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Finetuning Open-Source LLMs // LLMs in Production Conference 3 Keynote 1

Sebastian Raschka
Demetrios Brinkmann
Sebastian Raschka & Demetrios Brinkmann

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Davis Blalock
Bandish Shah
Abhi Venigalla
+3
Davis Blalock, Bandish Shah, Abhi Venigalla & 3 more speakers · Apr 12th, 2024

[Exclusive] Databricks Roundtable // Introducing DBRX: The Future of Language Models

DBRX is designed to be especially capable of a wide range of tasks and outperforms other open LLMs on standard benchmarks. It also promises to excel at code and math problems, areas where others have struggled. Our panel of experts will get into the technical nuances, potential applications, and implications of DBRx for businesses, developers, and the broader tech community. This session is a great opportunity to hear from insiders about how DBRX's capabilities can benefit you.
# LLMs
# DBRX
# Databricks
# Databricks.com
Savin Goyal
Savin Goyal · Apr 9th, 2024

Best Practices Towards Productionizing GenAI Workflows

Savin Goyal, the Co-founder and CTO of Outerbounds and former Netflix tech lead discusses Metaflow, an open-source platform for managing machine learning infrastructure. He explores "Gen AI" and its impact on personalized customer experiences, emphasizing data's crucial role in ML infrastructure, including storage, processing, and security. Savin highlights Metaflow's orchestration capabilities, simplifying deployment for data scientists through Python-based infrastructure as code. The platform addresses engineering challenges like optimizing GPU usage and handling multitenant workloads while emphasizing continuous improvement and reproducibility. Goyal advocates for agile experimentation and the development of "full stack data scientists," presenting Metaflow as a solution for securely connecting to data warehouses and generating embeddings.
# GenAI
# Metaflow
# Outerbounds
# Outerbounds.com
Shane Morris
Demetrios Brinkmann
Shane Morris & Demetrios Brinkmann · Apr 5th, 2024

Data Engineering in the Federal Sector

Let's focus on autonomous systems rather than automation, and then super-narrow it down to smaller, cheaper, and more accessible autonomous systems.
# Data Engineering
# Federal Sector
# Devis
# Devis.com
Peter Guagenti
Demetrios Brinkmann
Peter Guagenti & Demetrios Brinkmann · Apr 2nd, 2024

What Business Stakeholders Want to See from the ML Teams

Peter Guagenti shares his expertise in the tech industry, discussing topics from managing large-scale tech legacy applications and data experimentation to the evolution of the Internet. He returns to his history of building and transforming businesses, such as his work in the early 90s for People magazine's website and his current involvement in AI development for software companies. Guagenti discusses the use of predictive modeling in customer management and emphasizes the importance of re-architecting solutions to fit customer needs. He also delves deeper into the AI tools' effectiveness in software development and the value of maintaining privacy. Guagenti sees a bright future in AI democratization and shares his company's development of AI coding assistants. Discussing successful entrepreneurship, Guagenti highlights balancing technology and go-to-market strategies and the value of failing fast.
# ML Teams
# Business Stakeholders
# Tabnine
# Tabnine.com
Chandan Maruthi
Chandan Maruthi · Apr 1st, 2024

AI for Customer Experience Teams

Chandan discusses the concept of retrieval-augmented generation (RAG), emphasizing its relevance in enterprise settings where specific data and knowledge take precedence over generalized internet information. He delves into the intricacies of building and optimizing RAG systems, including data pipelines, data ingestion, semantic stores, embeddings, vector stores, semantic search algorithms, and caching. Maruthi also addresses the challenges and considerations in building and fine-tuning AI models to ensure high-quality responses and effective evaluation processes for AI systems. Throughout the talk, he provides practical guidance and valuable considerations for implementing AI solutions to elevate customer experience.
# AI
# RAG
# TwigAI
# Twig.so
Amritha Arun Babu
Abhik Choudhury
Demetrios Brinkmann
Amritha Arun Babu, Abhik Choudhury & Demetrios Brinkmann · Mar 29th, 2024

MLOps - Design Thinking to Build ML Infra for ML and LLM Use Cases

As machine learning (ML) and large language models (LLMs) continue permeating industries, robust ML infrastructure and operations (ML Ops) are crucial to deploying these AI systems successfully. This podcast discusses best practices for building reusable, scalable, and governable ML Ops architectures tailored to ML and LLM use cases.
# MLOps
# ML Infra
# LLM Use Cases
# Klaviyo
# IBM
Jineet Doshi
Jineet Doshi · Mar 25th, 2024

Evaluating Generative AI Systems

Jineet Doshi, an AI lead at Intuit, offered valuable insights into evaluating generative AI systems. Drawing from his experience architecting Intuit's platform, he discussed various evaluation approaches, including traditional NLP techniques, human evaluators, and LLMs. Jineet highlighted the importance of establishing trust in these systems and evaluating for safety and security. His comprehensive overview provided practical considerations for navigating the complexities of evaluating generative AI systems.
# GenAI
# LLMs
# Intuit
# Intuit.com
Bandish Shah
Davis Blalock
Demetrios Brinkmann
Bandish Shah, Davis Blalock & Demetrios Brinkmann · Mar 22nd, 2024

The Art and Science of Training LLMs

What's hard about language models at scale? Turns out...everything. MosaicML's Davis and Bandish share war stories and lessons learned from pushing the limits of LLM training and helping dozens of customers get LLMs into production. They cover what can go wrong at every level of the stack, how to make sure you're building the right solution, and some contrarian takes on the future of efficient models.
# LLMs
# MosaicML
# Databricks
Chris Booth shared insights into leveraging autonomous agents to enhance language model (LLM) readiness for production. Drawing from his experience as a Product Owner for machine learning, Chris highlighted the role of autonomous agents in streamlining processes and demonstrated their capabilities through a live financial data extraction demo. He emphasized addressing challenges such as reasoning, latency, and explainability, advocating for techniques like chain prompting and advanced models. Chris also encouraged open-source collaboration to fine-tune LLMs and integrate knowledge graphs for improved performance and reliability.
# Autonomous Agents
# LLMs
# NatWest Groups
# natwestgroup.com
Engineering deep-dive into the world of purpose-built databases optimized for vector data. In this live session, we explore why non-purpose-built databases fall short in handling vector data effectively and discuss real-world use cases demonstrating the transformative potential of purpose-built solutions. Whether you're a developer, data scientist, or database enthusiast, this virtual roundtable offers valuable insights into harnessing the full potential of vector data for your projects.
# Vector Database
# MLOps
# Zilliz
# Zilliz.com
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