Level 2 - Certified Enterprise Transformation Analyst (CETA)
Course Curriculum
๐๐๐ง๐ ๐ ๐๐๐กย โย ๐๐ฑ๐๐ฎ๐ป๐ฐ๐ฒ๐ฑ ๐๐,ย ๐๐๐ ย &ย ๐๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐ฆ๐๐๐๐ฒ๐บ๐
40+ Advanced Models, Architectures & Systems
1. ๐๐ฎ๐ฟ๐ด๐ฒ ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ ๐ ๐ผ๐ฑ๐ฒ๐น๐ (๐๐๐ ๐)
Foundation models trained on large-scale corpora
1.
Text Tokens
2.
Transformer
3.
Contextual Intelligence
Models: GPT-1 โ GPT-4, BERT, RoBERTa, ALBERT, DistilBERT, T5, XLNet
Focus: Scaling laws, pretraining vs. fine-tuning, enterprise trade-offs
2. ๐ง๐ฟ๐ฎ๐ป๐๐ณ๐ผ๐ฟ๐บ๐ฒ๐ฟ ๐๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฒ (๐๐ฒ๐ฒ๐ฝ ๐๐ถ๐๐ฒ)
Self-attentionโbased sequence modeling
1.
Embeddings
2.
Self-Attention
3.
Multi-Head Attention
4.
Output
Components: QโKโV, Positional Encoding, Residuals, Layer Norm
Math: Softmax attention, Dot-product scaling, Dimensionality management
3. ๐ง๐ฟ๐ฎ๐ป๐๐ณ๐ผ๐ฟ๐บ๐ฒ๐ฟ ๐ข๐ฝ๐๐ถ๐บ๐ถ๐๐ฎ๐๐ถ๐ผ๐ป & ๐ฆ๐ฐ๐ฎ๐น๐ถ๐ป๐ด
Reduce cost, increase context, improve efficiency
1.
Long Input
2.
Optimized Attention
3.
Scalable Output
Techniques: Sparse Attention, FlashAttention, Longformer, BigBird, Reformer
Focus: Computational complexity $O(n^2)$ vs linear scaling, memory bottlenecks
4. ๐ ๐ถ๐ ๐๐๐ฟ๐ฒ ๐ผ๐ณ ๐๐ ๐ฝ๐ฒ๐ฟ๐๐ (๐ ๐ผ๐)
Sparse activation of expert sub-models
1.
Input Embedding
2.
Router Mechanism
3.
Expert Selection
4.
Weighted Output
Models: Switch Transformer, GLaM, Mixtral (SMoE), DeepSeek-V3
Focus: Scaling parameters without increasing FLOPs, conditional computation
5. ๐ฆ๐๐ฎ๐๐ฒ ๐ฆ๐ฝ๐ฎ๐ฐ๐ฒ ๐ ๐ผ๐ฑ๐ฒ๐น๐ (๐ฆ๐ฆ๐ ๐)
Linear-time sequence modeling
1.
Sequence
2.
State Update
3.
Output
Models: SSM, Mamba
Advantages: Long-context, low-memory, edge-friendly
6. ๐ฅ๐ฒ๐๐ฟ๐ถ๐ฒ๐๐ฎ๐น-๐๐๐ด๐บ๐ฒ๐ป๐๐ฒ๐ฑ ๐๐ฒ๐ป๐ฒ๐ฟ๐ฎ๐๐ถ๐ผ๐ป (๐ฅ๐๐)
Ground LLM outputs using external knowledge
1.
Query
2.
Retriever
3.
Context
4.
LLM
5.
Answer
Architectures: Basic RAG, Hybrid RAG, Multi-Hop RAG
Tools: FAISS, Pinecone, Weaviate
7. ๐๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐ฅ๐๐
RAG + reasoning + action
1.
Goal
2.
Retrieve
3.
Reason
4.
Act
5.
Verify
Capabilities: Self-Reflection, Multi-Step Planning, Tool Use
Used in: legal AI, finance, enterprise automation
8. ๐๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐๐ ๐ฃ๐ฎ๐๐๐ฒ๐ฟ๐ป๐
Control how models think and speak
1.
Text
2.
Tokens
3.
Probabilities
4.
Output
Tokenizers: BPE, WordPiece, Unigram
Decoding: Greedy, Beam, Top-K, Top-P, Temperature
9. ๐๐๐๐ผ๐ป๐ผ๐บ๐ผ๐๐ ๐๐ด๐ฒ๐ป๐๐
Systems that execute multi-step goals
1.
Objective
2.
Planning
3.
Execution
4.
Monitoring
Autonomy levels: L1 (Assistive), L2 (Bounded), L3 (Limited Autonomy)
Safeguards: caps, approvals, audit trails
10. ๐ ๐๐น๐๐ถ-๐ ๐ผ๐ฑ๐ฎ๐น ๐๐
Joint reasoning across data types
1.
Text + Image + Audio
2.
Unified Model
3.
Output
Models: CLIP, DALLยทE, GPT-V, Whisper, Wav2Vec 2.0, VideoGPT
11. ๐ฉ๐ถ๐๐ถ๐ผ๐ป ๐ง๐ฟ๐ฎ๐ป๐๐ณ๐ผ๐ฟ๐บ๐ฒ๐ฟ๐ (๐ฉ๐ถ๐ง)
Transformers beyond language
1.
Image
2.
Patch Embedding
3.
Self-Attention
4.
Prediction
Models: ViT, Hybrid CNN-ViT
12. ๐๐ป๐๐๐ฟ๐๐ฐ๐๐ถ๐ผ๐ป ๐ง๐๐ป๐ถ๐ป๐ด & ๐ฅ๐๐๐
Align models with human intent
1.
Prompt
2.
Reward Model
3.
Policy Update
Techniques: Supervised Fine-Tuning, RLHF, FLAN-T5, InstructGPT
13. ๐ฃ๐ฎ๐ฟ๐ฎ๐บ๐ฒ๐๐ฒ๐ฟ-๐๐ณ๐ณ๐ถ๐ฐ๐ถ๐ฒ๐ป๐ ๐๐ถ๐ป๐ฒ-๐ง๐๐ป๐ถ๐ป๐ด (๐ฃ๐๐๐ง)
Adapt models without retraining everything
1.
Base Model
2.
Adapter
3.
Task-Specific Output
Methods: LoRA, Prefix-Tuning, Adapters, BitFit
14. ๐ง๐ผ๐ธ๐ฒ๐ป๐ถ๐๐ฎ๐๐ถ๐ผ๐ป & ๐๐ฒ๐ฐ๐ผ๐ฑ๐ถ๐ป๐ดโ
Control how models think and speak
1.
Text
2.
Tokens
3.
Probabilities
4.
Output
Tokenizers: BPE, WordPiece, Unigram
Decoding: Greedy, Beam, Top-K, Top-P, Temperature
15. ๐๐ฒ๐ป๐๐ ๐ข๐ฝ๐ (๐ฃ๐ฟ๐ผ๐ฑ๐๐ฐ๐๐ถ๐ผ๐ป ๐๐)โ
Design grammar for autonomy
1.
Task
2.
Agent Pattern
3.
Controlled Execution
Patterns: Plan-Act-Reflect, Supervisor-Worker, Reflexion Loop, Task Graphs