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In this podcast episode, we talked with Andrey Cheptsov about ​The future of AI infrastructure.

About the Speaker: Andrey Cheptsov is the founder and CEO of dstack, an open-source alternative to Kubernetes and Slurm, built to simplify the orchestration of AI infrastructure. Before dstack, Andrey worked at JetBrains for over a decade helping different teams make the best developer tools. During the event, the guest, Andrey Cheptsov, founder and CEO of dstack, discussed the complexities of AI infrastructure. We explore topics like the challenges of using Kubernetes for AI workloads, the need to rethink container orchestration, and the future of hybrid and cloud-only infrastructures. Andrey also shares insights into the role of on-premise and bare-metal solutions, edge computing, and federated learning. 00:00 Andrey's Career Journey: From JetBrains to DStack 5:00 The Motivation Behind DStack 7:00 Challenges in Machine Learning Infrastructure 10:00 Transitioning from Cloud to On-Prem Solutions 14:30 Reflections on OpenAI's Evolution 17:30 Open Source vs Proprietary Models: A Balanced Perspective 21:01 Monolithic vs. Decentralized AI businesses 22:05 The role of privacy and control in AI for industries like banking and healthcare 30:00 Challenges in training large AI models: GPUs and distributed systems 37:03 DeepSpeed's efficient training approach vs. brute force methods 39:00 Challenges for small and medium businesses: hosting and fine-tuning models 47:01 Managing Kubernetes challenges for AI teams 52:00 Hybrid vs. cloud-only infrastructure 56:03 On-premise vs. bare-metal solutions 58:05 Exploring edge computing and its challenges

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The future of AI infrastructure - Andrey Cheptsov

Outline:

1. On current challenges of AI infrastructure
2. What is dstack?
3. Code as infrastructure
4. Trends in AI infrastructure

About the speaker:

Andrey Cheptsov is the founder and CEO of dstack, an open-source platform revolutionizing AI infrastructure management for cloud and on-premises. With a background in product management at JetBrains and an Executive MBA from the Technical University of Munich, Andrey combines deep technical expertise with a passion for innovation in AI and developer tools.

Join our slack: https://datatalks.club/slack.html

Hallo world,

After some hiatus we are back, join us in our April meetup

18:00 Doors open 18:30 - 18:45 Organizers & Host Welcome and Introduction 18:45 - 19:30 Andrey Cheptsov, Founder of dstack.ai, Topic: Accessing GPUs From Multiple Providers With dstack Sky 19.30 - 20:15 Cornelia Gruber, PhD Candidate in Statistics at LMU, Topic: More Labels or Cases? Assessing Label Variation in Natural Language Inference 20:15 - 20.30 Mihir Mahajan and Franz Srambical, p(doom) - an Open, Entirely Decentralized, Global AI Research Lab 20.30 - 22.00 Get Together With Food & Drinks

In this session, Andrey Cheptsov, CEO and founder of dstack, will discuss open-source Large Language Models (LLMs) and their practical applications. The session will comprehensively compare open-source LLMs and proprietary options, such as OpenAI, and discuss the economic aspects of hosting these models.

This webinar will cover: – A comparative analysis of open-source LLMs and proprietary large language models. – An exploration into the economic aspects of hosting open-source LLMs. Giving you an understanding of the associated costs and benefits. – How to evaluate and benchmark LLM models based on performance.

In this session, Andrey Cheptsov, CEO and founder of dstack, will discuss open-source Large Language Models (LLMs) and their practical applications. The session will comprehensively compare open-source LLMs and proprietary options, such as OpenAI, and discuss the economic aspects of hosting these models.

This webinar will cover: – A comparative analysis of open-source LLMs and proprietary large language models. – An exploration into the economic aspects of hosting open-source LLMs. Giving you an understanding of the associated costs and benefits. – How to evaluate and benchmark LLM models based on performance.

In this session, Andrey Cheptsov, CEO and founder of dstack, will discuss open-source Large Language Models (LLMs) and their practical applications. The session will comprehensively compare open-source LLMs and proprietary options, such as OpenAI, and discuss the economic aspects of hosting these models.

This webinar will cover: – A comparative analysis of open-source LLMs and proprietary large language models. – An exploration into the economic aspects of hosting open-source LLMs. Giving you an understanding of the associated costs and benefits. – How to evaluate and benchmark LLM models based on performance.

Welcome to the wonderful October of Pydata Berlin meetup !!

We would like to welcome you all starting from 18:45. The talks begin around 19.15.

Please provide your first and last name for the registration because this is required for the venue's entry policy. If you cannot attend, please cancel your spot so others are able to join as the space is limited.

************************************************************************** The Lineup for the Evening

Talk 1: Leveraging open-source LLMs for production

Abstract: This talk examines using open-source LLMs for real-world purposes. It compares the benefits and drawbacks of open-source LLMs to proprietary options like OpenAI. The discussion covers the economics of hosting open-source LLMs, highlights serving frameworks, explores cloud GPU availability, and gives an overview of key open-source LLMs.

Speaker: Andrey Cheptsov is the founder of dstack, an open-source cloud-agnostic framework for training and deployment of generative AI models. Prior to this, Andrey worked at JetBrains with the PyCharm team.

Talk 2: Tutorial on Diffusion Models

Abstract: Diffusion models are the backbone of modern generative imaging frameworks, such as stable diffusion, midjourney or imagen. In this tutorial we will derive and implement the diffusion model algorithm from scratch. We will pay special attention to the relation between the discrete time and continuous time formulation (score-based generative models) of Diffusion Models.

Speaker: Jakiw Pidstrigach is doing his PhD in mathematics at the university of Potsdam. His broad interests are the interplay between stochastic analysis and sampling or generative modeling.

There will be slots for 2-3 Lightning Talks (5-6 Minutes for each). Kindly let us know if you would like to present something at the start of the meetup :)

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