AI Workflow & Data Preparation - Infinia plays a crucial role in AI workflows, particularly in data preparation stages, by handling diverse data ingestion, providing low-latency KV store access at scale, and integrating with various AI platforms [2] - The AI pipeline involves data collection, pre-processing, tagging, and indexing as key data preparation steps [1] - DDN's Infinia, combined with Spark integrations, facilitates a smooth and scalable workflow using familiar tools for AI developers [6][7] Data Management & Security - Infinia addresses the challenge of providing secure data buckets for multiple developers through multi-tenancy controls, enabling dynamic addition or removal of secure tenants and subtenants [6] - DDN has developed Spark integrations to efficiently move data into developer tenant buckets [6] - Infinia's multi-tenancy can create secure locations for hosting data used in each inference pipeline [9] Mortgage Default Modeling Demo - The demonstration uses 10 years of quarterly mortgage finance data to model delinquency rates and probabilities on mortgage defaults [4] - Apache Spark is used to prepare the data and pipe it into a model training process that could be run on top of Infinia [3] - The workflow includes extracting recent data subsets, copying them into new Infinia buckets using Spark, and transforming the data into parquet files for model training [4][8] - The model training utilizes the XGBoost machine learning library to create a predictive model for mortgage defaults [9]

Hey everyone, this is Christina from OpenAI. Welcome to Agent Builder 101. Agent Builder is a new visual tool for building AI workflows.You connect nodes and create agents without writing any code. So you can start from templates or build your own from scratch. And it also comes with built-in eval so you can test and understand how your agents perform.When you're ready, you can export the workflow as code or drop it straight into your product. Basically, it's your all-in-one space to design, test, and launc ...

Pytest for LLM Apps is finally here!DeepEval turns LLM evals into a two-line test suite to help you identify the best models, prompts, and architecture for AI workflows (including MCPs).Works with all frameworks like LlamaIndex, CrewAI, etc.100% open-source with 11k stars! https://t.co/Xayu1aFGFV ...

Overview - Mastra.ai is a TypeScript framework designed to streamline the development of agentic AI systems, offering an alternative to traditional approaches using LangChain and vector databases [1] - The workshop aims to equip participants with the skills to develop scalable AI-driven internal tools based on sound software engineering principles [1] Technical Aspects - Participants will learn to build structured AI workflows with composable tools and reliable control [1] - The session covers Mastra installation, running a local MCP server, defining tools and agents in TypeScript, and using the Mastra playground [1] - Practical examples include RAG setups and tool-chaining agents [1] Application - The framework enables the creation of internal AI assistants capable of handling requests like data cleaning, email drafting, and document summarization with minimal code [1] Speakers - Nick Nisi is an elite software engineer with expertise in open source web development [1] - Zachary Proser builds AI systems and shares his learnings through sample applications, technical guides, and real-world lessons [1]