talk-data.com

** RSVP: Register here to receive joining link before the deadline. There are also two other sessions, you need to register each one:

• May 28th: AI Workshop (Virtual): Full-Stack ML with Metaflow
• May 30th: AI Workshop (Virtual): LLMs, RAG, and Fine-Tuning

Description: The live session is at 9AM CEST. If you can not make the live event, still register to receive the recording.

​​Wolf Vollprecht has spent the last five years trying to make sure that the conda ecosystem can grow unbounded! He worked at Quantstack (C++ numerical computing with xtensor, Jupyter, and mamba) before starting his own venture (prefix.dev) where they are trying to take package management to the next level!

​In this fireside chat, Wolf joins Hugo Bowne-Anderson, Outerbounds’ Head of Developer Relations, to talk about package management and software supply chain challenges for data scientists and machine learning engineers, the magic of making it all "just work" for developers across stacks and platforms, and the future of package management and accessibility for GenAI and foundation models.

They’ll discuss: - ​​The main challenges developers\, data scientists\, and ML/AI engineers face in shipping software; - ​​How his team is working to improve the conda ecosystem by rewriting everything in Rust; - ​​What the developer experience needs to be like and how we can make package management “just work”; - ​The future of efficient and user-friendly tools for distributing and using LLMs and other foundation models. - And much more.

Community on Slack/Discord

• Event chat: chat and connect with speakers and attendees
• Sharing blogs, events, job openings, projects collaborations
• Join Slack/Discord (link is at the bottom of the page)

Speakers: If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: https://forms.gle/JkMt91CZRtoJBSFUA

Sponsors: We are actively seeking sponsors to support our community. Whether it is by offering venue spaces, providing food/drink, or cash sponsorship. Sponsors will not only speak at the meetups, receive prominent recognition, but also gain exposure to our extensive membership base of 350K+ AI developers worldwide.

** RSVP: Register here to receive joining link before the deadline. There are also two other sessions, you need to register each one:

• May 28th: AI Workshop (Virtual): Full-Stack ML with Metaflow
• May 30th: AI Workshop (Virtual): LLMs, RAG, and Fine-Tuning

Description: The live session is at 9AM CEST. If you can not make the live event, still register to receive the recording.

​​Wolf Vollprecht has spent the last five years trying to make sure that the conda ecosystem can grow unbounded! He worked at Quantstack (C++ numerical computing with xtensor, Jupyter, and mamba) before starting his own venture (prefix.dev) where they are trying to take package management to the next level!

​In this fireside chat, Wolf joins Hugo Bowne-Anderson, Outerbounds’ Head of Developer Relations, to talk about package management and software supply chain challenges for data scientists and machine learning engineers, the magic of making it all "just work" for developers across stacks and platforms, and the future of package management and accessibility for GenAI and foundation models.

They’ll discuss: - ​​The main challenges developers\, data scientists\, and ML/AI engineers face in shipping software; - ​​How his team is working to improve the conda ecosystem by rewriting everything in Rust; - ​​What the developer experience needs to be like and how we can make package management “just work”; - ​The future of efficient and user-friendly tools for distributing and using LLMs and other foundation models. - And much more.

Community on Slack/Discord

• Event chat: chat and connect with speakers and attendees
• Sharing blogs, events, job openings, projects collaborations
• Join Slack/Discord (link is at the bottom of the page)

Speakers: If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: https://forms.gle/JkMt91CZRtoJBSFUA

Sponsors: We are actively seeking sponsors to support our community. Whether it is by offering venue spaces, providing food/drink, or cash sponsorship. Sponsors will not only speak at the meetups, receive prominent recognition, but also gain exposure to our extensive membership base of 350K+ AI developers worldwide.

** RSVP: Register here to receive joining link before the deadline. There are also two other sessions, you need to register each one:

• May 28th: AI Workshop (Virtual): Full-Stack ML with Metaflow
• May 30th: AI Workshop (Virtual): LLMs, RAG, and Fine-Tuning

Description: The live session is at 9AM CEST. If you can not make the live event, still register to receive the recording.

​​Wolf Vollprecht has spent the last five years trying to make sure that the conda ecosystem can grow unbounded! He worked at Quantstack (C++ numerical computing with xtensor, Jupyter, and mamba) before starting his own venture (prefix.dev) where they are trying to take package management to the next level!

