Machine Learning & GenAI Course for DevOps Certification

Machine Learning & Generative AI Training for DevOps Engineers

Our Machine Learning & Generative AI program is designed specifically for Cloud and DevOps professionals who want to build modern AI engineering skills. The 100-hour, hands-on program takes learners from Python and Machine Learning foundations to Generative AI, RAG, Agentic AI, MLOps, LLMOps and Cloud AI.

Training includes:

1. AI, ML, Deep Learning & Generative AI foundations
2. Python, Data Handling, EDA & Machine Learning
3. LLM fundamentals, Prompt Engineering & GenAI APIs
4. Embeddings, Vector Databases & RAG pipelines
5. Agentic AI with LangChain, LangGraph, CrewAI & AutoGen
6. MLOps, LLMOps, Docker, Kubernetes & Cloud AI

100% Practical Learning:
Every module includes hands-on labs or projects focused on real-world infrastructure, DevOps and AI engineering use cases.

Machine Learning & GenAI Course with a Complete Learning Path

Machine Learning & Generative AI track delivers a well-thought-out path through 100 hours of 18 lessons for Cloud and DevOps engineers. Students advance gradually from the basics of Python and ML to building Agentic AI for real-world tasks.

The program covers:

1. Data Science & Machine Learning Foundations — 22 Hours
2. Deep Learning & LLM Foundations — 12 Hours
3. Prompt Engineering & GenAI Applications — 11 Hours
4. Embeddings, Vector Databases & RAG — 13 Hours
5. Agentic AI & Frameworks — 19 Hours
6. Open Source LLMs & Model Optimization — 5 Hours
7. MLOps, LLMOps & Cloud AI — 12 Hours
8. Production Capstone Project — 6 Hours

Learners finish with a portfolio-ready, deployed Agentic AI system and practical experience across the complete AI engineering lifecycle.

Career-Ready Skills + Online Learning

GenAI & MLOps Training with Career-Ready Skills

This training will help DevOps and Cloud professionals acquire the necessary skills to make a shift towards AI engineering.

Professionals learn to:

1. Build LLM-powered applications using modern AI APIs
2. Develop RAG-based Private GPT and enterprise knowledge systems
3. Build autonomous and multi-agent AI workflows
4. Apply AI to incident response, log analysis and runbook automation
5. Containerize and deploy AI applications using Docker & Kubernetes
6. Monitor and manage AI systems through MLOps and LLMOps practices

Career pathways include:

AI / LLMOps Engineer • GenAI Application Developer • Agentic AI Engineer • MLOps Engineer • Cloud AI Engineer • DevOps Engineer with AI Specialization

Machine Learning & GenAI Online Learning Options

Access the program through flexible learning modes designed for working professionals.

Learning includes:

1. Live interactive sessions with expert instructors
2. Recorded sessions + Live Q&A
3. 24/7 virtual labs
4. Hands-on projects and guided assessments

Career Benefits + Why Program Matters

GenAI & MLOps Certification with Proven Career Benefits

The GRRAS Machine Learning & Generative AI program helps Cloud and DevOps professionals build practical AI engineering capabilities and prepare for modern technology roles. Learners develop the ability to:

1.  Build and deploy LLM-powered applications
2.  Develop RAG and enterprise knowledge systems
3.  Create autonomous and multi-agent AI workflows
4.  Apply AI to incident response, log analysis and runbook automation
5.  Deploy and monitor AI systems using MLOps and LLMOps practices

The program combines Machine Learning foundations with 60% focus on Generative AI, including LLMs, RAG and Agentic AI.

Why Machine Learning & GenAI Training Matters

AI is increasingly being integrated into infrastructure, CI/CD pipelines, monitoring and operational workflows. DevOps and Cloud engineers who add AI engineering capabilities can move beyond traditional automation toward modern AI-enabled infrastructure.

The program is specifically designed for Cloud & DevOps professionals, rather than as a generic data science bootcamp, with every lab and project built around infrastructure and DevOps use cases.

Course Features & Offerings

Features of Machine Learning & Generative AI Course

Our well-crafted course comprises 100 hours and 18 modules covering everything from basics of Python and machine learning to building Agentic AI.

Core learning areas include:

1. Data Science & Machine Learning Foundations
2. Deep Learning & LLM Fundamentals
3. Prompt Engineering & GenAI Application Development
4. Embeddings, Vector Databases & RAG
5. Agentic AI with LangChain & LangGraph
6. Multi-Agent Systems with CrewAI & AutoGen
7. Open Source LLMs & Local Deployment with Ollama
8. MLOps, LLMOps & Cloud AI

Learning & Delivery Features

1. 100% Hands-on Labs and Projects
2. 18 Modules + Production Capstone
3. Real-world DevOps & Infrastructure Use Cases
4. Live Interactive Learning
5. Recorded Sessions + Live Q&A
6. 24/7 Virtual Labs

The curriculum is designed so that concepts are immediately applied through practical labs, projects and a final production-oriented capstone.

Tools & Practical Skills + Get Started

GenAI, MLOps & Cloud AI Tools

Through comprehensive hands-on training, you will gain practical experience with a modern AI engineering technology stack including:

1. Languages & APIs: Python, Requests, JSON, FastAPI
2. Data Science & ML: NumPy, Pandas, Matplotlib, Scikit-learn
3. LLM Providers: OpenAI, Anthropic, Google
4. Vector Databases: ChromaDB, FAISS, Pinecone
5. Agentic Frameworks: LangChain, LangGraph, CrewAI, AutoGen
6. Open Source AI: Ollama, Hugging Face, Llama, Mistral, DeepSeek, Gemma
7. MLOps / LLMOps: MLflow, DVC, BentoML, Docker, Kubernetes
8. Deep Learning: PyTorch, TensorFlow / Keras
9. Cloud AI: AWS Bedrock/SageMaker, Azure AI Studio, Google Vertex AI

Build the skills needed for the next generation of AI-powered infrastructure and DevOps. Choose a flexible learning path and progress from foundational Python and ML concepts to RAG, Agentic AI, MLOps, LLMOps and Cloud AI.

Career pathways include:

AI / LLMOps Engineer • GenAI Application Developer • Agentic AI Engineer • MLOps Engineer • Cloud AI Engineer • DevOps Engineer with AI Specialization