAG.

Module 13 Kubernetes

Subtopics:

Kubernetes Basics
Pods & Services
Deploying Containerized ML Apps

Learning Outcome:

Scale ML applications using Kubernetes.

Hands-on/Lab:

Deploy ML container on Kubernetes cluster.

Module 14 Monitoring with Grafana

Subtopics:

Monitoring Concepts
Prometheus Integration
Dashboards & Alerts

Learning Outcome:

Monitor production ML systems effectively.

Hands-on/Lab:

Visualize ML metrics using Grafana dashboards.

Module 15 AWS, Azure, GCP, Vertex for MLOps

Subtopics:

Introduction to AWS, Azure, and Google Cloud for ML workloads
Compute, storage, and managed ML services across cloud platforms
Overview of SageMaker, Azure ML, and Vertex AI
Deploying ML models on cloud infrastructure
⁠Managing scalable inference and ML pipelines in the cloud

Learning Outcome:

⁠Understand how to deploy, manage, and scale machine learning systems on AWS, Azure, and GCP using modern cloud-based MLOps services.

Hands-on/Lab:

Deploy a Dockerized ML model on cloud infrastructure and test cloud-based inference using managed ML services.

Module 16 LLMOps (Large Language Model Operations)

Subtopics:

Introduction to LLMs and Foundation Models
Prompt Engineering Basics
RAG (Retrieval Augmented Generation)
LLM Monitoring & Evaluation
Deploying LLM Applications

Learning Outcome:

Build, deploy, and monitor productionready LLM applications.

Hands-on/Lab:

Build a simple RAG based chatbot and deploy it as an API.