​In this fireside chat, Wolf joins Hugo Bowne-Anderson, Outerbounds’ Head of Developer Relations, to talk about package management and software supply chain challenges for data scientists and machine learning engineers, the magic of making it all "just work" for developers across stacks and platforms, and the future of package management and accessibility for GenAI and foundation models.

They’ll discuss: - ​​The main challenges developers\, data scientists\, and ML/AI engineers face in shipping software; - ​​How his team is working to improve the conda ecosystem by rewriting everything in Rust; - ​​What the developer experience needs to be like and how we can make package management “just work”; - ​The future of efficient and user-friendly tools for distributing and using LLMs and other foundation models. - And much more.

Community on Slack/Discord

• Event chat: chat and connect with speakers and attendees
• Sharing blogs, events, job openings, projects collaborations
• Join Slack/Discord (link is at the bottom of the page)

Speakers: If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: https://forms.gle/JkMt91CZRtoJBSFUA

Sponsors: We are actively seeking sponsors to support our community. Whether it is by offering venue spaces, providing food/drink, or cash sponsorship. Sponsors will not only speak at the meetups, receive prominent recognition, but also gain exposure to our extensive membership base of 350K+ AI developers worldwide.

** RSVP: Register here to receive joining link before the deadline. There are also two other sessions, you need to register each one:

• May 28th: AI Workshop (Virtual): Full-Stack ML with Metaflow
• May 30th: AI Workshop (Virtual): LLMs, RAG, and Fine-Tuning

Description: The live session is at 9AM CEST. If you can not make the live event, still register to receive the recording.

​​Wolf Vollprecht has spent the last five years trying to make sure that the conda ecosystem can grow unbounded! He worked at Quantstack (C++ numerical computing with xtensor, Jupyter, and mamba) before starting his own venture (prefix.dev) where they are trying to take package management to the next level!

​In this fireside chat, Wolf joins Hugo Bowne-Anderson, Outerbounds’ Head of Developer Relations, to talk about package management and software supply chain challenges for data scientists and machine learning engineers, the magic of making it all "just work" for developers across stacks and platforms, and the future of package management and accessibility for GenAI and foundation models.

They’ll discuss: - ​​The main challenges developers\, data scientists\, and ML/AI engineers face in shipping software; - ​​How his team is working to improve the conda ecosystem by rewriting everything in Rust; - ​​What the developer experience needs to be like and how we can make package management “just work”; - ​The future of efficient and user-friendly tools for distributing and using LLMs and other foundation models. - And much more.

Community on Slack/Discord

• Event chat: chat and connect with speakers and attendees
• Sharing blogs, events, job openings, projects collaborations
• Join Slack/Discord (link is at the bottom of the page)

Speakers: If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: https://forms.gle/JkMt91CZRtoJBSFUA

Sponsors: We are actively seeking sponsors to support our community. Whether it is by offering venue spaces, providing food/drink, or cash sponsorship. Sponsors will not only speak at the meetups, receive prominent recognition, but also gain exposure to our extensive membership base of 350K+ AI developers worldwide.

** RSVP: Register here to receive joining link before the deadline. There are also two other sessions, you need to register each one:

• May 29th: AI Seminar (Virtual): Packaging Code for ML and GenAI
• May 30th: AI Workshop (Virtual): LLMs, RAG, and Fine-Tuning

Description: This workshop is for data scientists and machine learning engineers who want to learn how to move machine learning projects from prototypes and experiments to production as a repeatable process. If you want to build production-grade, SLA-satisfying software systems in Python code, including core MLOps building blocks (such as versioning, experiment tracking, workflows, and so on), then this course is for you.

​This workshop will teach you all the ingredients of full-stack machine learning and the infrastructure you’ll need to use to deploy your ML and AI models to production. It will involve continuous, hands-on coding using our Metaflow sandbox, a browser-based VSCode environment with the ML infrastructure to power Metaflow provisioned for you at the click of a button.

