2024 Bootcamp: Generative AI + LLM App Development

سرفصل های دوره

From zero to professional level: learn the keys to AI and build the most potential Generative AI applications.

1. Program presentation

1.1 001BENG-PRESENTATION.pdf

1. Program presentation

2. Opportunities this program will open for you

3. What will you learn in this program

4. Materials included in the program

5. Who is this program for

6. What makes this program different

7. Introduction of the Instructor

8. Share your progress

2. Tips for the students

1.1 002BENG-PRACTICAL TIPS.pdf

1. Tips for the students

2. Practical tips for the students

3.1 003BENG-THE SECRET TO SUCCESS.pdf

3. The secret to successfully completing this bootcamp

3. INTRODUCTION LLM Apps, the key to the New AI

1.1 004BENG-LLM APPS, THE UNIVERSALIZATION OF AI.pdf

1. LLM Apps, the key to the New AI

2. LLM Apps and the universalization of AI

4. ChatGPT vs. LLM Apps

1. ChatGPT vs. LLM Apps

5. Download the two books included with the program

1.1 EBOOK 100 AI Startups.pdf

1.2 EBOOK Keys to AI.pdf

1. Download the two books included with the program.html

6. PART 1 IMPORTANCE OF ARTIFICIAL INTELLIGENCE AND GENERATIVE AI

1.1 006BENG-WHAT IS AI.pdf

1.2 007BENG-CHANGES INTRODUCED.pdf

1. Intro to Artificial Intelligence

2. Artificial Intelligence What is it Why is so popular now How important is it

3. Changes introduced by AI Introduction

7. AI Changes in Employment

1.1 008BENG-CHANGES IN EMPLOYMENT.pdf

1. AI Changes in Employment

2. Jobs that will benefit the most from AI

3. Jobs most affected by AI

4. Jobs least affected by AI

5. New professions created by AI

6. The new AI Engineers

8. AI Changes in Businesses

1.1 009BENG-CHANGES IN BUSINESS (1).pdf

1. AI Changes in Businesses

2. Consequences of the changes in employment

3. Industries with high impact

4. Industries with median impact

5. Industries with immediate impact

9. AI Changes in Startups

1.1 010BENG-CHANGES IN STARTUPS.pdf

1. AI Changes in Startups

2. Opportunities for Startups Characteristics of the New AI

3. Opportunities for Startups Changes in Employment

4. Opportunities for Startups AI impact on Businesses

5. Opportunities for Startups Book 100 AI Startups

10. AI Changes in Society

1.1 011BENG-CHANGES IN SOCIETY.pdf

1. AI Changes in Society

2. Social changes generated by the New AI

3. Social challenges generated by the New AI

11. How to introduce AI in your company

1.1 012BENG-HOW TO INTRODUCE AI IN YOUR COMPANY.pdf

1. How to introduce AI in your company

2. Plan to introduce AI in your company

3. Tech and Business Analysis to introduce AI in your company

4. How to select the right pilot project to introduce AI in your company

5. How to form the first AI Team in your company

6. How to prepare the AI Strategy of your company

7. Example Plan to adopt a new LLM App in your company

12. The new AI Training

1.1 013BENG-AI TRAINING.pdf

1. The new AI Training

2. AI Training in your company an strategic necessity

3. Who should get AI Training in your company

4. How to design an AI Training Plan for your company

5. The new AI Training for engineers

13. The new AI creates opportunities for consulting, advisors and marketing agencies

1.1 014BENG-OPPORTUNITIES FOR CONSULTING FIRMS.pdf

1. The new AI creates opportunities for consulting, advisors and marketing agencies

