MLOps Training In Bangalore

SKU: 12722
15 Lesson
|
30 Hours
Do not look further, if you are searching for the best MLOps training in Bangalore. We, at igmGuru, provide a best in class MLOps training course in Bangalore. It includes the learning of best practices from development operations, such as creating ML pipelines, implementation of models, monitoring product performance. Our MLOps certification course in Bangalore is meticulously designed to teach you how to train ML models, track experiments, optimize models in production. In short, we provide an in-depth understanding on automating all the ML operations from development to deployment while monitoring the matrix, logs and performance.

MLOps Training In Bangalore Overview

MLOps is a set of tools, techniques and practical experience needed to train, monitor and deploy machine learning models. With this MLOps online certification course in Bangalore, participants will learn the use of each tool and technique effectively. igmGuru’s MLOps course in Bangalore is a blend of seasoned instructors, cutting-edge curriculum and hands-on learning experience.

We designed this course to equip our students with the best skills required to excel in this industry.  In order to build a successful career in this domain, it is crucial to have experience in production engineering, ML and DL concepts. Our course includes a comprehensive learning on DL and ML algorithms along with building capabilities in DevOps.

By the completion of this training, learners will be fully skilled in applying ML algorithms and libraries, designing machine learning systems and pipelines and many more. With this knowledge they can easily clear the MLOps certification exam that helps to improve the holder's credibility. Our course content covers important topics such as what is MLOps, learn about MLOps in AWS, and many more.

We also provide hands-on experience with interactive sessions where you will learn the use of various tools and technology. It includes learning on Linux, Keras, Git, Docker, TensorFlow, Graffana, Python, Prometheus, Kubernetes, and Jenkins. Join our MLOps online training in Bangalore to delve into the broad realm of machine learning operations.

Why MLOps Certification Course In Bangalore?

Our curriculum is focused to deliver this MLOps certification course in Bangalore to every level of students. Hence, whether you are a fresher or an experienced with this technology, it can help to improve your capabilities in both situations. Apart from the learning, we also offer an industry recognized certificate that can assure your skills in this domain.

This credential will be provided after the successful completion of this training.  As the top MLOps training providers in Bangalore, we prioritize the success of our students by providing them with advanced learning material. This material includes various recorded videos, documents, blogs, interview questions and test series.

We have delivered this program to more than 3300 students and most of them have established a successful career in this domain. Enroll in our MLOps online certification course in Bangalore to develop your career in this domain. We also help you learn all the essential  MLOps Tools required to perform various tasks.

Who Should Join MLOps Online Training In Bangalore? 

MLOps is a critical field that integrates machine learning with operations to automate deployment, monitoring and management of ML models in production environments. Our MLOps online training in Bangalore can benefit various professionals and individuals, including

  • Beginners
  • Data Scientists
  • machine learning Engineers
  • Software Engineers
  • DevOps Engineers
  • IT Professionals
  • Researchers

Prerequisites of MLOps Online Course In Bangalore

Before joining our MLOps online training in Bangalore, students must follow these essential requirements

  • They must have a fundamental knowledge of machine learning, Python, PyTorch and TensorFlow. 
  • Experience in working on cloud computing platforms like AWS and Azure and VCS like GIT. 
  • Although it is not necessary yet, a prior experience in DevOps will be a positive addition to learn MLOps

Key Features

MLOps Training Modules

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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MLOps Course Fees in Bangalore

SELF PACED LEARNING

US $ 399.00 US $ 199.00
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  • Duration : 30 hrs
  • Lifetime Free Upgrade
  • Reference Documents
  • 24x7 Support & Access

1 ON 1 Training

US $ 1,099.00
100% Money Back Guarantee
  • Duration : 30 Hrs
  • Plus Self Paced

Classes Starting From

  • Fast Track Batch 08 Jun 2026
  • Weekday Batch 08 Jun 2026
  • Weekend Batch 13 Jun 2026

Corporate Training

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  • Flexible Training Schedule Options
  • Industry Experienced Trainers
  • 24x7 Support

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MLOps Certification Exam

On successful completion of training, you receive an MLOps Certification by igmGuru. This is a verifiable credential you can add to your LinkedIn profile, resume, and professional portfolio. It signals to employers that you have not just studied MLOps in theory- you have built real systems and seen them through to deployment.

MLOps Certification Exam Guide 2026

If you are preparing for a specific vendor certification after this training, here is a practical breakdown of what each exam tests and how to approach it.

  • AWS ML Specialty Exam

The AWS ML Specialty exam covers four domains: data engineering, exploratory data analysis, modelling, and machine learning implementation and operations. The operations domain - which includes deployment, monitoring, and cost optimization - is where this course’s CI/CD and cloud modules will serve you most directly.

  • Azure AI Engineer Associate exam

The Azure AI Engineer Associate exam tests your ability to architect and implement AI solutions, including using Azure Machine Learning pipelines and managing deployed model endpoints. Expect scenario-based questions, not just recall.

  • Google Cloud Professional ML Engineer exam

The Google Cloud Professional ML Engineer exam is notably practical. It tests your ability to architect ML systems that are scalable, reproducible, and maintainable - the exact competencies this course builds across Modules 1 through 8.

None of these certifications requires you to attend official training before sitting the exam, though having real hands-on experience - which this course provides - is the most reliable preparation.

MLOps Certification Exam

MLOps Online Training In Bangalore FAQ's

We, at igmGuru, offer this program in three different modules and each with different price structures. We also provide various offers, which can be claimed by contacting our consultant teams via email, chat or call.

  • Corporate Training (varies according to the batch size)

  • Self Paced (19,950 rupees)

  • Instructor-Led (39,950 rupees)


Yes, we do provide a credential upon the course completion, which is highly reputed by several companies. It is a great addition to the resume in order to secure a job position in top MNCs.

As this curriculum is aligned with the current industry standards you will learn every operation of machine learning. With this program, you will discover how to train ML models, track experiments and optimize models in production.

Our seasoned trainers have meticulously designed this program to provide an in-depth understanding of ML operation according to the latest trends. This curriculum not only helps to build advanced skills but also helps to prepare for the MLOps certification exam in Bangalore.

Our learning program will provide various benefits including -

  • A recognized certificate

  • Personalized training 

  • Interactive sessions

  • 24*7 support

  • Community support

  • Excellent career opportunities

Yes, you can easily access the course material at any point of time as we provide a lifetime access to our study material.

There are various career opportunities available after completing this learning program including

  1. MLOps Engineer

  2. Machine Learning Engineer

  3. Data Scientist

  4. AI Operations Specialist

  5. DevOps Engineer

  6. Data Engineer

  7. Cloud Engineer 

  8. AI Solutions Architect

  9. ML Systems Administrator

Additional benefits in the course include • Small batches up to 10 candidates • Lifetime support and access • 1 on 1 training option available • Flexible schedule
Yes, this course is MLOps certification based training, and certification is provided online after one has successfully cleared the course assignments and test with the minimum required cut-off.
The course will be conducted online through live meetings and will have a minimum total duration of 30 hours.

Yes, igmGuru offers several other online certification courses. These include specialized online certification courses, tailored to different levels. igmGuru, greatly emphasizes upskilling and boosting career opportunities across any industry sectors, with each online certification course designed to help learners enhance their expertise.

Yes, igmGuru offers several other online courses under Machine Learning or Artificial Intelligence . These include specialized online courses, tailored to different skill levels. igmGuru greatly emphasizes upskilling and boosting career opportunities across IT industry sectors, with each online course designed to help learners enhance their expertise in Machine Learning or Artificial Intelligence . Below are few listed Courses.

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