Tomislav Tipurić

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.

Exploring LLMs across clouds
#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.

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Matching moments

Addressing the core challenges of large language models
05:18 MIN

Addressing the core challenges of large language models

Accelerating GenAI Development: Harnessing Astra DB Vector Store and Langflow for LLM-Powered Apps

Understanding the core capabilities of large language models
02:26 MIN

Understanding the core capabilities of large language models

Data Privacy in LLMs: Challenges and Best Practices

The rapid evolution and adoption of LLMs
02:35 MIN

The rapid evolution and adoption of LLMs

Building Blocks of RAG: From Understanding to Implementation

Three pillars for integrating LLMs in products
01:47 MIN

Three pillars for integrating LLMs in products

Using LLMs in your Product

Understanding the fundamental shift to generative AI
04:05 MIN

Understanding the fundamental shift to generative AI

Your Next AI Needs 10,000 GPUs. Now What?

Introducing the Azure AI platform for end-to-end LLMOps
04:59 MIN

Introducing the Azure AI platform for end-to-end LLMOps

From Traction to Production: Maturing your LLMOps step by step

The challenge of applying general LLMs to enterprise problems
03:15 MIN

The challenge of applying general LLMs to enterprise problems

Give Your LLMs a Left Brain

Strategies for integrating local LLMs with your data
00:56 MIN

Strategies for integrating local LLMs with your data

Self-Hosted LLMs: From Zero to Inference

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