2. Opportunities for consulting firms, business advisors, and marketing agencies

14. PART 2 LLM APPS, THE GENERATIVE AI APPLICATIONS WITH THE HIGHEST POTENTIAL

1.1 015BENG-WHAT ARE LLM APPS.pdf

1.2 016BENG-THE MYTH OF THE PREREQUISITES FOR LEARNING.pdf

1.3 017BENG-TYPES OF STUDENTS AND REASONS FOR LEARNING.pdf

1.4 018BENG-BUILD VS HIRE.pdf

1.5 019BENG-THE LONG ROAD FROM TOY DEMO TO PROFESSIONAL APP.pdf

1.6 020BENG-PROFESSIONAL OPPORTUNITIES OF THE LLM APP DEVELOPER.pdf

1. Intro LLM Apps, the Generative AI applications with the highest potential

2. What is an LLM App

3. The myth of pre-requisites for learning

4. Most frequent types of students reasons to learn

5. DIY or hire an external professional

6. The long way from the toy demo to the professional app

7. Job opportunities for the LLM App Developer

15. Use Cases for LLM Applications

1.1 021BENG-USE CASES LLMS.pdf

1.2 022BENG-LLMS USE CASES IN DATA ANALYSIS AND CONTENT CREATION.pdf

1.3 023BENG-USES FOR DATA SCIENTISTS.pdf

1.4 024BENG-LLM APP TYPES BY LEVELS OF REASONING.pdf

1.5 028BENG-GEN AI USE CASES.pdf

1.6 041BENG-TOP LLM USE CASES.pdf

1. Use Cases for LLM Applications

2. Use Cases for LLM Apps by Industry

3. Use Cases for LLM Apps in Startups

4. Use Cases for LLM Apps in Professions

5. Most frequent Use Cases for LLM Apps

6. Use Cases for LLM Apps by autonomy level

16. Intro to LLMs

1.1 025BENG-ORIGINS OF LLM APPS AI, ML, NLP, LLM.pdf

1.2 031BENG-HOW BIG ARE LLMS.pdf

1.3 032BENG-WHAT ARE FOUNDATION LLM.pdf

1. Intro to LLMs

2. Origins of LLM Apps AI, ML, NLP, Generative AI, LLMs

3. LLM size, precision, and cost

4. The Foundation LLM Models

17. LLMs Basic Concepts

1.1 033BENG-WHAT IS THE CONTEXT WINDOW.pdf

1.2 034BENG-WHAT ARE TOKENS.pdf

1.3 035BENG-PROMPTS AND RELATIONSHIP WITH CONTEXT.pdf

1.4 036BENG-PROMPT ENGINEERING AND ITS IMPORTANCE.pdf

1.5 037BENG-HALLUCINATIONS.pdf

1. LLMs Basic Concepts

2. What is the Context Window

3. What are Tokens

4. What are Prompts

5. What is Prompt Engineering

6. What are Hallucinations

18. Architecture of an LLM App

1.1 038BENG-BASIC LLM APP ARCHITECTURE.pdf

1.2 039BENG-ADVANCED ARCHITECTURE LLM APP.pdf

1. Architecture of an LLM App

2. Basic Architecture of an LLM App

3. Advanced Architecture of an LLM App

4. Preview of a professional LLM App (1)

5. Preview of a professional LLM App (2)

19. Details of the advanced architecture of an LLM Application

1.1 040BENG-TOP CONSIDERATIONS TO CHOOSE FOUNDATION MODEL.pdf

1.2 042BENG-TOP LLMs USED.pdf

1.3 043BENG-ALTERNATIVE LLMS PALM.pdf

1.4 044BENG-STACK USED.pdf

1.5 045BENG-ORCHESTRATION FRAMEWORKS.pdf

1.6 046BENG-APP DEBUGGING WITH LANGSMITH.pdf

1.7 047BENG-TOP EVALUATION METHODS.pdf

1.8 048BENG-EVALUATION MEASURING RAG PERFORMANCE.pdf

1.9 049BENG-OPTIMIZING RAG APPS.pdf

1.10 050BENG-TOP CHALLENGES DEPLOYING TO PRODUCTION.pdf

1.11 051BENG-MICROSERVICES ARCHITECTURE.pdf

1. Details of the advanced architecture of an LLM Application

2. Selection of Foundation LLMs

3. Stack of tools

4. Orchestration Frameworks

5. Other interesting notes

20. The RAG Technique (Retrieval Augmented Generation)

1.1 052BENG-OVERCOMING CONTEXT WINDOW.pdf

1.2 053BENG-THE RAG TECHNIQUE.pdf

1.3 054BENG-RAG VS IN-CONTEXT LEARNING.pdf

1.4 055BENG-RAG IS ESSENTIAL FOR LLM APP DEV.pdf