​You’ll learn how to:

• - ​​Orchestrate machine learning workflows;
• - ​​​Use versioning\, model reporting\, and notebooks to inspect your workflows and models;
• - ​​​Leverage cloud compute resources to scale entire workflows and single steps;
• - ​​Deploy workflows and models to production systems and;
• - ​Configure A/B tests to establish iterative AI development cycles;
• - And much more.

​Warnings: This course is not a typical data science workshop in the sense that:

• - This is an engineering-focused course\, not an ML course. You will get to build actual functional systems\, not learn about how to train models or run cross-validation. That said\, the course is intended to be accessible to non-engineers: we want to make sure that all (data) scientists can build real-world DS\, ML\, and AI software systems;
• ​- Outerbounds provides managed infrastructure for the course, including data storage in S3, Kubernetes as a computing platform, workflow scheduling and event-triggering with Argo, and more - all accessed through Python files and notebooks in a familiar IDE.

Community on Slack/Discord

• Event chat: chat and connect with speakers and attendees
• Sharing blogs, events, job openings, projects collaborations
• Join Slack/Discord (link is at the bottom of the page)

Speakers: If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: https://forms.gle/JkMt91CZRtoJBSFUA

Sponsors: We are actively seeking sponsors to support our community. Whether it is by offering venue spaces, providing food/drink, or cash sponsorship. Sponsors will not only speak at the meetups, receive prominent recognition, but also gain exposure to our extensive membership base of 350K+ AI developers worldwide.

** RSVP: Register here to receive joining link before the deadline. There are also two other sessions, you need to register each one:

• May 29th: AI Seminar (Virtual): Packaging Code for ML and GenAI
• May 30th: AI Workshop (Virtual): LLMs, RAG, and Fine-Tuning

Description: This workshop is for data scientists and machine learning engineers who want to learn how to move machine learning projects from prototypes and experiments to production as a repeatable process. If you want to build production-grade, SLA-satisfying software systems in Python code, including core MLOps building blocks (such as versioning, experiment tracking, workflows, and so on), then this course is for you.

​This workshop will teach you all the ingredients of full-stack machine learning and the infrastructure you’ll need to use to deploy your ML and AI models to production. It will involve continuous, hands-on coding using our Metaflow sandbox, a browser-based VSCode environment with the ML infrastructure to power Metaflow provisioned for you at the click of a button.

​You’ll learn how to:

• - ​​Orchestrate machine learning workflows;
• - ​​​Use versioning\, model reporting\, and notebooks to inspect your workflows and models;
• - ​​​Leverage cloud compute resources to scale entire workflows and single steps;
• - ​​Deploy workflows and models to production systems and;
• - ​Configure A/B tests to establish iterative AI development cycles;
• - And much more.

​Warnings: This course is not a typical data science workshop in the sense that:

• - This is an engineering-focused course\, not an ML course. You will get to build actual functional systems\, not learn about how to train models or run cross-validation. That said\, the course is intended to be accessible to non-engineers: we want to make sure that all (data) scientists can build real-world DS\, ML\, and AI software systems;
• ​- Outerbounds provides managed infrastructure for the course, including data storage in S3, Kubernetes as a computing platform, workflow scheduling and event-triggering with Argo, and more - all accessed through Python files and notebooks in a familiar IDE.

Community on Slack/Discord

• Event chat: chat and connect with speakers and attendees
• Sharing blogs, events, job openings, projects collaborations
• Join Slack/Discord (link is at the bottom of the page)

Speakers: If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: https://forms.gle/JkMt91CZRtoJBSFUA

Sponsors: We are actively seeking sponsors to support our community. Whether it is by offering venue spaces, providing food/drink, or cash sponsorship. Sponsors will not only speak at the meetups, receive prominent recognition, but also gain exposure to our extensive membership base of 350K+ AI developers worldwide.

** RSVP: Register here to receive joining link before the deadline. There are also two other sessions, you need to register each one:

• May 29th: AI Seminar (Virtual): Packaging Code for ML and GenAI
• May 30th: AI Workshop (Virtual): LLMs, RAG, and Fine-Tuning

Description: This workshop is for data scientists and machine learning engineers who want to learn how to move machine learning projects from prototypes and experiments to production as a repeatable process. If you want to build production-grade, SLA-satisfying software systems in Python code, including core MLOps building blocks (such as versioning, experiment tracking, workflows, and so on), then this course is for you.

