talk-data.com

In this talk, Hugo Bowne-Anderson, an independent data and AI consultant, educator, and host of the podcasts Vanishing Gradients and High Signal, shares his journey from academic research and curriculum design at DataCamp to advising teams at Netflix, Meta, and the US Air Force. Together, we explore how to build reliable, production-ready AI systems—from prompt evaluation and dataset design to embedding agents into everyday workflows.

You’ll learn about: How to structure teams and incentives for successful AI adoptionPractical prompting techniques for accurate timestamp and data generationBuilding and maintaining evaluation sets to avoid “prompt overfitting”- Cost-effective methods for LLM evaluation and monitoringTools and frameworks for debugging and observing AI behavior (Logfire, Braintrust, Phoenix Arise)The evolution of AI agents—from simple RAG systems to proactive, embedded assistantsHow to escape “proof of concept purgatory” and prioritize AI projects that drive business valueStep-by-step guidance for building reliable, evaluable AI agents This session is ideal for AI engineers, data scientists, ML product managers, and startup founders looking to move beyond experimentation into robust, scalable AI systems. Whether you’re optimizing RAG pipelines, evaluating prompts, or embedding AI into products, this talk offers actionable frameworks to guide you from concept to production.

LINKS Escaping POC Purgatory: Evaluation-Driven Development for AI Systems - https://www.oreilly.com/radar/escaping-poc-purgatory-evaluation-driven-development-for-ai-systems/Stop Building AI Agents - https://www.decodingai.com/p/stop-building-ai-agentsHow to Evaluate LLM Apps Before You Launch - https://www.youtube.com/watch?si=90fXJJQThSwGCaYv&v=TTr7zPLoTJI&feature=youtu.beMy Vanishing Gradients Substack - https://hugobowne.substack.com/Building LLM Applications for Data Scientists and Software Engineers https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=datatalksclub TIMECODES: 00:00 Introduction and Expertise 04:04 Transition to Freelance Consulting and Advising 08:49 Restructuring Teams and Incentivizing AI Adoption 12:22 Improving Prompting for Timestamp Generation 17:38 Evaluation Sets and Failure Analysis for Reliable Software 23:00 Evaluating Prompts: The Cost and Size of Gold Test Sets 27:38 Software Tools for Evaluation and Monitoring 33:14 Evolution of AI Tools: Proactivity and Embedded Agents 40:12 The Future of AI is Not Just Chat 44:38 Avoiding Proof of Concept Purgatory: Prioritizing RAG for Business Value 50:19 RAG vs. Agents: Complexity and Power Trade-Offs 56:21 Recommended Steps for Building Agents 59:57 Defining Memory in Multi-Turn Conversations

Connect with Hugo Twitter - https://x.com/hugobowneLinkedin - https://www.linkedin.com/in/hugo-bowne-anderson-045939a5/Github - https://github.com/hugobowneWebsite - https://hugobowne.github.io/ Connect with DataTalks.Club: Join the community - https://datatalks.club/slack.htmlSubscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQCheck other upcoming events - https://lu.ma/dtc-eventsGitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/

​A practical guide for data scientists and engineers - Hugo Bowne-Anderson

​As AI moves from experimentation to real-world impact, the challenges are no longer just technical. They’re about design, evaluation, and collaboration. In this episode, Hugo will share his perspective on how teams and individuals can build AI responsibly, work effectively across disciplines, and keep learning as the field continues to change.

​He’ll cover:

• ​When (and when not) to build AI agents
• ​Using AI for coding vs. building software with LLMs
• ​The AI software development lifecycle and escaping “PoC purgatory”
• ​What happens to data science in the age of AI

​About the Speaker

​Hugo Bowne-Anderson is an independent data and AI consultant with extensive experience in the tech industry. He has advised and taught teams building AI-powered systems, including engineers from Netflix, Meta, and the U.S. Air Force. He is the host of Vanishing Gradients and High Signal, podcasts exploring developments in data science and AI.