1.5 056BENG-COMPONENTS OF RAG.pdf

1.6 057BENG-EMBEDDINGS.pdf

1.7 058BENG-VECTOR DATABASES.pdf

1.8 059BENG-CHALLENGES OF RAG.pdf

1.9 113BENG-RAG TECHNIQUE ADVANCED CONCEPTS.pdf

1. The RAG Technique (Retrieval Augmented Generation)

2. Basic Concepts

3. Components

4. RAG Technique Advanced Concepts

5. Challenges

21. Selecting Orchestration Framework LangChain, LlamaIndex or OpenAI API

1.1 060BENG-LANGCHAIN, LLAMAINDEX OR OPENAI API .pdf

1. Selecting Orchestration Framework LangChain, LlamaIndex or OpenAI API

2. LangChain, LlamaIndex or OpenAI API

22. Intro to the usage of Programming Languages

1.1 084BENG-PROGRAMMING AND LLM APPS.pdf

1.2 085BENG-PRELIMINARY NOTES.pdf

1.3 086BENG-RECOMMENDATION USE VENV.pdf

1.4 087BENG-TERMINAL.pdf

1.5 088BENG-HIDDEN FILE FOR OPENAI API TOKEN.pdf

1.6 089BENG-OPERATIONS WITH JUPYTER NOTEBOOKS.pdf

1. Intro to the usage of Programming Languages

2. Never programmed before Do not worry.

3. Practical Tips if you are new to programming

4. DEMO W3Schools and ChatGPT in action

5. Virtual environment what is it, why is it important and how to create one (1)

6.1 086BENG-RECOMMENDATION USE VENV.pdf

6. Virtual environment what is it, why is it important and how to create one (2)

7. Terminal what is it, why is it important, basic operations (1)

8.1 087BENG-TERMINAL.pdf

8. Terminal what is it, why is it important, basic operations (2)

9.1 088BENG-HIDDEN FILE FOR OPENAI API TOKEN.pdf

9. File for secret credentials why is it important, how to create it

10. How to create and read Hybrid Notebooks (code + text) with Jupyter (1)

11.1 089BENG-OPERATIONS WITH JUPYTER NOTEBOOKS.pdf

11. How to create and read Hybrid Notebooks (code + text) with Jupyter (2)

23. Basic LangChain

1.1 lc-v010-001.zip

1. Warning Modifications introduced by the LangChain v0.1.0 version

2. Examples of .env file and data folder

3. Links to data folder and example of .env file.html

4. Basic LangChain

5.1 090BENG-BASIC LANGCHAIN IN 15 MIN.pdf

5.2 v1-052-langchain-in-15-mins.zip

5. Basic LangChain in 15 minutes

6.1 062ENG-LANGCHAIN MODELS.pdf

6.2 v1-401-langchain-models.zip

6. Models

7.1 063BENG-LANGCHAIN PROMPTS AND PROMPT TEMPLATES.pdf

7.2 v1-403-langchain-prompts.zip

7. Prompts and prompt templates

8.1 064BENG-FEW SHOT PROMPT TEMPLATE.pdf

8.2 v1-801-langchain-few-shot-template.zip

8. Few shot prompt templates

9.1 065BENG-LANGCHAIN OUTPUT PARSERS.pdf

9.2 v1-404-langchain-output-parsers.zip

9. Output parsers

10.1 066BENG-LANGCHAIN MEMORY.pdf

10.2 v1-405-langchain-memory.zip

10. Memory

11.1 067BENG-LANGCHAIN CHAINS.pdf

11.2 v1-406-langchain-chains.zip

11. Chains

12.1 068BENG-LANGCHAIN DOCUMENT LOADERS.pdf

12.2 v1-407-langchain-document-loaders.zip

12. Document loaders

13.1 069BENG-LANGCHAIN SPLITTERS.pdf

13.2 v1-408-langchain-splitters.zip

13. Splitters

14.1 070BENG-LANGCHAIN CALLBACKS.pdf

14.2 v1-803-langchain-callbacks.zip

14. Callbacks

15.1 071BENG-LANGCHAIN OPENAI FUNCTIONS.pdf

15.2 v1-804-langchain-openai-functions-app-avanzada-comida-china.zip

15. OpenAI functions

16.1 072BENG-LANGCHAIN CONNECT WITH FASTAPI.pdf

16.2 v1-805-basic-rag-app-qa-to-fastapi.zip

16. Connect with fastAPI

17.1 073BENG-LANGCHAIN AGENTS.pdf

17.2 v1-806-langchain-agents.zip

17. Agents

18.1 074BENG-LANGCHAIN INDEXING API.pdf

18.2 v1-807-langchain-indexing-api.zip

