Exploring LLMs across clouds

Don't just pick an LLM, pick an ecosystem. This session compares the complete generative AI stacks from the top three cloud providers.

#1about 3 minutes

Understanding the fundamentals of large language models

Large language models function by predicting the next most probable word in a sequence, with a "temperature" setting controlling randomness.

#2about 4 minutes

Tracing the evolution from LLMs to agentic AI

The journey from text-only models to multimodal interfaces and reasoning models has led to the development of autonomous, event-triggered agents.

#3about 2 minutes

Comparing the LLM strategies of major cloud providers

Microsoft leverages its partnership with OpenAI, Google develops its own Gemini models, and Amazon is building out its Nova family of models.

#4about 4 minutes

A detailed breakdown of foundational models by vendor

Each cloud provider offers a suite of specialized models for tasks like text embedding, multimodal input, reasoning, and image or audio generation.

#5about 3 minutes

Comparing LLM performance benchmarks and pricing models

While Google and OpenAI consistently top performance leaderboards, cloud vendors are evening out their pricing for input and output tokens.

#6about 6 minutes

Understanding retrieval-augmented generation (RAG)

RAG enhances LLM capabilities by grounding them in private data, retrieving relevant information to provide accurate, context-specific answers.

#7about 1 minute

How vector search enables semantic information retrieval

Vector search works by representing text as numerical vectors, where proximity in the vector space indicates a closer semantic meaning.

#8about 3 minutes

Comparing the RAG ecosystem across cloud platforms

Each major cloud offers a complete ecosystem for RAG, including proprietary search solutions, vector databases, storage, and integrated AI studio environments.

#9about 2 minutes

Exploring practical industry use cases for LLMs

Enterprises are already implementing LLMs for document processing automation, contact center analytics, media analysis, and retail recommendation engines.

#10about 1 minute

Implementing generative AI in development teams effectively

Successfully integrating AI tools into development workflows requires a structured change management process, including planning, testing, and documentation.