​Previously, Hugo served as Head of Developer Relations at Outerbounds and held roles at Coiled and DataCamp, where his work in data science education reached over 6 million learners. He has taught at Yale University, Cold Spring Harbor Laboratory, and conferences like SciPy and PyCon, and is a passionate advocate for democratizing data skills and open-source tools. He also regularly teaches courses on Building LLM Applications for Data Scientists and Software Engineers.

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

Hugo Bowne-Anderson and I chat about the future of AI and automation, agents, and much more.

A Fireside Chat with Hugo Bowne-Anderson and Alex Filipchik (Head of Infrastructure, Cloud Kitchens) on how machine learning (ML) and AI are evolving from niche specializations into essential engineering disciplines. Topics include engineering ML and AI at scale, the shift from specialist roles to core engineering, practical infrastructure decisions, generative AI use cases, simplifying ML adoption for engineers, and the future of data and ML engineering.

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

Description: ​Join Hugo Bowne-Anderson and Alex Filipchik (Head of Infrastructure, Cloud Kitchens) for a Fireside Chat on how machine learning (ML) and AI are evolving from niche specializations into essential engineering disciplines. Alex will share his experience in transforming Cloud Kitchens' data and ML infrastructure to empower engineers, support real-time and batch processing, and ensure seamless deployment of data-driven applications.

​Key Topics of Discussion:

• Engineering ML and AI at Scale: An exploration of how Cloud Kitchens designs and manages data and ML systems, making them accessible to engineers while handling the complexities of large-scale integration.
• The Shift from Specialist Roles to Core Engineering: Why ML and AI are increasingly seen as part of the broader software engineering toolkit, and how this shift is reshaping teams and skill sets.
• ​Practical Infrastructure Decisions: Insights into the build vs. buy dilemma and how Cloud Kitchens navigates decisions around custom solutions versus leveraging external platforms. *
• Generative AI Use Cases: A look at how Cloud Kitchens applies generative AI to improve operations, automate tasks, and enhance productivity, along with the challenges and learnings from these implementations.
• ​Simplifying ML Adoption for Engineers: Discussion on lowering the barriers for engineers to adopt ML tools and practices, from intuitive tooling to better integration with existing workflows. *
• The Future of Data and ML Engineering: Alex’s thoughts on the next decade, including trends, emerging best practices, and how companies can prepare for the continuous evolution of data-powered engineering.

​This session is perfect for data engineers, software engineers, and AI/ML practitioners who are interested in making ML and AI an integral part of the software development lifecycle.

--------------------------- 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: register on event website to receive joining link. (RSVP on meetup will NOT have joining link)

Description: Join us for a fireside chat with Hailey Schoelkopf, a leading researcher at EleutherAI, where we'll explore the evolving landscape of AI research and the critical role of open-source tools in advancing the field. Hosted by Hugo Bowne-Anderson, this discussion will cover the latest developments and challenges in AI, from the evaluation harness to the impact of research infrastructure.

​Hailey Schoelkopf is deeply involved in research on Large Language Models (LLMs) at EleutherAI, with a focus on critical evaluation, efficient distributed training, and interpreting the inner workings of AI/ML systems. As a key contributor to widely used open-source projects like the LM Evaluation Harness and GPT-NeoX, Hailey plays a significant role in advancing open-source AI. Her work at EleutherAI includes publishing research in top academic venues, contributing to the open-source community, and managing collaborative research projects.

​Key topics of discussion:

• ​EleutherAI’s Journey: From a grassroots community to a leading nonprofit in AI research.
• ​​Open-Source vs. Open-Weight: Understanding the nuances and implications for AI development.
• ​Evaluation Harness: The role of evaluation tools in AI research and industry adoption.
• ​​Multilingual and Multimodal AI: Challenges and progress in creating AI models that serve diverse communities.
• ​Industry vs. Academia in AI Research: Exploring the shifting landscape of AI innovation.

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

**RSVP instructions: register on event website to receive joining link. (RSVP on meetup will NOT have joining link)

Description: Join us for a fireside chat with Santiago Valdarrama, a machine learning engineer, educator, and freelancer, renowned for his hands-on, pragmatic approach to AI and ML. Hugo Bowne-Anderson will host this Outerbounds event, diving into the real-world challenges and opportunities of implementing machine learning at scale. ​ Santiago, creator of a highly acclaimed end-to-end machine learning course, is dedicated to equipping engineers with the practical skills needed to excel in real-world ML environments. His expertise in simplifying complex concepts and preparing students for real-world challenges offers invaluable insights for ML practitioners at all levels.