18. Indexing API

24. LangChain Expression Language

1. LangChain Expression Language

2.1 075BENG-LCEL CHAINS.pdf

2.2 v1-809-lcel-chains.zip

2. LCEL Chains

3.1 076BENG-LCEL OUTPUT PARSERS.pdf

3.2 v1-810-lcel-output-parsers.zip

3. LCEL Output parsers

4.1 077BENG-LCEL ARGUMENTS.pdf

4.2 v1-811-lcel-kargs-arguments.zip

4. LCEL Arguments

5.1 078BENG-LCEL OPENAI FUNCTIONS.pdf

5.2 v1-812-lcel-openai-functions.zip

5. LCEL OpenAI functions

6.1 079BENG-LCEL RAG APPS.pdf

6.2 v1-813-lcel-basic-rag-app-qa.zip

6. LCEL Aplicaciones RAG

25. LangChain Advanced Components

1. LangChain Advanced Components

2.1 080BENG-LANGCHAIN LANGSMITH.pdf

2.2 v1-808-langchain-langsmith.zip

2. LangSmith

3.1 081BENG-LANGCHAIN LANGSERVE.pdf

3.2 v1-814-langserve.zip

3. LangServe

4.1 082BENG-LANGCHAIN TEMPLATES.pdf

4.2 v1-816-langchain-templates.zip

4. LangChain Templates

5.1 083BENG-LANGCHAIN CHAT WITH LANGCHAIN.pdf

5. The new LangChain Chatbot

26. Level 1 Applications Toy Demos with LangChain

1. Reminder Modifications introduced by the LangChain v0.1.0 version

2. Reminder Examples of .env file and data folder

3. Reminder Link to data folder and sample .env file.html

4. Level 1 Applications Toy Demos with LangChain

5.1 091BENG-BASIC APP SUMMARIZE A LONG ARTICLE.pdf

5.2 v1-053-basic-app-summarization.zip

5. Basic app summarize long article

6.1 092BENG-BASIC RAG APP QA OF DOCUMENT.pdf

6.2 v1-054-basic-rag-app-qa.zip

6. Basic RAG app document QA

7.1 093BENG-BASIC APP EXTRACT DATA FROM CONVERSATION.pdf

7.2 v1-055-basic-app-text-extraction.zip

7. Basic app extract structured data from conversation

8.1 094BENG-BASIC APP EVALUATE A QA APP.pdf

8.2 v1-056-basic-app-evaluation.zip

8. Basic app eval of QA app

9.1 095BENG-BASIC APP ASK A DATABASE.pdf

9.2 v1-057-basic-app-querying-tabular-data.zip

9. Basic app ask a database

10.1 096BENG-BASIC APP ASK A GITHUB REPO.pdf

10.2 v1-058-basic-app-qa-a-github-repo.zip

10. Basic app ask a github repo

11.1 097BENG-BASIC APP ASK TO AN API.pdf

11.2 v1-059-basic-app-interacting-with-api.zip

11. Basic app ask an API

12.1 098BENG-BASIC APP CHATBOT WITH MEMORY.pdf

12.2 v1-060-basic-app-chatbot.zip

12. Basic app chatbot with personality and memory

13.1 099BENG-BASIC APP RAG WITH DEEPLAKE.pdf

13.2 v1-061-basic-rag-with-deeplake.zip

13. Basic app RAG with DeepLake

14.1 100BENG-BASIC APP A SIMPLE AGENT.pdf

14.2 v1-062-basic-app-simple-agent.zip

14. Basic app simple agent

15.1 101BENG-BASIC APP ADVANCED OUTPUT PARSER.pdf

15.2 v1-063-basic-app-with-pydantic-output-parser.zip

15. Basic app advanced output parser

27. Level 2 Applications Toy Demos with LangChain and Toy UIs with Streamlit

1.1 lc-v010-001.zip

1. Reminder Modifications introduced by the LangChain v0.1.0 version

2. Reminder Examples of .env file and data folder

3. Reminder Link to data folder and sample of .env file.html

4. Level 2 Applications Toy Demos with LangChain and Toy UIs with Streamlit

5.1 v1-102BENG-END TO END APPS WITH STREAMLIT.pdf

5. Intro to the level 2 apps

6.1 103BENG-FROM POC TO PRODUCTION.pdf

6. From Proof of Concept to Production

7.1 104BENG-BASIC STREAMLIT.pdf

7. Basic Streamlit

8.1 105BENG-WRITING APP IMPROVE REDACTION.pdf

8. App for re-writing informal text

9. Link to download the code of the app in Github and URL to try the app.html