​This workshop will teach you all the ingredients of full-stack machine learning and the infrastructure you’ll need to use to deploy your ML and AI models to production. It will involve continuous, hands-on coding using our Metaflow sandbox, a browser-based VSCode environment with the ML infrastructure to power Metaflow provisioned for you at the click of a button.

​You’ll learn how to:

• - ​​Orchestrate machine learning workflows;
• - ​​​Use versioning\, model reporting\, and notebooks to inspect your workflows and models;
• - ​​​Leverage cloud compute resources to scale entire workflows and single steps;
• - ​​Deploy workflows and models to production systems and;
• - ​Configure A/B tests to establish iterative AI development cycles;
• - And much more.

​Warnings: This course is not a typical data science workshop in the sense that:

• - This is an engineering-focused course\, not an ML course. You will get to build actual functional systems\, not learn about how to train models or run cross-validation. That said\, the course is intended to be accessible to non-engineers: we want to make sure that all (data) scientists can build real-world DS\, ML\, and AI software systems;
• ​- Outerbounds provides managed infrastructure for the course, including data storage in S3, Kubernetes as a computing platform, workflow scheduling and event-triggering with Argo, and more - all accessed through Python files and notebooks in a familiar IDE.

Community on Slack/Discord

• Event chat: chat and connect with speakers and attendees
• Sharing blogs, events, job openings, projects collaborations
• Join Slack/Discord (link is at the bottom of the page)

Speakers: If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: https://forms.gle/JkMt91CZRtoJBSFUA

Sponsors: We are actively seeking sponsors to support our community. Whether it is by offering venue spaces, providing food/drink, or cash sponsorship. Sponsors will not only speak at the meetups, receive prominent recognition, but also gain exposure to our extensive membership base of 350K+ AI developers worldwide.

** RSVP: Register here to receive joining link before the deadline. There are also two other sessions, you need to register each one:

• May 29th: AI Seminar (Virtual): Packaging Code for ML and GenAI
• May 30th: AI Workshop (Virtual): LLMs, RAG, and Fine-Tuning

Description: This workshop is for data scientists and machine learning engineers who want to learn how to move machine learning projects from prototypes and experiments to production as a repeatable process. If you want to build production-grade, SLA-satisfying software systems in Python code, including core MLOps building blocks (such as versioning, experiment tracking, workflows, and so on), then this course is for you.

​This workshop will teach you all the ingredients of full-stack machine learning and the infrastructure you’ll need to use to deploy your ML and AI models to production. It will involve continuous, hands-on coding using our Metaflow sandbox, a browser-based VSCode environment with the ML infrastructure to power Metaflow provisioned for you at the click of a button.

​You’ll learn how to:

• - ​​Orchestrate machine learning workflows;
• - ​​​Use versioning\, model reporting\, and notebooks to inspect your workflows and models;
• - ​​​Leverage cloud compute resources to scale entire workflows and single steps;
• - ​​Deploy workflows and models to production systems and;
• - ​Configure A/B tests to establish iterative AI development cycles;
• - And much more.

​Warnings: This course is not a typical data science workshop in the sense that:

• - This is an engineering-focused course\, not an ML course. You will get to build actual functional systems\, not learn about how to train models or run cross-validation. That said\, the course is intended to be accessible to non-engineers: we want to make sure that all (data) scientists can build real-world DS\, ML\, and AI software systems;
• ​- Outerbounds provides managed infrastructure for the course, including data storage in S3, Kubernetes as a computing platform, workflow scheduling and event-triggering with Argo, and more - all accessed through Python files and notebooks in a familiar IDE.

Community on Slack/Discord

• Event chat: chat and connect with speakers and attendees
• Sharing blogs, events, job openings, projects collaborations
• Join Slack/Discord (link is at the bottom of the page)

Speakers: If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: https://forms.gle/JkMt91CZRtoJBSFUA

Sponsors: We are actively seeking sponsors to support our community. Whether it is by offering venue spaces, providing food/drink, or cash sponsorship. Sponsors will not only speak at the meetups, receive prominent recognition, but also gain exposure to our extensive membership base of 350K+ AI developers worldwide.