MLOps Classes In Bangalore

SKU: 12722
15 Lesson
|
30 Hours
Choose MLOps Classes in Bangalore to develop in-demand skills and open new career paths. Master concepts and tools that top employers actively look for in skilled candidates.
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MLOps Training In Bangalore

Overview

Prerequisites

  • Basic knowledge of mathematics, statistics, and programming

Skills You Will Gain

  • Deploy and monitor models in production environments
  • Apply data preprocessing techniques for better model results
  • Work with common frameworks used in the field

Course Curriculum

1. What is MLOps & MLOps Motivation
2. Solutions and Future Trends
3. MLOps Components
4. Different Roles involved in MLOps ( ML Engineering + Operations )
5. Machine Learning Life Cycle
6. MLOps Vs DevOps
7. Major Phases — what it takes to master MLOps
8. Different tools for MLOps
9. MLOps Maturity Model Levels
10. MLOps - Stages Of CI / CD
1. Why Linux? Linux types? How to access Linux env in different system
2. Free tier Amazon EC2 ubuntu instance
3. SSH and SSH tools & Putty
4. File zilla & WinSCP
5. Introduction to Shell , Bash Shell & Basic Linux Commands
6. Help for Command Line
7. Linux Core Concepts & Kernel and types
8. Linux file system, Boot Sequence, Run levels, File Types & Filesystem Hierarchy
9. Package Management Introduction and Configuration
10. Linux Type Based Package Manager
11. RPM and YUM
12. DPKG and APT
13. File Compression and Archival, Searching for Files and Patterns using grep/wildcards etc
14. VI, Nano Editor
15. Security and File Permissions, The Security Incident (story)
16. Linux Accounts, User Management, Access Control Files, Account Management
17. File Permissions and Ownership , Cron jobs
18. Service management with systemd, Working overtime (story)
19. Creating a Systemd Service, Systemd Tools
20. Lab - systemd services
21. Assignment | Assignment Solution 
1. What? Why? When? Type? Vendor? Pricing? Industry wise uses of GIT
2. Creation of Github / Gitlab / bitbucket account
3. Local GitHub UI installation, setup with VSCode and Pycharm
4. Local and Remote Repositories installation and configuration
5. GIT Repository initialisation
6. Commands: git log
7. Git Branches - What is branching in Git and why we need it?
8. Master/main branch and user-defined branch
9. Checkout and pushing to a branch, Merging of branches
10. Project control and management
11. In Remote Repositories, Initialisation of Remote Repositories
12. Pushing code to the remote repositories
13. Cloning of the remote repositories to local
14. PR (Pull Requests), Fetch and Pull
15. Handling conflict on merging branch, Forking of repository
16. Rebasing, Resetting and Reverting, Stashing
17. Assignment | Assignment Solution 
1. What is DVC, DVC Uses, Installation in Mac OS, Windows & Linux
2. Data Versioning, Model Versioning
3. Data Access, Model Access & Data Pipelines, Metrics, Parameters, Plots
4. Run, Queue, Compare, Persisting, and Sharing Experiments
5. Clean up, Versioning Data and Models, Sharing Data and Model Files
6. Data Registries, Shared Development Server & Project Structure
7. Setup Google Drive Remote, Large Dataset Optimisation
8. External Dependencies, Managing External Data
9. Automate Pipelines with DVC, Pipelines & Experiment Automation, Build automated pipelines
10. Experiments Management, Experimenting with reproducible pipelines, Common issues with ML experiments
11. Tracking metrics and plots & Compare experiment results, Build, Test & Deploy
12. Introduction to CI/CD in Machine Learning & Build CI/CD pipeline
13. Install GitLab Runner and Trigger CI/CD pipeline
14. Build Machine Learning pipeline, Build CI/CD pipeline, Trigger CI/CD pipeline
15. Making Continuous Integration work with ML, DVC Integration with Project
16. Build a model Prototype, Build a prototype with Jupyter Notebook
17. Start to version your code with Git, Version your code with Git
18. Create pipelines, Automate pipelines and data versioning with DVC
19. Create CI pipeline to build, test, experiment, Experimenting with DVC and CML & Deploy your model
20. Assignment | Assignment Solution 
1. What is DevOps, Why DevOps
2. Dev-Test-Deploy ,DevOps Principles,DevOps Toolchain
3. Overview of DevOps Tools
4. Co-relation between Agile and DevOps,Categories of DevOps Tools
5. Containers Concepts , Container Vs Virtual Machine
6. Installing docker on CentOS, Debian and Windows
7. Managing Container with Docker Commands
8. Building your own docker images & Docker Compose
9. Docker registry - Docker Hub , Networking inside single docker container
10. Lab - Running Python Web App in docker container
11. Lab - Create a docker image from git repo
12. Lab - Deploying flask app using docker-compose
13. Lab - Complex deployment using docker-compose
14. Lab - Creating your own docker registry 
15. Assignment | Assignment Solution 
1. Introduction to Kubernetes
2. Architecture and Kubernetes cluster installation
3. Raft Consensus Algorithm and Networking in Kubernetes
4. Raft Consensus Algorithm and Networking in Kubernetes
5. Installing Minikube and Objects in Kubernetes - Pod, Deployment
6. Services - Service Discovery, Service Object, Headless Services, Service Type
7. Role based Access