10.1 106BENG-WRITING APP BLOG POST.pdf

10. App to write a Blog Post from a topic

11. Link to download the code of the app in Github and URL to try the app.html

12.1 107BENG-SUMMARIZING APP TXT FILE.pdf

12. App to summarize the content of a TXT file

13. Link to download the code of the app in Github and URL to try the app.html

14.1 108BENG-SUMMARIZING APP WRITING TEXT.pdf

14. App to summarize text

15. Link to download the code of the app in Github and URL to try the app.html

16.1 109BENG-EXTRACTING APP KEY DATA FROM PRODUCT REVIEW.pdf

16. App to Extract Key Data from a Product Review

17. Link to download the code of the app in Github and URL to try the app.html

18.1 110BENG-RAG APP PDF.pdf

18. RAG App to ask about the content of a private PDF file

19. Link to download the code of the app in Github and URL to try the app.html

20.1 112BENG-RAG APP CSV.pdf

20. RAG App to ask about the content of a private CSV file

21. Link to download the code of the app in Github and URL to try the app.html

22.1 114BENG-EVALUATING APP RAG.pdf

22. App to Evaluate a RAG App

23. Link to download the code of the app in Github and URL to try the app.html

28. LlamaIndex

1. LlamaIndex

2.1 1004-llamaindex.zip

2.2 116BENG-LLAMAINDEX.pdf

2. Introduction to LlamaIndex

3.1 1008-llamaindex-deep-dive.zip

3.2 117BENG-LLAMAINDEX IN DEPTH.pdf

3. LlamaIndex in depth

29. The OpenAI API

1. The OpenAI API

2.1 1001-changes-opeai-api.zip

2.2 118BENG-THE NEW OPENAI API VS LANGCHAIN.pdf

2. The OpenAI API as alternative to LangChain and LlamaIndex

3.1 1002-openai-api.zip

3.2 119BENG-OPENAI API.pdf

3. The OpenAI API in depth

4.1 120BENG-OPENAI FUNCTIONS.pdf

4. The OpenAI Functions

30. Intro to Level 3 LLM Applications Professional Applications

1. Intro to Level 3 LLM Applications Professional Applications

2. Intro to Full-Stack Applications (1)

3.1 121BENG-INTRO TO WEB APPS.pdf

3. Intro to Full-Stack Applications (2)

4. Front-End Key Elements in a Level 3 Application

5.1 1009-vercel-deep-dive.zip

5.2 122BENG-NEXTJS AND VERCEL IN DEPTH.pdf

5. Next.js and Vercel

6.1 1012-llamaindex-with-vercel.zip

6.2 123BENG-LLAMAINDEX WITH VERCEL.pdf

6. Front-End Key Elements with an Orchestration Framework LlamaIndex

7. Link to download the code of the previous lesson from Github.html

8. Back-End Key Elements in a Level 3 Application

9.1 1022-fastapi.zip

9.2 124BENG-FASTAPI.pdf

9. FastAPI

10. Link to download the code of the previous lesson from Github.html

11.1 1023-minimal-lc-fastapi-starterkit.zip

11.2 125BENG-FASTAPI WITH LANGCHAIN.pdf

11. Back-End Key Elements with an Orchestration Framework LangChain

12. Link to download the code of the previous lesson.html

31. Level 3 LLM Applications Professional Applications

1. Level 3 LLM Applications Professional Applications

2. Reminder Architecture of an Advanced LLM App

3. Reminder Preview of a Professional LLM App (1)

4. Reminder Preview of a Professional LLM App (2)

5.1 1006-sec-insights.zip

5.2 1007-full-stack-app-components.zip

5.3 126BENG-RAG APP PRODUCTION-READY TEMPLATE.pdf

5.4 127BENG-SEC INSIGHTS FULL STACK APP LLAMAINDEX.pdf

5.5 128BENG-SEC INSIGHTS ANALYSIS OF THE COMPONENTS.pdf

5. Reminder main elements of a Professional LLM App

6.1 1014-todo-app-fastapi-vercel-fullstack.zip

6.2 130BENG-FULL STACK LEVEL 1 APP TO DO APP.pdf

