Generative AI and MLOps
Defend Every Layer –
Next Cohort
Course Duration
160 Hrs
Course Overview
The Generative AI and MLOps program is a cutting-edge, career-aligned course designed to equip learners with the dual power of building intelligent generative models and deploying them reliably at scale. It blends deep knowledge of foundational and advanced Generative AI techniques (text, image, code, and multimodal generation) with real-world, production-grade Machine Learning Operations (MLOps) workflows and infrastructure practices.
Key Features
- Combines Gen AI, LLMs, and MLOps into one unified course
- Learn to build real agent-based applications with LangChain
- Focused on practical tools: OpenAI, Gemini, FastAPI, Docker
- Learn end-to-end ML lifecycle management
Skills Covered
- Real-world use cases: Chatbots, Auto-coding, Content AI
- Hands-on with LLMs, LangChain, Gemini, and OpenAI
- Cloud-native MLOps tools (Kubeflow, MLflow)
- Explainability, Bias detection, and Governance frameworks
- Model performance tracking and versioning
- FastAPI & Streamlit deployment strategies
Course Curriculum
Generative AI and MLOps
Module 1 - Foundations of NLP and Computer Vision
- Tokenization The process of converting text into smaller units like words or subwords, preparing it for input into NLP models. It’s the foundational step in transforming language into machine-readable format.
- Embeddings Convert words or images into dense numerical vectors that capture meaning, relationships, or features. Used across NLP and computer vision tasks to enhance learning.
- Text Classification Automatically categorize textual data (e.g., emails, reviews) into predefined classes using deep learning models. Applications include spam detection, topic labeling, and sentiment analysis.
- Image Processing Preprocess and transform raw images through resizing, filtering, or normalization. Ensures data quality and consistency before model training.
- Image Classification Train deep learning models to assign labels to images. Used in applications like product recognition, medical diagnostics, and safety surveillance.
- Facial Recognition Use deep learning algorithms to detect, recognize, or verify individual faces from images or video. Key in authentication, access control, and personalization systems.
- GPT(Generative Pre-trained Transformer) Study transformer-based models like GPT that understand and generate human-like text. Explore applications such as chatbots, content generation, and coding copilots.
Module 2 - LLMs, Transformers & Prompt Engineering
- Transformers Understand the architecture behind modern NLP models that use attention mechanisms to process sequences in parallel. Backbone of models like BERT and GPT.
- BERT(Bidirectional Encoder Representations from Transformers) A pre-trained transformer model that understands context from both directions. Used for tasks like question answering and NER.
- GPT(Generative Pre-trained Transformer) Explore autoregressive language models like GPT that generate coherent human-like text. Useful for chatbots, summarization, and content creation.
- Named Entity Recognition (NER) Identify and classify named entities (like people, places, organizations) in text. Widely used in information extraction and document tagging.
- Sentiment Analysis Determine the emotional tone behind textual content (positive, negative, neutral). Applied in social media monitoring and customer feedback.
- Fine-Tuning Customize pre-trained models like BERT/GPT on domain-specific data to improve task performance. Essential for adapting models to specific applications.
- Prompt Engineering Craft effective prompts to guide responses from large language models like GPT. Crucial for optimizing accuracy in zero- or few-shot learning setups.
Module 3 - Generative AI & Agentic Systems
- OpenAI APIs Integrate powerful capabilities like text generation, code completion, and embeddings using OpenAI’s models (e.g., GPT, DALL·E). Enables AI-driven applications with minimal setup.
- Gemini APIs Access Google’s Gemini models for advanced multimodal interactions including text, images, and reasoning. Ideal for enterprise and scalable AI integrations.
- Text/Image Generation Create human-like text or realistic images using generative models like GPT and DALL·E. Power use cases in content creation, design, and personalization.
- RAG Pipelines (Retrieval-Augmented Generation) Enhance generative models with external knowledge by retrieving relevant documents in real-time. Boosts accuracy in Q&A, chatbots, and enterprise AI.
- Multimodal Models Train and use models that understand and generate across multiple input types like text, images, and audio. Examples include Gemini and GPT-4 with vision.
- LangChain Build advanced AI apps by chaining together LLM calls, tools, and memory. Enables dynamic decision-making, document QA, and chat agents.
- LangGraph Extend LangChain with graph-based state management for multi-step, branching AI workflows. Ideal for agents and tool-using LLM systems.
- GANs (Generative Adversarial Networks) Use two neural networks — generator and discriminator — to create highly realistic synthetic data. Applied in deepfakes, art, and simulations.
- A2A Protocol (Agent-to-Agent) Facilitate secure, structured communication between autonomous AI agents. Useful for collaborative task execution and decentralized systems.
- MCP(Multi-Agent Collaboration Protocol)
Module 4 - UI Development & React Essentials
- React.js A popular JavaScript library for building fast and interactive user interfaces. Emphasizes reusable components and efficient DOM updates.
- Redux A predictable state container for managing application state globally in React apps. Helps handle complex data flows with ease.
- JSX(JavaScript XML) A syntax extension that allows writing HTML-like code within JavaScript. Makes UI components more readable and intuitive in React.
- Components Independent, reusable building blocks of React applications. Each component encapsulates its own logic and UI.