8. Volumes - Persistent Volumes, Persistent Volume Claim, Storage Class
9. Config Map and Secrets 
10. Ingress - Virtual Host, Types, Fanout, Virtual Host, Fanout Ingress configuration,
11. Virtual Host Ingress configuration
12. Lab - Installing Minikube on EC2
13. Lab - Enable and access Dashboard Addon
14. Lab - Deploy flask web app on Minikube
15. Lab - Deploy Nginx app on Minikube
16. Lab - Deploy application with host type volumes 
17. Assignment | Assignment Solution 
1. Introduction to Prometheus
2. Prometheus installation
3. Introduction to Grafana
4. Grafana Installation
5. Integration of Prometheus and Grafana
6. Adding customised dashboard in Grafana
7. Introduction to node exporter
8. Integrating node exporter for monitoring
9. Lab - Scrape metric from Grafana
10. Lab - View Node exporter metric in Grafana
11. Lab - View Docker metric in Grafana
12. Lab - Import AWS EC2 dashboard in Grafana 
13. Assignment | Assignment Solution 
1. Introduction to Jenkins
2. Continuous Integration & Continuous Integration with Jenkins
3. Jenkins Architecture
4. Installing Jenkins on EC2
5. User management
6. Set up Jenkins Master & Slave
7. Setup CI-CD pipeline for sample project
8. Lab - Setup Role based access
9. Lab - Master/Slave Setup
10. Lab - Configure SCM in Jenkins 
11. Assignment | Assignment Solution 
1. What is MLFLow & Installation
2. MLFlow Tracking, Where Runs Are Recorded, How Runs and Arti-facts are Recorded
3. Scenario 1: MLFlow on localhost
4. Scenario 2: MLFlow on localhost with SQLite
5. Scenario 3: MLFlow on localhost with Tracking Server
6. Scenario 4: MLFlow with remote Tracking Server, backend and arti-fact stores
7. Logging Data to Runs, Logging Functions, Launching Multiple Runs in One Program, Performance Tracking with Metrics
8. Visualising Metrics, Automatic Logging
9. Scikit-learn, TensorFlow and Keras, Gluon, XGBoost, Pytorch
10. MLFLow Tracker, Organising Runs in Experiments, Managing Experiments and Runs with the Tracking Service API, Tracking UI
11. Querying Runs Programmatically, MLFlow Tracking Servers, Storage,Networking
12. Logging to a Tracking Server, MLFlow Projects, Specifying Projects, Running Projects, Iterating Quickly, Building Multi Step Workflows
13. MLFLow Models, Storage Format, Model Signature And Input Example
14. Model API, Built-In Model Flavours, Model Customisation, Built-In Deployment Tools, Deployment to Custom Targets
15. Model Registry, Model Registry Workflows, UI Workflow, Registering a Model, Using the Model Registry, API Workflow
16. Adding an MLFlow Model to the Model Registry, Fetching an MLFlow Model from the Model Registry
17. Serving an MLFlow Model from Model Registry, Adding or Updating an MLFlow Model Descriptions, Renaming an MLFlow Model
18. Transitioning an MLFlow Model’s Stage, Listing and Searching MLFlow Models, Archiving an MLFlow Model, Deleting MLFlow Models
19. Assignment | Assignment Solution
1. Introduction to TFX
2. Data Ingestion using TFX & Data Validation using TFDV
3. Data Preprocessing using TFT
4. Model Training, Model Analysis & Model Evaluation using TFX
5. Model Deployment using TF Serving
6. Assignment Assignment Solution 
1. What is Kubeflow?
2. Core Kubeflow components
3. How to set up Kubeflow on Kubernetes
4. How to develop basic ML models in Kubeflow Notebooks
5. How to train and deploy models in Kubeflow
6. How to use Kubeflow Pipelines
7. How to use KFServing to deploy models
8. How to manage logs with Kubeflow Metadata component
9. Katib Hyper Parameter Tuning
10. Kubeflow Pipelines to KFServing
11. Assignment | Assignment Solution 
1. GitLab Triggers
2. AWS S3 storage
3. GitLab CI/CD Pipelines
4. Pipelines definition
5. MongoDB cloud Atlas
6. Heroku | Logdata | Coral for Monitoring
7. Assignment | Assignment Solution 
1. Amazon Sagemaker | Amazon S3 | AWS Codebuild | AWS Codecommit
2. Sagemaker Training Job | Sage Maker Endpoint | Amazon Api Gateway
3. Sagemake Model Monitoring | Cloudwatch Synthetics | Cloudwatch Alarm
4. Assignment | Assignment Solution 
1. Create an Azure Machine Learning workspace
2. Setup a new project in Azure DevOps
3. Import existing YAML pipeline to Azure DevOps
4. Declare variables for CI/CD pipeline
5. Create training compute
6. Train ML model | Register model
7. Deploy model in AKS
8. Assignment | Assignment Solution 
1. Deploy a Personalized Product Recommendation using MLOps
2. Deploy a Classification Model using MLOps on AWS
3. Deploy a Multiple Linear Regression Model using MLOps
4. Deploy a Gaussian Model in Time Series using MLOps on AWS
5. Deploy a Customer Churn Prediction using MLOps on Azure
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Course Fees

SELF PACED LEARNING

€ 370.00 € 185.00
Refund Policy
  • Duration : 30 hrs
  • Lifetime Free Upgrade
  • Reference Documents
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€ 1,021.00
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  • Duration : 30 Hrs
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Classes Starting From

  • Fast Track Batch 14 Oct 2026
  • Weekday Batch 19 Oct 2026
  • Weekend Batch 17 Oct 2026

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You will get a course completion certification after completing the training.

MLOPs Classes with Certification

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