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MLOps Bootcamp

By Shubhankit In Grow Data Skills

Self Paced
English
Completion Certificate

1 Year

Validity (From the date of Enrollment)

42 Hours

Duration

20

Sessions

2

Projects

INR 3500

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This course includes

Content Duration42 Hours
Total Video Session20
Total Industry Project2
Tools & Technology – DVC, MLflow, Docker, Kubernetes, Prometheus, Grafana, Jenkins, ArgoCD
Certificate of completion

Tech stack you'll learn

Course Content

✅ ML in Practice

Understanding the ML Lifecycle
Dataset exploration, preprocessing, training, evaluation, and tuning
Problems in the Traditional ML Lifecycle
Reproducibility
Scalability
Monitoring
End-to-End Implementation in Jupyter
Load dataset
Feature engineering
Model training & evaluation
Hyperparameter tuning
Transitioning to MLOps
Challenges in productionizing ML models
Overview of automation, deployment, and monitoring

✅ MLOps Fundamentals

What is MLOps?
Definition & Need for MLOps
MLOps vs DevOps vs DataOps
Key MLOps Concepts
Scalability
Automation
Monitoring
Reproducibility
Overview of the MLOps Ecosystem
Tools & Frameworks
Docker, Kubernetes, MLflow, CI/CD, Model Monitoring

✅ Data and Model Versioning

Introduction to DVC for data versioning
Data Versioning with DVC
Setting up DVC
Versioning datasets for ML workflows
Introduction to MLflow for Model Versioning
Logging parameters, metrics, and artifacts

✅ Experiment Tracking with MLflow

Setting Up an Experiment Tracking System
Hands-on: Logging Model Metrics
Exploring the MLflow Model Registry
Managing multiple model versions
Model comparison & best practices

✅ Containerization with Docker

Docker Introduction
What is Docker?
Why use Docker for ML models?
Docker Architecture & Components
Containers
Images
Dockerfiles
Hands-on: Building & Running Docker Containers
Writing a Dockerfile
Creating a containerized ML model

✅ Orchestrating ML Workflows

Docker Compose for Multi-Container Applications
Hands-on: Running an ML Pipeline with Docker Compose
Pushing Images to Docker Hub

✅ Kubernetes Essentials

Introduction to Kubernetes
Why Kubernetes?
Kubernetes vs Docker Swarm
Components of Kubernetes
Pods
Deployments
Services
Namespaces
Setting Up Kubernetes (Minikube/Kind)
Deploying a Containerized ML Model

✅ Advanced Kubernetes

Scaling Deployments and Managing Resources
Horizontal & Vertical Scaling
Rolling Updates & Canary Deployments
Monitoring Kubernetes Workloads

✅ Model Monitoring

Importance of monitoring ML models in production
Tools for Monitoring
Prometheus
Grafana
Hands-on: Setting up monitoring dashboards

✅ Handling Model Drift

Understanding model drift
Retraining Pipelines
Automating Retraining Workflows

✅ CI/CD Pipelines

Basics of CI/CD in MLOps
CI/CD Tools
Jenkins
GitHub Actions
ArgoCD
Hands-on: Automating Model Deployments with Jenkins

✅ Cloud Deployments

Overview of Cloud Platforms
Azure
AWS
GCP
Hands-on: Deploying ML Model on Azure

✅ Project 1: End-to-End MLOps Pipeline

Building a Training Pipeline
Dataset Exploration & Preprocessing
Training Pipeline Design
Versioning with DVC & MLflow
Deployment and Monitoring
Deploying the trained model using Docker and Kubernetes
Setting up monitoring with Prometheus and Grafana

✅ Project 2: Real-Time Inference API

API Development
Building a real-time inference API with FastAPI
Containerizing and testing the API locally
Deploying the API with CI/CD
Automating API deployment with Jenkins or ArgoCD
Live demo: Real-time inference with a production-ready API

Attention Seeking Resume Preparation and Interview Strategies
Strategies To Crack Tech Interviews
Linkedin Profile Making
How To Expand Your Professional Network On Linkedin
How To Use Various Job Portals
How To Approach For Referrals

Course Schedule

Mode of the Course:

Recorded

Course Duration:

42 Hours

Total Sessions:

Total Projects:

Validity:

1 Year (Starting From The Date Of Enrollment)

Programming Language Used:

Python

Prerequisite:

Python

⚠️ Important Notice :

The video may not work on Linux due to DRM restrictions. It is only accessible on Chrome when using Windows or macOS.

Workaround: To access the video on Linux, you can create a Windows virtual machine (VM) and watch the video through the VM. Alternatively, you can use our Android or iOS application to view the video on your mobile device.

Instructor

Shubhankit Sirvaiya

Shubhankit Sirvaiya is a Data Scientist At World Wide Technology | Ex- Accenture Growth & Strategy , mentored over 700+ students around Data Science Domain.