- Props & State Props allow data to be passed between components, while state holds local, dynamic data that affects rendering. Core concepts for interactivity.
- Routing Enables navigation between different views or pages in a single-page application. Supports dynamic routing and nested routes.
- Hooks Functions like useState, useEffect, and useContext that add state and side effects to functional components. Simplify logic reuse and component lifecycle handling.
- Form Handling Techniques to manage form input, validation, and submission within React. Covers controlled components and libraries like Formik or React Hook Form.
Module 5 - Model Deployment & Serving at Scale
- Flask A lightweight Python web framework to turn ML models into web applications and APIs. Ideal for quick deployments and prototyping.
- FastAPI A modern, high-performance web framework for building fast, asynchronous APIs. Great for production-grade ML model deployment.
- AWS/GCP Deployment Learn how to deploy ML applications and APIs on leading cloud platforms like Amazon Web Services and Google Cloud. Covers compute, storage, and CI/CD.
- Streamlit Create interactive dashboards and data science web apps with minimal code. Perfect for showcasing ML models and analytics results.
- Kubernetes Automate deployment, scaling, and management of containerized ML services. Critical for production-grade model serving in cloud environments.
- Model Serving Package and expose trained models via REST APIs or gRPC endpoints for real-time inference. Ensures fast, scalable access to predictions.
- ONNX(Open Neural Network Exchange) A format to export and run ML models across platforms and frameworks. Supports cross-framework interoperability and optimized runtime.
- Triton An NVIDIA-powered serving platform to deploy models at scale with support for TensorFlow, PyTorch, ONNX, and more. Enables GPU-accelerated inference.
- Scalable APIs Build robust APIs that handle large user loads, enable authentication, logging, and error handling. Key for production-grade AI applications.
Module 6 - Responsible AI: Ethics, Bias & Governance
- Deepfakes AI-generated synthetic media that mimics real people’s appearance or voice. Raises ethical concerns in misinformation, consent, and identity misuse.
- Explainable AI (XAI) Design AI models whose decisions can be understood and interpreted by humans. Vital for transparency in sectors like healthcare, finance, and law.
- GDPR(General Data Protection Regulation) A European Union regulation that governs how personal data is collected, processed, and protected. Affects AI systems using user data.
- Bias Refers to unfair patterns in training data or models that lead to discriminatory predictions. Must be identified and corrected for ethical AI.
- Fairness Ensures that AI systems treat all user groups equitably across race, gender, and demographics. Integral to building inclusive AI systems.
- Privacy Protecting individual data from unauthorized access or misuse in AI systems. Involves techniques like anonymization, encryption, and federated learning.
- Governance Frameworks Structured guidelines and policies to ensure ethical development, deployment, and auditing of AI technologies. Examples include OECD, NIST, and AI Act.
- Tokenization
- Embeddings
- Text Classification
- Image Processing
- Image Classification
- Facial Recognition
- GPT
- Transformers
- BERT
- GPT
- Named Entity Recognition (NER)
- Sentiment Analysis
- Fine-Tuning
- Prompt Engineering
- OpenAI APIs
- Gemini APIs
- Text/Image Generation
- RAG Pipelines
- Multimodal Models
- LangChain
- LangGraph
- GANs
- A2A Protocol
- MCP
- ML Lifecycle
- Model Tracking
- Versioning
- Automation Pipelines
- CI/CD for ML
- Data Drift Detection
- Docker
- MLflow
- Kubeflow
- Pipeline Orchestration
- Enterprise Infrastructure
- Flask
- FastAPI
- AWS/GCP Deployment
- Streamlit
- Kubernetes
- Model Serving
- ONNX
- Triton
- Scalable APIs
- Deepfakes
- Explainable AI (XAI)
- GDPR
- Bias
- Fairness
- Privacy
- Governance Frameworks
Salary Scale
Maximum
35 LPA
Average
15 LPA
Minimum
10 LPA
Job Role
- Generative AI Engineer
- MLOps Engineer
- AI/ML Developer
- Prompt Engineer
- Data Scientist – GenAI
- AI DevOps Specialist
Course Certificate
Eligible Criteria
- B.E/B.Tech in ECE, EEE, Instrumentation (Final Year or Recent Graduates)
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Possess good English communication skills
Tools & Technologies
Training Options
Online
Training
₹
20,000
Including GST*
-
24/7 LMS Access
-
Live Online Session
-
On-Campus Immersion
Classroom
Training
₹
40,000
Including GST*
-
24/7 LMS Access​
-
Peer Learning & Support
-
Career Guidance & Mentorship
Why Join this Program
All-in-One Generative AI + MLOps Program
Most programs separate them — this unites both for full-cycle AI expertise.
Career-Ready for the Future of Work
Designed for the emerging roles in Gen AI & AI Ops.
Ethics Built-In
Tackle fairness, transparency, and responsible AI from day one.
Hands-On, Real-World Projects
Build and deploy chatbots, auto-generators, agent apps, etc.
FAQ
Basic ML knowledge is helpful but core concepts are covered.
It’s best for intermediate to advanced learners.
OpenAI, LangChain, Gemini, MLflow, Triton, etc.
Yes – each module has hands-on labs.
Yes – includes AWS/GCP deployment.
GenAI Engineer, MLOps Engineer, LLM Specialist, and more.