LIVE ONLINE • INSTRUCTOR-LED • 24-WEEK GENERATIVE AI PROGRAMME
Generative AI Course in Hyderabad
Build and evaluate Generative AI applications through live online learning across LLMs, RAG, fine-tuning, quantization, multimodal AI, evaluation and deployment concepts.
For software developers, ML engineers, data scientists, cloud engineers and working professionals interested in practical Generative AI skills.
Course fee: ₹64,999.
Course Snapshot
Page Definition
Live Online Gen AI Training in Hyderabad
The Generative AI course in Hyderabad is delivered live online through instructor-led sessions, six guided projects and mentor reviews. Learners work across LLMs, RAG, fine-tuning, quantization, multimodal AI, evaluation and deployment concepts.
Who Should Join
Who Should Take This Generative AI Course in Hyderabad?
This programme is for engineers and technical professionals who want in-depth GenAI engineering — not surface-level API tutorials. If you work with software, ML, data, or cloud and want to go deep into model foundations, RAG, fine-tuning, multimodal AI and production deployment, this course builds the missing layer.
Recommended foundation
The course begins with a refresher in Python, mathematics, machine learning and deep learning before moving into advanced Generative AI topics.
GenAI Capability Studio
What You Will Learn in This Gen AI Course
Generative AI is not just prompting. It is a stack of engineering capabilities — from understanding foundation models to grounding, adapting, expanding, evaluating, and shipping them. Each capability has a responsibility and an engineering question.
Understanding Transformer Architectures
Responsibility
Understand how transformer architectures, tokenization, embeddings, and attention work — the foundation for everything else.
Engineering Question
How do you reason about model behaviour without understanding the architecture?
Tools You Work With
UNDERSTAND
GROUND
ADAPT
EXPAND
EVALUATE
SHIP
ORCHESTRATE
Curriculum
Generative AI Course Syllabus and Learning Path for Hyderabad Learners
The curriculum follows a layered learning model — each layer builds on the previous one, moving from model foundations to production deployment.
LAYER 01
Model Foundations
ML Foundations and Deep Learning
Python essentials, mathematical foundations, ML basics, neural networks, CNNs, RNNs, transfer learning
Module 1: Foundation Refresher, Module 2: Neural Network Essentials, Module 3: Applied Deep Learning
LAYER 02
Generation
Generation and LLM Fundamentals
Transformer architecture, tokenization, attention, LLM fundamentals, GANs, vision transformers, NLP with transformers
Module 4: Generative AI Fundamentals, Module 5: LLMs Demystified, Module 6: GenAI for Vision
LAYER 03
Grounding
Retrieval-Augmented Generation
RAG architecture, chunking, embedding, retrieval, reranking, vector databases (Pinecone, FAISS, Qdrant), citations
Module 9: Retrieval-Augmented Generation
LAYER 04
Adaptation
Fine-Tuning GenAI Models
LoRA, QLoRA, PEFT, SFT, DPO, RLHF, quantization, pruning, diffusion models, fine-tuning stable diffusion
Module 8: Fine-Tuning GenAI Models
LAYER 05
Multimodal
Multimodal AI Architectures
Vision-language models, CLIP, multimodal generation, image captioning, document intelligence, VLMs
Module 7: Multimodal AI Architectures
LAYER 06
Production
Quantization, Serving and Deployment
Quantization, vLLM, TGI, FastAPI serving, Docker, Kubernetes, evaluation, RAGAS, DeepEval, agents introduction
Module 10: Quantization & Serving, Module 11: Reasoning & RLHF, Module 12: Agentic AI Introduction
Projects
Gen AI Projects You Will Build and Evaluate
You build 6 portfolio-grade GenAI systems across the programme — each one a real engineering project, not a tutorial demo. These are the projects you bring to interviews.
Problem: Before building GenAI systems, you need to understand how models are evaluated and compared — accuracy, loss, generalization, and overfitting.
Architecture: ML pipeline → model training → evaluation metrics → comparison framework → visualization.
Engineering decisions: Choosing evaluation metrics, setting up reproducible experiments, comparing model variants.
Evaluation: Cross-validation, metric selection, overfitting detection, baseline comparison.
What gets reviewed: Pipeline correctness, metric selection rationale, experiment reproducibility.
