- iPEC Solutions — Flagship Programme
MLOps and Intelligent Automation Engineer Course
Master the skills required to build, deploy, monitor, and automate AI-powered systems with our MLOps and Intelligent Automation engineer course.
- 100% Placement Support
- Duration : 3,6,12 Months programme
- Mode: Offline / Online
Technology Tracks
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Hands-On Training
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Real-World Projects
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About the Programme
The MLOps and Intelligent Automation engineer course is built for learners who want to understand how AI, machine learning, DevOps, cloud deployment, and business automation come together in modern companies. Today, organizations are not only building AI models; they need professionals who can deploy those models, automate workflows, monitor performance, and make AI systems reliable at scale.
This course helps you learn the complete lifecycle of an AI solution. You will start with the basics of Python, Git, APIs, data workflows, and machine learning lifecycle management. Then you will move into model tracking, model registry, containerization, CI/CD pipelines, cloud deployment, monitoring, governance, and automation workflows.
By the end of this MLOps and Intelligent Automation engineer course, you will be able to create practical AI automation pipelines, deploy ML models as APIs, manage model versions, build automated workflows, and present real-time projects confidently during interviews. The course is designed with a strong focus on practical implementation, not just theory.
Global certifications programme
Curriculum
MLOps & Intelligent Automation Engineer Curriculum
12-week practical training roadmap with real-world AI, automation, deployment, and career outcomes.
| Phase / Week | Module | Topics Covered | Practical Outcome |
|---|---|---|---|
| Phase 1 / Week 1 | MLOps & Intelligent Automation Foundations | Introduction to MLOps, AI lifecycle, automation lifecycle, production AI challenges, roles and responsibilities | Understand how AI models, automation workflows, and business systems work together |
| Phase 1 / Week 2 | Python, Git & API Basics | Python revision, Git/GitHub, REST APIs, JSON, virtual environments, project structure | Build a clean Python project and push it to GitHub |
| Phase 2 / Week 3 | Machine Learning Lifecycle | Data preparation, training workflow, model evaluation, model artifacts, experiment tracking basics | Train and evaluate a machine learning model with structured outputs |
| Phase 2 / Week 4 | MLflow & Model Management | MLflow tracking, model registry, model versioning, metadata, reproducibility, model promotion stages | Track experiments and register a model for production use |
| Phase 3 / Week 5 | Docker & Model Packaging | Docker basics, Dockerfile, containers, dependency management, serving ML models as APIs | Package an ML model into a Docker container |
| Phase 3 / Week 6 | CI/CD for AI Projects | GitHub Actions, automated testing, linting, build workflows, deployment pipeline basics | Create an automated CI/CD pipeline for an AI project |
| Phase 4 / Week 7 | Cloud & Kubernetes Basics | Cloud deployment concepts, Kubernetes overview, pods, services, deployments, scaling basics | Deploy a containerized model API on a cloud-style environment |
| Phase 4 / Week 8 | Monitoring & Model Reliability | Model drift, data drift, performance monitoring, logs, alerts, dashboard basics | Build a basic monitoring dashboard for model performance |
| Phase 5 / Week 9 | Intelligent Automation Workflows | RPA concepts, workflow automation, API automation, business process automation, human-in-the-loop systems | Create an automated workflow for a real business process |
| Phase 5 / Week 10 | AI Agents & LLM Automation | Prompt workflows, LLM APIs, document automation, AI assistant workflows, safety and validation | Build an AI-powered automation assistant for a business use case |
| Phase 6 / Week 11 | Capstone Project Development | End-to-end project planning, architecture, pipeline design, deployment, automation integration | Build a complete MLOps + intelligent automation capstone project |
| Phase 6 / Week 12 | Career Preparation | Resume building, GitHub portfolio, project explanation, interview questions, mock interviews | Prepare a job-ready profile for MLOps, AI automation, and DevOps-AI roles |
Tools covered
What You Receive
Everything You Need to Succeed
We provide every resource, tool, and support system needed — from your first lesson all the way through to job placement. Nothing is left to chance.
Textbooks
Structured Notes
Cheatsheets
Practice Questions
Interview Preparation Kit
Portfolio Creation
Resume Building with Latest Tools
Real-World Projects
Case Studies
Coding Challenges
Weekly Activities
Daily Activity Report
Continuous Evaluation
Mentoring & Guidance
Doubt Clarification Sessions
Assignments with Feedback
Hands-On Projects
Projects You'll Complete
MLflow Model Lifecycle Project
Build a machine learning model, track experiments, compare performance, register the best model, and manage versioning using MLflow.
AI Model Deployment API Project
Convert a trained ML model into a REST API using Python and deploy it as a production-ready service.
Dockerized Machine Learning Application
Package an AI application using Docker with proper dependencies, environment setup, and reproducible execution.
CI/CD Pipeline for AI Deployment
Create an automated GitHub Actions pipeline that tests, builds, and prepares an AI project for deployment.
Intelligent Document Processing Automation
Build an automation workflow that extracts information from documents, validates it, and sends structured output to another system.
End-to-End MLOps and Intelligent Automation Capstone
Build a complete project that includes model training, experiment tracking, API deployment, monitoring, and an automated business workflow.
