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GenAI Skills Inventory
This form aims to catalog the Generative AI skills within your organization. The objective is to map these skills to the diverse needs based on maturity levels & needs for our customer base.
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Some questions have a clear Yes/No Answer
There are some questions which ask to rate yourself between the scale Novice to Expert
A few Examples that demonstrate a certain level of expertise in a given area have been provided.
Use these examples as a reference to help place yourself in a bucket in other areas as well
Which Location are you based in *
Southeast Asia (Singapore, Indonesia, Vietnam, Thailand, Malaysia)
India
Greater China ( Mainland, Hong Kong, Taiwan )
AUNZ (Australia, New Zealand)
South Korea
Languages Spoken *
✓
English
Mandarin
Cantonese
Korean
Thai
Indonesian / Malay
Are you GCP Professional ML engineer Certified ?
Check this link here for more details https://cloud.google.com/learn/certification/machine-learning-engineer
Yes
No
[Marked] Exam Dates Scheduled
GCP Vertex Product Expertise
1 - Novice
(eg. Understand how to demo Vertex Enterprise Search or Conversation using Drag & Drop PDF Files or coerce Large Language Model Responses using CoT or Few Shot Prompting)

2 - Beginner
(eg. Can tweak & deploy a basic Retrieval Augmented Generation Pipeline or ReACT- talk to your Data or customize results by specifying a schema and faceted search config for Structured Enterprise Search )

3 - Intermediate
( eg. Can use the Enteprise Search Retriever as a part of a larger Orchestration Pipeline or can use Generators- ConversationAI within Dialogflow CX or can deploy a model in TFSave Format to NVIDIA Triton for Inference )

4 - Advanced
(eg. Can create a fine-tuning pipeline for SAM or Style Transfer on Stable Diffusion from the Model Garden or built a multi-stage GenerativeTransaction & Information Agent on Dialogflow CX by interfacing with several Webhooks & APIs)

5 - Expert
( eg. Can use Synthetic Data Generation Techniques and execute multiple Fine-Tuning & Evaluation experiments in parallel while deploying best practices such Composable Prompting etc or optimized MatchingEngine search - Latency/accuracy trade-off on a 100's of Millions or Billions records scale
Select your Level of Expertise in each of these Products *
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Novice Beginner Intermediate Advanced Expert
AI Solutions
Search
Conversation
AI Platform : GenAI APIs & Orchestration
AI Platform: Distillation, Tuning, Evaluations
AI Platform: Pipelines, Experiments
Accelerators: TPU, GPU
GenAI Tasks Confidence
Spend a minute to look at the questions, it may be something you have not done first hand but if it appeals to your intuition of how exactly one can solve it and you have a good idea of how to use them, select those tick boxes
Fluency & Comfort in AI Prototyping Frameworks like Streamlit, Gradio, Mesop or other frameworks *
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Basic: Multi-page layout, real-time stock data API integration, LSTM price prediction, interactive Plotly charts, basic user auth
Intermediate: eg. Natural language querying, news sentiment analysis, strategy backtesting, personalized user experiences via session state
Advanced: Eg. Copilot for Relationship Managers, multiple AI models, live data streaming, custom indicator creation, visual strategy designer, Auth & Login , Streaming Displays
Using Task Specific and Foundation Models for Problem Solving across Domains *
Basic: Improve visual question answering, caption or transcript generation accuracy by X% using prompt engineering and basic image preprocessing techniques
Intermediate: Boost multimodal sentiment analysis accuracy by Y% through fine-tuning on domain-specific data and implementing cross-modal attention mechanisms
Advanced: Enhance multimodal document understanding by Z%, combining OCR, layout analysis, and language models to extract structured information from complex visual and textual inputs
Advanced LLM Capabilities
Can you implement and optimize the following advanced multimodal AI capabilities
0 - Little or no knowledge

1 - Novice
(e.g., Understand basic concepts of function calling and tool use in multimodal models, or implement simple context caching for improved response consistency)