Tools and Models
Generative AI Tools, Models and Frameworks Covered
Every tool in this programme answers a capability question: why is this tool used, and what part of the GenAI stack does it serve?
Model Engineering
Frameworks for building, training, and adapting generative models.
Grounding and RAG
Orchestration frameworks and vector databases for retrieval-augmented generation.
Multimodal
Models and frameworks for vision-language and multimodal AI.
Evaluation
Tools for measuring retrieval quality, generation quality, and safety.
Serving and Deployment
Infrastructure for serving and deploying GenAI systems in production.
APIs and UI
Model APIs and interface tools for building GenAI applications.
Course Comparison
Generative AI vs AI Developer and Agentic AI Courses
SCAI offers multiple AI engineering programmes. Understanding the scope of each helps you choose the right path.
AI Developer Course
Build LLM-powered software — APIs, RAG applications, agent backends. Application-focused, faster pace.
Learn moreGenerative AI Specialization (this course)
Models + RAG + fine-tuning + multimodal + evaluation + production. Broader and deeper GenAI engineering.
Agentic AI
Stateful agents, tool use, multi-agent orchestration, and production agent systems.
Learn moreHyderabad Engineering Context
Generative AI Applications for Hyderabad's GCC and Product Teams
For professionals in Hyderabad's GCC, product and enterprise technology community, the course connects GenAI methods to application tasks such as retrieval, document workflows, model adaptation and evaluation.
Building and Evaluating GenAI Applications
Explore how retrieval, model adaptation, multimodal processing and evaluation fit into Generative AI applications. Guided projects give learners practice connecting these methods to defined use cases.
Where These Generative AI Skills Apply
- · Enterprise knowledge assistants and RAG systems
- · Document intelligence and multimodal workflows
- · Internal copilots and productivity applications
- · LLM evaluation and quality monitoring
- · Model adaptation and domain-specific GenAI
- · Application serving and deployment concepts
Local Engineering Ecosystem
Hyderabad AI and Engineering Ecosystem
Microsoft
Cloud & AI Engineering
Amazon
Cloud & AI Platforms
AI & Cloud Engineering
NVIDIA
AI Infrastructure
ServiceNow
Enterprise AI Platforms
Deloitte
Enterprise AI & Consulting
Company names provide examples of Hyderabad's broader technology ecosystem; no course affiliation is represented.
How the Program Is Taught
How Our Live Gen AI Training Works
The programme follows an Explore → Implement → Generate → Compare → Evaluate → Refine loop. You explore concepts in live sessions, implement systems in projects, generate outputs, compare approaches, evaluate quality, and refine based on feedback.
Live Technical Teaching
Live instructor-led sessions on each GenAI capability — not recorded videos.
Guided Model and Application Builds
Each project builds a connected piece of the GenAI stack, not isolated demos.
Evaluation and Failure Analysis
You learn to evaluate GenAI systems — retrieval quality, generation quality, safety — and debug failures.
Project Reviews and Technical Feedback
Mentors review your work against production standards. You learn to explain and defend your engineering decisions.
Certification & Career
Gen AI Course Certification and Career Support
Generative AI Course Certificate
Course completion certificate from School of Core AI, awarded after completing all assignments and the capstone project.
Portfolio Evidence
Your certificate is backed by 6 reviewed, production-ready GenAI projects — RAG systems, fine-tuned models, multimodal apps, and production deployments.
Career Support
Resume review, GitHub and project presentation, mock interviews, interview preparation, job-search guidance, and referral connections where available.
Course Fee and Cohort
Gen AI Course Fees and Batch Details in Hyderabad
DURATION
24 weeks / approximately 6 months
FORMAT
Live online
PROJECTS
6 guided projects
MENTOR REVIEWS
Included
RECORDINGS
Included
COHORT
Current batch details on enquiry
Contact SCAI for current batch timings, applicable taxes, payment terms and any cloud or API usage allowance.
FAQ
Generative AI Course in Hyderabad — FAQs
School of Core AI Reviews on Google
Independent Google reviews for School of Core AI, separate from programme learner experiences.
Read independent reviews of School of Core AI on Google.
Next Step
Get the Gen AI Course Syllabus, Fees and Batch Details
Tell us your current role, technical background, and what you want to build with Generative AI. The team can help you decide whether this 6 months programme is the right next step for your career in Hyderabad.
No need to enrol before understanding the course fit, curriculum and current cohort details.