What You Will Learn
Skills & Knowledge You'll Walk Away With
After completing this MLOps and Intelligent Automation engineer course, you will be able to:
- Understand the complete machine learning lifecycle from development to production
- Use Git and GitHub for professional AI project management
- Track experiments and manage model versions using MLflow
- Package ML models using Docker
- Build and deploy ML APIs using Python
- Create CI/CD workflows for AI and automation projects
- Understand Kubernetes basics for scalable deployment
- Monitor model performance, logs, errors, and drift signals
- Automate repetitive business workflows using APIs and automation tools
- Build intelligent automation systems using AI and LLM workflows
- Design end-to-end MLOps pipelines for real-world use cases
- Prepare a job-ready GitHub portfolio with practical projects
Who Should Join
Built for Every Learner
No matter your background, age, or starting point — if you are committed, this programme is designed to take you to the next level.
Students & Fresh Graduates
Build real AI skills alongside your degree and stand out in the job market from day one — any graduation or UG background welcome.
Career Switchers
Transition into data, AI, or tech from any field — structured, step-by-step, with full mentor support and placement assistance.
Working Professionals
Upskill in AI and data to amplify your impact, earn promotions, and future-proof your career across any industry.
Entrepreneurs
Harness GenAI, automation, and data insights to make smarter decisions and build faster with a competitive AI edge.
Educators & Trainers
Integrate AI literacy and modern tools into your teaching to stay ahead of the curriculum and inspire the next generation.
Curious Beginners
Zero prior experience? No coding background needed. We start from absolute basics and move at your pace with full support.
Business & Operations Professionals
Marketing, sales, and ops professionals working with AI tools who need foundational knowledge and practical fluency.
Managers & Leaders
Evaluating AI adoption strategies and needing AI literacy to make informed decisions about technology investments.
Analysts & Researchers
Professionals from diverse fields who want to augment their analytical capabilities using AI tools and methodologies.
Career Assurance
Job Guarantee & Career Assurance Track
Our MLOps and Intelligent Automation engineer course is designed to make learners career-ready through practical training, project-based learning, resume preparation, interview practice, and portfolio building. You will not only learn tools but also understand how to explain your projects, architecture, deployment pipeline, and automation workflow in a professional interview. The course includes career guidance, mock interview support, LinkedIn and GitHub profile improvement, and project presentation training so you can confidently apply for MLOps, AI automation, DevOps-AI, and intelligent automation roles.
- 100% Job Assistance & Placement Support through iPEC AI-Hire Hub
- Career Readiness Bootcamp: resume optimisation, mock interviews, LinkedIn building
- Guaranteed Internship for eligible learners
- Access to 50+ Hiring Partners through iPEC AI-Hire Hub
- AI Interview Simulation & Big 5 Personality Assessment
- Portfolio & Resume built with latest AI tools
Career Opportunities
After completing this course, learners can apply for roles such as:
MLOps Engineer
Intelligent Automation Engineer
AI Automation Engineer
Machine Learning Operations Associate
AI Deployment Engineer
DevOps Engineer with AI Skills
Automation Developer
RPA Developer
AI Workflow Engineer
Cloud AI Operations Engineer
Why Choose iPEC Solutions
iPEC Solutions presents itself as an AI training institute in Bangalore offering AI, Machine Learning, Data Science, Power BI and IT skill development programmes, with 18 years of experience and thousands of trained learners.
18 years of training and technology education experience
Hands-on learning with practical projects and datasets
Online, offline and blended learning modes
Fast-track and advanced programme options
Python, SQL, Excel, Power BI, AI and cloud AI exposure
Resume, LinkedIn and interview preparation support
Career-focused curriculum for students and working professionals
Bangalore-based training centre with phone and WhatsApp enquiry support
Programme Director
Fathima Afroz
Programme Director — AI-Generalist Programme
- BE, M.Tech — Research @ NITK Surathkal
- 22 Years of Experience in Academic & Industry
- Areas of Interest: Data Science, Machine Learning, NLP, Speech Recognition
- Expertise in designing industry-aligned AI curriculum
- Mentor to hundreds of successful AI and data science professionals
FAQ
1. What is the MLOps and Intelligent Automation engineer course?
The MLOps and Intelligent Automation engineer course is a practical training program that teaches how to deploy, automate, monitor, and manage AI and machine learning systems in real production environments.
2. Who can join this course?
Students, freshers, developers, data analysts, data science learners, DevOps beginners, automation professionals, and working professionals who want to build a career in AI deployment and automation can join this course.
3. Do I need coding experience before joining?
Basic programming knowledge is helpful, but the course starts with Python, Git, APIs, and project structure fundamentals before moving into advanced MLOps and automation topics.
4. What tools will I learn in this course?
You will learn Python, Git, GitHub, MLflow, Docker, GitHub Actions, API development, cloud deployment basics, Kubernetes basics, monitoring concepts, and intelligent automation workflow tools.
5. Is this course practical or theory-based?
This is a hands-on course. Every major module includes practical exercises, real-time use cases, and projects so that you can build a strong portfolio for job interviews.
6. Will I work on real-time projects?
Yes. You will work on multiple hands-on projects, including MLflow model tracking, Docker deployment, CI/CD pipeline creation, AI API deployment, document automation, and a final capstone project.
7. What career roles can I apply for after this course?
You can apply for roles such as MLOps Engineer, AI Automation Engineer, Intelligent Automation Engineer, DevOps-AI Engineer, RPA Developer, ML Pipeline Developer, and AI Deployment Engineer.
8. Why should I choose this MLOps and Intelligent Automation engineer course?
This course combines MLOps, DevOps, cloud deployment, AI automation, and real-world project implementation in one structured program. It helps you move from basic AI learning to production-ready AI and automation skills.
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What Our Students Say
Student Experiences: In Their Own Words
Hear from our successful students who have transformed their careers with iPEC’s hands-on training. From mastering AI and automation to securing top industry roles, our graduates share how iPEC’s expert mentorship, real-world projects, and career-focused learning helped them achieve their dreams.