6. Basic Level 3 App CRUD with FastAPI, Postgres and Next.js

7. Link to download the code of the Basic Level 3 App from Github.html

8. Basic Level 3 App What is CRUD (1)

9. Basic Level 3 App What is CRUD (2)

10. Basic Level 3 App How to build the Backend (1)

11. Basic Level 3 App How to build the Backend (2)

12. Basic Level 3 App How to build the Backend (3)

13. Basic Level 3 App How to build the Frontend (1)

14. Basic Level 3 App How to build the Frontend (2)

15. Basic Level 3 App How to start the Full Stack App (1)

16. Basic Level 3 App How to start the Full Stack App (2)

17. Basic Level 3 App How to deploy the backend to Render.com (1)

18. Basic Level 3 App How to deploy the backend to Render.com (2)

19. Basic Level 3 App How to deploy the frontend to Vercel.com (1)

20. Basic Level 3 App How to deploy the frontend to Vercel.com (2)

21.1 1019-pdf-app-fastapi-vercel-fullstack.zip

21.2 131BENG-FULL STACK LEVEL 2 APP PDF APP.pdf

21. Medium Level 3 App CRUD integrated with AWS S3 (1)

22. Link to download the App code from Github.html

23. Medium Level 3 App CRUD integrated with AWS S3 (2)

24. Medium Level 3 App CRUD integrated with AWS S3 (3)

25.1 1024-langchain-plus-todo-app.zip

25.2 132BENG-TEMPLATE ENGLISH.pdf

25. Basic Level 3 App with Orchestration Frameworks and LLMs (1)

26. Reminder Modifications introduced by LangChain v010

27. Link to download the App code from Github.html

28. Basic Level 3 App with Orchestration Frameworks and LLMs (2)

29.1 1025-langchain-pdf-app.zip

29.2 133BENG-LANGCHAIN IN THE PDF FULL STACK APP.pdf

29. Medium Level 3 App with Orchestration Frameworks and LLMs (1)

30. Link to download the App code from Github.html

31. Medium Level 3 App with Orchestration Frameworks and LLMs (2)

32.1 1016-deep-dive-sec-insights.zip

32.2 129BENG-DEEP DIVE SEC INSIGHTS FULL STACK APP.pdf

32. Advanced Level 3 LLM App (1)

33. Advanced Level 3 LLM App (2)

34.1 1017-create-llama.zip

34.2 1018-chat-llamaindex.zip

34.3 134BENG-FULL STACK STARTER WITH CREATE LLAMA.pdf

34.4 135BENG-FULL STACK APP CHAT LLAMAINDEX.pdf

34. Other interesting Level 3 LLM Apps (1)

35. Other interesting Level 3 LLM Apps (2)

32. LLM Applications Advanced Concepts

1. LLM Applications Advanced Concepts

2.1 136BENG-PREPARATION OF PRIVATE DATABASES.pdf

2. Database preparation

3.1 137BENG-ADVANCED CONCEPTS TO OPTIMIZE RAG.pdf

3. RAG Optimization Advanced Concepts

4.1 138BENG-LATENCY AND SPEED OF LLM APPS.pdf

4. Latency and Speed in LLM Applications

33. Cost control in LLM Applications

1.1 139BENG-COST OF LLM APPS.pdf

1. Cost control in LLM Applications

34. LLMOps

1. LLMOps

2.1 140BENG-LLM OPS.pdf

2. Intro to LLMOps

3.1 141BENG-EVALUATION LLM APPS MISALIGNED BEHAVIOR.pdf

3. Evaluation Misaligned Behavior

4.1 142BENG-EVALUATION LLM APPS AND LACK OF REPRODUCIBILITY.pdf

4. Evaluation Lack of Reproducibility

5.1 143BENG-LLM OPS MODEL LIFECYCLE MANAGEMENT.pdf

5. Lifecycle Management

6.1 144BENG-LLM OPS RESPONSIBLE AI.pdf

6. Responsible AI

7.1 145BENG-LLM OPS SOLUTIONS.pdf

7. LLMOps Solutions

35. Top Information Channels for AI Engineers

1.1 146BENG-TOP INFORMATION CHANNELS.pdf

1. Top Information Channels for AI Engineers

36. Congrats! Next steps

1.1 147BENG-CONGRATS.pdf

1. Congrats! Next steps.

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تولید کننده: Udemy