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Important: RSVP here to receive joining link. (rsvp on meetup will NOT receive joining link).

Description: Join us for an in-depth discussion on scaling AI workflows with NVIDIA NIM, in collaboration with NVIDIA.

Ville Tuulos, CEO and co-founder of Outerbounds, and Michael Balint, Director of Product Architecture at NVIDIA, will explore how NVIDIA’s NIM microservices can streamline the deployment of large language models (LLMs) and other generative AI applications in production environments. We’ll dive into the unique value propositions of NIM compared to other solutions, explore challenges in scaling LLMs for enterprise use, and discuss key benefits such as enhanced fine-tuning and long-term model stability.

This is a must-attend event for ML professionals interested in cutting-edge enterprise solutions for LLMs and generative AI workflows.

Speakers/Topics: Stay tuned as we are updating speakers and schedules. If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: Submit Topics

Sponsors: We are actively seeking sponsors to support AI developers community. Whether it is by offering venue spaces, providing food, or cash sponsorship. Sponsors will not only have the chance to speak at the meetups, receive prominent recognition, but also gain exposure to our extensive membership base of 400K+ AI developers worldwide.

AICamp Community on Slack/Discord - Event chat: chat and connect with speakers and attendees - Sharing blogs\, events\, job openings\, projects collaborations

Important: RSVP here to receive joining link. (rsvp on meetup will NOT receive joining link).

Description: Join us for an in-depth discussion on scaling AI workflows with NVIDIA NIM, in collaboration with NVIDIA.

Ville Tuulos, CEO and co-founder of Outerbounds, and Michael Balint, Director of Product Architecture at NVIDIA, will explore how NVIDIA’s NIM microservices can streamline the deployment of large language models (LLMs) and other generative AI applications in production environments. We’ll dive into the unique value propositions of NIM compared to other solutions, explore challenges in scaling LLMs for enterprise use, and discuss key benefits such as enhanced fine-tuning and long-term model stability.

This is a must-attend event for ML professionals interested in cutting-edge enterprise solutions for LLMs and generative AI workflows.

Speakers/Topics: Stay tuned as we are updating speakers and schedules. If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: Submit Topics

Sponsors: We are actively seeking sponsors to support AI developers community. Whether it is by offering venue spaces, providing food, or cash sponsorship. Sponsors will not only have the chance to speak at the meetups, receive prominent recognition, but also gain exposure to our extensive membership base of 400K+ AI developers worldwide.

AICamp Community on Slack/Discord - Event chat: chat and connect with speakers and attendees - Sharing blogs\, events\, job openings\, projects collaborations

Simplify data science infrastructure to give data scientists an efficient path from prototype to production. In Effective Data Science Infrastructure you will learn how to: Design data science infrastructure that boosts productivity Handle compute and orchestration in the cloud Deploy machine learning to production Monitor and manage performance and results Combine cloud-based tools into a cohesive data science environment Develop reproducible data science projects using Metaflow, Conda, and Docker Architect complex applications for multiple teams and large datasets Customize and grow data science infrastructure Effective Data Science Infrastructure: How to make data scientists more productive is a hands-on guide to assembling infrastructure for data science and machine learning applications. It reveals the processes used at Netflix and other data-driven companies to manage their cutting edge data infrastructure. In it, you’ll master scalable techniques for data storage, computation, experiment tracking, and orchestration that are relevant to companies of all shapes and sizes. You’ll learn how you can make data scientists more productive with your existing cloud infrastructure, a stack of open source software, and idiomatic Python. The author is donating proceeds from this book to charities that support women and underrepresented groups in data science. About the Technology Growing data science projects from prototype to production requires reliable infrastructure. Using the powerful new techniques and tooling in this book, you can stand up an infrastructure stack that will scale with any organization, from startups to the largest enterprises. About the Book Effective Data Science Infrastructure teaches you to build data pipelines and project workflows that will supercharge data scientists and their projects. Based on state-of-the-art tools and concepts that power data operations of Netflix, this book introduces a customizable cloud-based approach to model development and MLOps that you can easily adapt to your company’s specific needs. As you roll out these practical processes, your teams will produce better and faster results when applying data science and machine learning to a wide array of business problems. What's Inside Handle compute and orchestration in the cloud Combine cloud-based tools into a cohesive data science environment Develop reproducible data science projects using Metaflow, AWS, and the Python data ecosystem Architect complex applications that require large datasets and models, and a team of data scientists About the Reader For infrastructure engineers and engineering-minded data scientists who are familiar with Python. About the Author At Netflix, Ville Tuulos designed and built Metaflow, a full-stack framework for data science. Currently, he is the CEO of a startup focusing on data science infrastructure. Quotes By reading and referring to this book, I’m confident you will learn how to make your machine learning operations much more efficient and productive. - From the Foreword by Travis Oliphant, Author of NumPy, Founder of Anaconda, PyData, and NumFOCUS Effective Data Science Infrastructure is a brilliant book. It’s a must-have for every data science team. - Ninoslav Cerkez, Logit More data science. Less headaches. - Dr. Abel Alejandro Coronado Iruegas, National Institute of Statistics and Geography of Mexico Indispensable. A copy should be on every data engineer’s bookshelf. - Matthew Copple, Grand River Analytics