#foundation-models

#foundation-models

Amazon SageMaker AI is a fully managed ML service. With SageMaker AI, data scientists and developers can quickly and confidently build, train, and deploy ML models into a production-ready hosted environment. It provides a UI experience for running ML workflows that makes SageMaker AI ML tools available across multiple integrated development environments (IDEs). Within a few steps, you can deploy a model into a secure and scalable environment from the SageMaker AI console.

Today, we're talking about building real AI products with foundation models. Not toy demos, not vibes. We'll get into the boring dashboards that save launches, evals that change your mind, and the shift from analyst to AI app builder. Our guide is Hugo Bowne-Anderson, educator, podcaster, and data scientist, who's been in the trenches from scalable Python to LLM apps. If you care about shipping LLM features without burning the house down, stick around.

Every startup lives with the same tension: a large incumbent can copy or crush it at any time. For Harvey, an artificial intelligence startup that builds tools for law firms and enterprises, the model-maker that powers its tech also has the power to destroy it. From Harvey's Manhattan office on Friday afternoon, cofounders Winston Weinberg and Gabe Pereyra put it bluntly. Harvey's toughest competitor isn't another niche startup; it's OpenAI.

Imagine that you want a robot to sort a pile of laundry into whites and colors. Gemini Robotics-ER 1.5 would process the request along with images of the physical environment (a pile of clothing). This AI can also call tools like Google search to gather more data. The ER model then generates natural language instructions, specific steps that the robot should follow to complete the given task.

The platforms emerging in AI app generation aren't locked in zero-sum battles - they're carving out differentiated spaces and coexisting, fostering diversity and innovation.

Amazon Bedrock AgentCore enables enterprise-grade services for deploying AI agents at scale, providing infrastructure, tools, and controls for trustworthy operations.