A deep self-paced bootcamp covering Generative AI, prompt engineering, NLP, deep learning, transformer architecture, BERT, NER fine-tuning, text generation, MLOps, FastAPI, Docker, Kubernetes, SageMaker, and production AI foundations.
Modern GenAI tools make it easy to look productive without understanding what's happening underneath. When learners try to build serious systems, the gaps show up fast — in NLP, in deep learning, in transformer internals, in production workflows.
Preprocessing, tokenization, and classical language representations get skipped in favour of chat UIs.
Neural networks, activation functions, initialization, and regularization aren't a comfort zone.
Self-attention, encoder/decoder blocks, positional information — conceptually present, structurally unclear.
Learners repeat the phrase "attention is all you need" without a working mental model.
Different transformer variants get used interchangeably without understanding when each one fits.
Fine-tuning a transformer for a real downstream task feels far from the prompt-only workflows.
Greedy search, beam search, and decoding strategy trade-offs rarely get explored deliberately.
Model packaging, FastAPI serving, and monitoring don't yet show up in the learner's actual workflow.
Container orchestration and managed AI platforms are read about, never worked through.
Prompting alone is not enough. Serious AI learners need to understand the foundations behind GenAI systems — NLP pipelines, transformer models, and production workflows — before Agentic AI, RAG, and LLMOps start making real sense.
This bootcamp exists to help learners build depth in the foundations that support modern GenAI and production AI engineering. Not a crash course, not a prompt trick collection — a long-form technical foundation.
Instead of jumping between disconnected tutorials, this bootcamp brings the foundations together into a single structured path — from prompt engineering all the way through to production AI workflows on Kubernetes and SageMaker.
This is a 71+ hour bootcamp. Read the fit carefully before enrolling — it saves everyone time.
By the end of the bootcamp, learners will be able to reason across the full stack — from prompt engineering intuition to transformer internals to Kubernetes-based deployment.
GenAI & prompt engineering foundations
NLP pipelines and text preprocessing
Classical NLP representations
Deep learning basics for language systems
RNNs, LSTMs, attention & transfer learning
Transformer architecture and attention mechanisms
NER fine-tuning on real downstream tasks
Text generation using beam & greedy search
BERT variants for different tasks
Transformer models in production
Few-label & no-label learning approaches
Current trends in transformer architecture
MLOps foundations for AI systems
Docker, packaging & FastAPI implementation
Model monitoring in production
Kubernetes for ML projects
SageMaker deep dive for GenAI workflows
Agentic AI, RAG, AI Evals, and LLMOps become easier to understand when learners already have strong foundations underneath — not just prompt patterns. Modern GenAI systems are built on top of a language modelling and production ML stack.
Scope note. This is not the current Agentic AI implementation bootcamp. It is the foundation layer that prepares learners for deeper work in Agentic AI, RAG, LLMOps, AI Evals, and production AI engineering.
The full curriculum — every section, every included topic. Expand any section to see the lectures inside.
Follow the natural progression — each step builds the mental model needed for the next.
Anchor intuition in what LLMs are actually doing before diving deeper.
Build the vocabulary of preprocessing, tokenization, and classical representations.
Neural nets, activation, training dynamics, RNNs, LSTMs, and transfer learning.
Set up the production mindset before any code ships.
Package models, containerise them, and serve them behind a real API.
Understand how models drift, degrade, and get noticed in production.
Orchestration foundations that show up everywhere in production AI.
Go deep into fine-tuning for downstream tasks and decoding strategy trade-offs.
Understand which variant fits which task — and what that looks like in production.
Close with current architecture trends and a hands-on SageMaker deep dive.
Most GenAI content stops at the surface. This bootcamp goes underneath.
Long-form technical content — not tutorial fragments stitched together.
Prompting is one section. The other seventeen build the foundations underneath.
The three foundations of modern language systems, taught in one connected path.
MLOps, Docker, FastAPI, Kubernetes, SageMaker — not left for "later".
The right foundation before Agentic AI, RAG, LLMOps, and production AI systems.
Real classroom pacing — walk-throughs, Q&A moments, and concept unfolding.
One self-paced bundle — 71+ hours across 18 sections and 54 lectures.
One price. Full bootcamp. Self-paced access. Deep foundations across GenAI, NLP, transformers, and production AI.
71+ hours of deep technical content across GenAI, NLP, transformers, deep learning, and production AI workflows — recorded from live bootcamp-style sessions.
Recorded from live bootcamp-style sessions. This is a foundations bootcamp — not the current Agentic AI implementation bootcamp.
Direct answers about scope, depth, and fit.
No. This is a Generative AI, NLP, transformer, and production AI foundations bootcamp. It prepares learners for deeper work in Agentic AI, RAG, LLMOps, and production AI systems — but it is not the current Agentic AI implementation bootcamp.
The bootcamp includes 71+ hours of recorded content across 18 sections and 54 lectures.
No. Prompt engineering is only one part. The bootcamp also covers NLP, deep learning, transformers, NER fine-tuning, text generation, MLOps, Kubernetes, and SageMaker.
Yes. It includes a deep NLP section with language processing concepts, Word2Vec, TensorFlow references, and transformer updates.
Yes. It includes neural networks, TensorFlow, activation functions, RNNs, LSTMs, transfer learning, and a transformer architecture overview.
Yes. It includes transformer architecture, BERT variants, transformer models in production, and current transformer trends.
Yes. It includes MLOps foundations, version control, Docker, model packaging, FastAPI, monitoring, Kubernetes, and production-oriented workflows.
Yes. It includes a SageMaker deep dive for GenAI workflows.
It is best for learners who are ready for long-form technical learning. Some sections cover foundations, but learners should be willing to study NLP, deep learning, and production AI concepts seriously.
Yes. This is a self-paced recorded bootcamp created from live bootcamp-style sessions.
Yes. It builds deeper foundations in NLP, transformers, deep learning, MLOps, and production AI workflows, which are useful before advanced Agentic AI and LLMOps learning.
Strengthen your understanding of GenAI, NLP, transformers, deep learning, MLOps, Kubernetes, SageMaker, and production AI workflows through 71+ hours of structured recorded bootcamp content.
Enroll Now71+ hours · 18 sections · 54 lectures · self-paced recorded bootcamp.