Build Your Own AI Lab
3h 18mIntermediate2025-07-01
Authors
Pearson
Omar Santos
Course details
In this course, instructor Omar Santos shows you how to create powerful and secure AI research environments. Explore both home-based and cloud-based AI labs, from hardware and software setup to security best practices, cost management, and scalability. Learn how to integrate and leverage the strengths of both environments, ensuring they have the flexibility to meet diverse research needs. Omar demonstrates how to run open-source models that can be accessed from Hugging Face, such as Llama 3, Phi 3, Mistral, Gemma, and more. Along the way, learn how to use Ollama to run these models easily from home. An ideal fit for data scientists and AI practitioners, or AI enthusiasts who want to learn more, this course covers cutting-edge AI tools such as Amazon Bedrock, Amazon SageMaker, Google Vertex AI, and Microsoft Azure Cognitive Services.
Skills covered
Vertex AIAmazon BedrockAmazon SageMakerAI Productivity ToolsAmazonArtificial Intelligence FoundationsGoogleArtificial Intelligence for BusinessArtificial Intelligence (AI)Business Software and ToolsOne-Off
Concepts
0. Introduction
- 01 - Introduction
1. Introduction to AI Labs and Sandboxes
- 02 - Learning objectives
- 03 - Home-based vs. cloud-based AI labs and sandboxes
- 04 - Choosing the right hardware (GPUs, CPUs, memory, etc.)
- 05 - Building or buying prebuilt systems
- 06 - Choosing operating systems - Linux, Windows, or macOS
- 07 - Surveying essential software
- 08 - Introducing Hugging Face
- 09 - Introducing Ollama
- 10 - Installing Ollama
- 11 - Ollama integrations
- 12 - Exploring the Ollama REST API
- 13 - Introducing retrieval augmented generation (RAG)
- 14 - Leveraging RAGFlow
2. Cloud-Based AI Labs and Sandboxes
- 15 - Learning objectives
- 16 - Pros and cons of cloud-based AI labs and sandboxes
- 17 - Introducing Amazon Bedrock
- 18 - Surveying Amazon SageMaker
- 19 - Exploring Google Vertex AI
- 20 - Using Microsoft Azure AI Foundry
- 21 - Discussing cost management and security
- 22 - Deploying Ollama in the cloud with Terraform
3. Integrating and Leveraging AI Environments
- 23 - Learning objectives
- 24 - Using hybrid AI labs to combine home and cloud resources
- 25 - Synchronizing data and projects
- 26 - Leveraging the strengths of both environments
- 27 - Running open-source models available on Hugging Face
- 28 - Introducing LangChain
- 29 - Introducing LlamaIndex
- 30 - Understanding embedding models
- 31 - Using vector databases
4. Advanced Topics
- 32 - Learning objectives
- 33 - Leveraging LangSmith and LangGraph
- 34 - Using fine-tuning frameworks
- 35 - High-performance computing and edge AI
Conclusion
- 36 - Summary