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# AI2

Beyond AGI, Can AI Help Save the Planet?

Patrick Beukema
Patrick Beukema

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# Machine learning
Viking Björk Friström
Albin Sundqvist
Viking Björk Friström & Albin Sundqvist · May 13th, 2024

Making the ML Development Process Mature & Sustainable

You productionalize AI/ML development by having a good foundation. This can be given by a standard repository structure that helps you create good quality code, and use best practices but also lets you build automation on top of the structure. The focus should always remain on delivering value, guided by strategic decisions on when and how to implement these practices to best support the project's goals and context.
# ML Development
# AI/ML Development
Ryan Carson
Ryan Carson · May 10th, 2024

From A Coding Startup to AI Development in the Enterprise

Ryan shares his professional journey, tracing his transition from building Treehouse to joining Intel. The conversation evolves into a deep dive into Carson's aspiration to democratize access to AI development. Furthermore, he expounds on the exciting prospects of new technology like Gaudi three, a new ASIC for AI workloads. Ryan emphasizes the need for driving competition in compute to lower prices and increase access, underlining the importance of associating individual work with company-based OKRs or KPIs. There is also a reflection on the essentiality of forging quality relationships in professional settings and aligning work with top-level OKRs. Discussion on the potential benefits of AI in constructing and maintaining professional interactions is explored. Touching upon practical applications of AI, they also delve into smaller projects, the possibility of one-person companies, and the role of AI for daily interactions. The episode concludes with an expression of optimism about technological advances shaping the future and an appreciation for the enlightening conversation.
# Coding
# Startup
# Intel
Mahesh Murag
Jose Navarro
Nikhil Garg
Mahesh Murag, Jose Navarro, Nikhil Garg & 2 more speakers · May 8th, 2024

AI Innovations: The Power of Feature Platforms // MLOps Mini Summit #6

Building a Unified Feature Platform for Production AI Mahesh walks through Tecton’s journey to build a unified feature platform that powers large-scale real-time AI applications with only Python. He'll dive into how Tecton has navigated key tradeoffs like balancing developer experience with scalability and flexibility while adapting to rapidly evolving data and ML engineering best practices. Journey of a Made-to-Measure Feature Platform at Cleo Jose shows how the platform team at Cleo has built a production-ready Feature Platform using Feast. DIY: How to Build a Feature Platform at Home Nikhil decomposes a modern feature platform in key components, describes a few common options for each component, gotchas in assembling them, and some key architectural tradeoffs.
# AI Innovations
Salman Avestimehr
Demetrios Brinkmann
Salman Avestimehr & Demetrios Brinkmann · May 7th, 2024

FedML Nexus AI: Your Generative AI Platform at Scale

FedML is your generative AI platform at scale to enable developers and enterprises to build and commercialize their own generative AI applications easily, scalably, and economically. Its flagship product, FedML Nexus AI, provides unique features in enterprise AI platforms, model deployment, model serving, AI agent APIs, launching training/Inference jobs on serverless/decentralized GPU cloud, experimental tracking for distributed training, federated learning, security, and privacy.
# GenAI
# Nexus AI
Francesca Carminati
Francesca Carminati · May 6th, 2024

Technical Debt in ML Systems

Maintaining Machine Learning systems can be difficult and costly because they often end up with large amount of technical debt. In this presentation we will discuss the reasons why ML systems are more likely to have this type of debt and three sources of technical debt in ML systems.
# ML Systems
# Technical Debt
# King
Mohamed Elgendy
Demetrios Brinkmann
Mohamed Elgendy & Demetrios Brinkmann · May 3rd, 2024

What is AI Quality?

Delve into the multifaceted concept of AI Quality. Demetrios and Mo explore the idea that AI quality is dependent on the specific domain, equitable to the difference in desired qualities between a $1 pen and a $100 pen. Mo underscores the performance of a product being in sync with its intended functionality and the absence of unknown risks as the pillars of AI Quality. They emphasize the need for comprehensive quality checks and adaptability of standards to differing product traits. Issues affecting edge deployments like latency are also highlighted. A deep dive into the formation of gold standards for AI, the nuanced necessities for various use cases, and the paramount need for collaboration among AI builders, regulators, and infrastructure firms form the core of the discussion. Elgendy brings to light their ambitious AI Quality Conference, aiming to set tangible, effective, but innovation-friendly Quality standards for AI. The dialogue also accentuates the urgent need for diversification and representation in the tech industry, the variability of standards and regulations, and the pivotal role of testing in AI and machine learning. The episode concludes with an articulate portrayal of how enhanced testing can streamline the entire process of machine learning.
# AI Quality
# Quality Standards
# Kolena
Simon Karasik
Demetrios Brinkmann
Simon Karasik & Demetrios Brinkmann · Apr 30th, 2024

Handling Multi-Terabyte LLM Checkpoints

The talk provides a gentle introduction to the topic of LLM checkpointing: why is it hard, and how big are the checkpoints? It covers various tips and tricks for saving and loading multi-terabyte checkpoints, as well as the selection of cloud storage options for checkpointing.
# LLM Checkpoints
# Cloud Storage
# Nebius AI
Anna Maria  Modée
Anna Maria Modée · Apr 29th, 2024

Generative AI & Elastic Observability

The fact that search is not just traditional TF/IDF anymore but the current trend of machine learning and models has opened another dimension for search. This talk gives an overview of: - "Classic" search and its limitations - What is a model and how can you use it - How to use vector search or hybrid search in Elasticsearch - Where ChatGPT or similar LLMs come into play with Elastic
# Generative AI
# LLMs
# Elastic
Sol Rashidi
Demetrios Brinkmann
Sol Rashidi & Demetrios Brinkmann · Apr 26th, 2024

Leading Enterprise Data Teams

In the dynamic landscape of MLOps and data leadership, Sol shares invaluable insights on building successful teams and driving impactful projects. In this podcast episode, Sol delves into the importance of prioritizing relationships, introduces a pragmatic "Wrong Use Cases Formula" to streamline project prioritization, and emphasizes the critical role of effective communication in data leadership. Her wealth of experience and practical advice provides a roadmap for navigating the complexities of MLOps and leading data-driven initiatives to success.
# Enterprise Data Teams
# Leadership
# ExecutiveAI
Chad Sanderson
Demetrios Brinkmann
Chad Sanderson & Demetrios Brinkmann · Apr 24th, 2024

The Rise of Modern Data Management

In this session, Chad Sanderson, CEO of and author of the upcoming O’Reilly book: "Data Contracts," tackles the necessity of modern data management in an age of hyper iteration, experimentation, and AI. He will explore why traditional data management practices fail and how the cloud has fundamentally changed data development. The talk will cover a modern application of data management best practices, including data change detection, data contracts, observability, and CI/CD tests, and outline the roles of data producers and consumers. Attendees will leave with a clear understanding of modern data management's components and how to leverage them for better data handling and decision-making.
# Modern Data
# Machine Learning