2 - Beginner
(e.g., Implement basic parallel function calling, use JSON mode for structured outputs, or apply grounding with inline text for improved model accuracy)

3 - Intermediate
(e.g., Develop a system combining forced function calling with response validation, implement efficient context caching strategies, or use web grounding to enhance model knowledge)

4 - Advanced
(e.g., Create a complex multimodal pipeline integrating parallel function calling, JSON mode with custom schema validation, and grounding with both inline text and datastores)

5 - Expert
(e.g., Build a highly optimized multimodal system leveraging all mentioned capabilities, including advanced code interpretation, custom extensions, and sophisticated grounding techniques, while implementing best practices for scalability and performance)

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Rate your Skills on using advanced LLM Capabilities like Fn Calling, Context Caching, JSON Mode, Grounding , Code Interpreter & other extensions *
0 1 2 3 4 5
Prompting, Orchestration Frameworks & Tools
Spend a minute to look at these frameworks' Github Repos / ReadTheDocs Links. it may be something you have not used but if it appeals to your intuition of where it can be applied and you have a good idea of how to use them, select those tick boxes
Select if you know or understand how to use these LLM App Orchestration Libraries/Frameworks that you know how to apply
1/ Langchain
2/ LlamaIndex
3/ Semantic Kernel
4/ Instructor
*
✓
Langchain & LangGraph
✓
LlamaIndex
Instructor
✓
Semantic Kernel
None
Select all applicable Prompt / Guardrail Utilities Libraries/Frameworks that you know how to apply

1/ Guidance
2/ Outlines Regex Constrained Generation
3/ NeMo Self Check
4/ LMQL
5/ VertexAI JSON Mode with Response Schema
*
Instructor : Pydantic Validators
✓
MarvinAI
✓
Langchain Composable / Context-Aware Prompting
Microsoft Guidance Controlled Generation
✓
Normal Computing: Outlines
NeMo Guardrails
LMQL Templated Prompting
Select all applicable NLP / Data Index, Processing, Search and Retrieval Techniques that you know how to apply *
✓
Web Scraping, Basic HTML Parsing, Junk Removal
✓
Fixed, Recursive, Context-Aware, Specialized Document Chunking
✓
Entity Extraction, Knowledge Triplet, Data Dedupe
✓
Multi-Vector, Ensemble, Hybrid Retrieval
Contextual Compression, MapReduce or Rerank Synthesis
Hybrid Search, Boost & Bury, Faceted Filtering
PII Data Removal / Synth Data Generators
None
RAG
Spend a minute to look at these concepts in RAG. it may be something you have not used but if it appeals to your intuition of where it can be applied and you have a good idea of how to use them, select those tick boxes
Expertise Level in any of these RAG Techniques
Select the highest expertise level you have in any of these frameworks
RAG (Retrieval-Augmented Generation) expertise can be categorized into six levels, from Limited Knowledge (0) to Expert (5), across nine key pillars. Each level builds upon the previous, incorporating more sophisticated techniques and broader capabilities

Compile Time

Ingestion: Progresses from single file format processing to web-scale distributed ingestion systems.

Processing: Advances from basic tokenization and fixed-size chunking to custom OCR and layout analysis for highly complex documents.

Embedding: Evolves from simple dense vectors to state-of-the-art embedding techniques with domain adaptation.

Indexing: Develops from simple indexing with dense vectors to billion-scale indexing with advanced techniques like HNSW and PQ.
Runtime

Search Query Expansion & Rewrite: Improves from no query expansion to multi-query retrieval with dynamic prompt optimization, adversarial search query resistance

Retrieval & Reranking: Progresses from simple nearest neighbor search to reinforcement learning for dynamic re-ranking.

Latency & Recall Trade-off: Advances from no explicit optimization to dynamic optimization based on query characteristics and user feedback.

Citation Support & Grounding: Evolves from no citation support to advanced grounding with multi-hop reasoning and fact-checking.

