Get Certified as Enterprise AI Transformation Analyst (CETA) @

Level 2 Certification

Certified Enterprise Transformation Analyst

CETA Main is designed for advanced AI, LLM, transformer architecture, RAG, agentic systems, multimodal AI, fine-tuning, and production-grade GenAI operations.

40+ Advanced models, architectures, and AI systems

15 Structured modules from LLMs to GenAI Ops

LLM Transformer, scaling, RAG, tuning, and decoding focus

AI Ops Agentic execution, safeguards, and production systems

01 LLM Core

Large Language Models

Foundation models trained on large-scale corpora for contextual intelligence, generation, and reasoning.

Text Tokens → Transformer → Contextual Intelligence

GPT-1 to GPT-4
BERT
RoBERTa
ALBERT
DistilBERT
T5
XLNet

Focus: Scaling laws, pretraining, fine-tuning, and enterprise trade-offs.

02 Architecture

Transformer Architecture

Deep dive into self-attention based sequence modeling and the internal mechanics of transformers.

Embeddings → Self-Attention → Multi-Head Attention → Output

Q-K-V
Positional Encoding
Residuals
Layer Norm

Math: Softmax attention, dot-product scaling, and dimensionality management.

03 Architecture

Transformer Optimization

Reduce cost, extend context, improve efficiency, and understand the constraints of attention scaling.

Long Input → Optimized Attention → Scalable Output

Sparse Attention
FlashAttention
Longformer
BigBird
Reformer

Focus: O(n²) complexity, linear scaling, and memory bottlenecks.

04 Architecture

Mixture of Experts

Use sparse activation and expert routing to scale model capacity without proportionally increasing compute.

Input Embedding → Router → Expert Selection → Weighted Output

Switch Transformer
GLaM
Mixtral
DeepSeek-V3

Focus: Conditional computation and parameter scaling without equivalent FLOP growth.

05 Architecture

State Space Models

Explore linear-time sequence modeling as an alternative path for long-context and low-memory AI systems.

Sequence → State Update → Output

SSM
Mamba

Advantages: Long-context handling, low memory use, and edge-friendly deployment.

06 LLM Core

Retrieval-Augmented Generation

Ground LLM outputs using external knowledge, search layers, vector stores, and retrieval pipelines.

Query → Retriever → Context → LLM → Answer

Basic RAG
Hybrid RAG
Multi-Hop RAG
FAISS
Pinecone
Weaviate

07 Agentic AI

Agentic RAG

Extend retrieval systems with reasoning, action, verification, and multi-step execution loops.

Goal → Retrieve → Reason → Act → Verify

Self-Reflection
Multi-Step Planning
Tool Use

Used in: Legal AI, finance, and enterprise automation.

08 Agentic AI

Agentic AI Patterns

Study the control patterns that shape model behaviour, generation, and structured AI responses.

Text → Tokens → Probabilities → Output

BPE
WordPiece
Unigram
Greedy
Beam
Top-K
Top-P
Temperature

09 Agentic AI

Autonomous Agents

Understand systems that execute multi-step goals under controlled autonomy and operational monitoring.

Objective → Planning → Execution → Monitoring

L1 Assistive
L2 Bounded
L3 Limited Autonomy
Approvals
Audit Trails

Safeguards: Caps, approvals, escalation boundaries, and traceable execution.

10 LLM Core

Multi-Modal AI

Learn joint reasoning across text, image, audio, and video through unified model architectures.

Text + Image + Audio → Unified Model → Output

CLIP
DALL·E
GPT-V
Whisper
Wav2Vec 2.0
VideoGPT

11 Architecture

Vision Transformers

Understand how transformer principles extend beyond language into image understanding and prediction.

Image → Patch Embedding → Self-Attention → Prediction

ViT
Hybrid CNN-ViT

12 Production AI

Instruction Tuning & RLHF

Align model behaviour with human intent using supervised tuning, reward models, and policy updates.

Prompt → Reward Model → Policy Update

Supervised Fine-Tuning
RLHF
FLAN-T5
InstructGPT

13 Production AI

Parameter-Efficient Fine-Tuning

Adapt large models for specific tasks without retraining every parameter from scratch.

Base Model → Adapter → Task-Specific Output

LoRA
Prefix-Tuning
Adapters
BitFit

14 LLM Core

Tokenization & Decoding

Control how language models process text, split tokens, assign probabilities, and generate output.

Text → Tokens → Probabilities → Output

BPE
WordPiece
Unigram
Greedy
Beam
Top-K
Top-P
Temperature

15 Production AI

GenAI Ops

Design production-grade AI workflows using agent patterns, execution controls, and operational loops.

Task → Agent Pattern → Controlled Execution

Plan-Act-Reflect
Supervisor-Worker
Reflexion Loop
Task Graphs

Focus: Design grammar for controlled autonomy and production AI systems.

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Move from AI literacy to enterprise AI transformation.

This level prepares learners to analyse, design, and govern advanced AI systems, from LLM architectures to agentic execution and production GenAI workflows.

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