Evaluation: Develops from simple metrics like exact match and F1 score to novel metrics for temporal consistency, causal correctness, and multi-hop reasoning.
Expertise Levels
Key milestones in expertise development example:

Limited Knowledge (0): Basic RAG implementation with simple techniques across all pillars.
Novice (1): Introduction of multi-format ingestion and basic preprocessing.
Beginner (2): Implementation of semantic chunking and basic re-ranking.
Intermediate (3): Handling of complex documents and use of sophisticated hybrid retrieval methods.
Advanced (4): Utilization of massively parallel processing, ensemble approaches, and comprehensive evaluation metrics.
Expert (5): Web-scale Production Deployment experience, A/B testing, experience in incremental improvements, continuous experimentation, and novel evaluation approaches
RAG Systems *
0 ( Little or No Knowledge) 1 2 3 4 5 ( Expert )
Ingestion
Processing
Embedding
Indexing
Search Query Rewrite & Expansion
Retrieval & reranking
Latency & Recall
Citation & Grounding
Evaluation
Frameworks & Libraries in ML/LLMOps, Training & Inference, Deep Learning, Accelerators
Spend a minute to look at these frameworks' Github Repos / ReadTheDocs Links. it may be something you have not used but if it appeals to your intuition of where it can be applied and you have a good idea of how to use them, select those tick boxes
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Expertise Level in any of these MLOps Frameworks
Select the highest expertise level you have in any of these frameworks
0 - Little or No Knowledge
(eg. Aware that Kubeflow exists, have seen some code snippets, but never worked with it)

1 - Novice
(eg. Deployed a simple pipeline using Kubeflow's Pipelines component)

2 - Beginner
(eg. Set up a hyperparameter tuning experiment using Kubeflow's Katib)

3 - Intermediate
(eg. Used Kubeflow's KFServing to deploy machine learning models into a production environment)

4 - Advanced
(eg. Created complex workflows involving multiple components like Pipelines, Katib, and KFServing)

5 - Expert
(eg. Integrated Kubeflow with existing CI/CD pipelines; have a deep understanding of its architecture and can customize its components for specific needs)

Kubeflow or VertexAI Pipelines *
0 1 2 3 4 5
None
Expert
Expertise Level in any of these Deep Learning Frameworks
Select the highest expertise level you have in any of these frameworks

1 - Novice
(eg. Implemented MNIST Handwriten Digit Classifier from TFDS using Tensorflow)

2 - Beginner
(eg. Downloaded the Huggingface Transformers library, deployed T5 for Inference)

3 - Intermediate
( eg. Used the PyTorch Profiler to Diagnose Perf Bottlenecks in Model/Data Loading)

4 - Advanced
(eg. Proficient in Model Quantization, ONNX Export, some custom CUDA Kernels)

5 - Expert
( eg. Can use vmap, mesh utils in JAX to leverage Model/Operator, Data & Pipeline, device Parallelism )
Select your Level of Expertise in these Core Deep learning frameworks *
N/A Novice Beginner Intermediate Advanced Expert
JAX
PyTorch
Tensorflow
Keras
Training & Inference - Accelerator Optimization Frameworks
Spend a minute to look at these frameworks' Github Repos / ReadTheDocs Links. it may be something you have not used but if it appeals to your intuition of where it can be applied and you have a good idea of how to use them, select those tick boxes
Huggingface Accelerate / PEFT ecosystem
Google Research - T5X ecosystem
Microsoft DeepSpeed ecosystem
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Select all Inference frameworks you know and are comfortable with deploying *
✓
Huggingface TGI + vLLM
✓
vLLM
Ray-LLM wih Ray-Serve
NVIDIA - TensorRT / Triton + TensorRT LLM
None
Accelerator Skills - TPU Focussed ( Select all that Apply) *
Size & setup a TPU Slice / Pod for Training & Inference
✓
Identify Bottlenecks using - JAX/PyTorch/TF Profiler & Tensorboard
✓
Understand Model Architecture & Optimize with Operator Fusion, Batch Size etc
None
Other Information you'd like to share with us
Share Public URLs of your work eg. Github / Medium / LinkedIn ( newline for each url)
<PRESIDIO_ANONYMIZED_URL>

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