​Key topics of discussion: ​- Full Machine Learning Lifecycle: How to master the entire process from data collection to deployment and monitoring. - ​ML in Production: Overcoming common pitfalls in deploying machine learning models. - AI/ML Evolution: What sets modern AI approaches apart from traditional ML methods? - ​Freelancing in ML: What does it take to succeed as a freelancer in the machine learning space? - ​Future ML Skills: Which competencies will be critical for ML engineers in the AI-driven future?

​This conversation aims to bridge the gap between academic knowledge and industry application, offering actionable insights on implementing machine learning solutions.

​This fireside chat is relevant for students, practitioners, and leaders in the ML space, providing actionable insights and a realistic perspective on the current and future state of machine learning engineering.

**RSVP instructions: register on event website to receive joining link. (RSVP on meetup will NOT have joining link)

Description: Join us for a fireside chat with Santiago Valdarrama, a machine learning engineer, educator, and freelancer, renowned for his hands-on, pragmatic approach to AI and ML. Hugo Bowne-Anderson will host this Outerbounds event, diving into the real-world challenges and opportunities of implementing machine learning at scale. ​ Santiago, creator of a highly acclaimed end-to-end machine learning course, is dedicated to equipping engineers with the practical skills needed to excel in real-world ML environments. His expertise in simplifying complex concepts and preparing students for real-world challenges offers invaluable insights for ML practitioners at all levels.

​Key topics of discussion: ​- Full Machine Learning Lifecycle: How to master the entire process from data collection to deployment and monitoring. - ​ML in Production: Overcoming common pitfalls in deploying machine learning models. - AI/ML Evolution: What sets modern AI approaches apart from traditional ML methods? - ​Freelancing in ML: What does it take to succeed as a freelancer in the machine learning space? - ​Future ML Skills: Which competencies will be critical for ML engineers in the AI-driven future?

​This conversation aims to bridge the gap between academic knowledge and industry application, offering actionable insights on implementing machine learning solutions.

​This fireside chat is relevant for students, practitioners, and leaders in the ML space, providing actionable insights and a realistic perspective on the current and future state of machine learning engineering.

**RSVP instructions: register on event website to receive joining link. (RSVP on meetup will NOT have joining link)

Description: Chip Huyen is a writer and computer scientist currently at Voltron Data, working on GPU-native data processing and open data standards (Ibis, Apache Arrow, Substrait). Previously, she built machine learning tools at NVIDIA, Snorkel AI, and Netflix. ​ In this fireside chat, Chip joins Hugo Bowne-Anderson to explore the unique challenges and opportunities in productionizing foundation models compared to traditional machine learning approaches. As AI systems become increasingly advanced and open-ended, ML engineers must adapt their strategies and techniques to ensure reliable, efficient, and scalable deployments. ​Key topics of discussion will include:

• ​From closed-ended to open-ended evaluation: Developing robust evaluation methodologies for foundation models, which can generate novel outputs and exhibit emergent behaviors;
• ​From feature engineering to context construction: Techniques for effectively prompting and guiding foundation models to perform desired tasks across diverse domains;
• Adapting to unstructured data: Strategies for processing and integrating the vast amounts of unstructured data required to train and operate foundation models;
• Infrastructure and tooling challenges: Scaling compute resources, optimizing workflows, and building reliable pipelines for foundation model deployment;
• The evolving role of the ML engineer: New skills, collaborations, and best practices for succeeding in the era of foundation models and AI-driven products.

​Join us for a conversation on the forefront of AI engineering, and discover strategies for navigating the shift to foundation models in your organization.

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

**RSVP instructions: register on event website to receive joining link. (RSVP on meetup will NOT have joining link)

Description: Chip Huyen is a writer and computer scientist currently at Voltron Data, working on GPU-native data processing and open data standards (Ibis, Apache Arrow, Substrait). Previously, she built machine learning tools at NVIDIA, Snorkel AI, and Netflix. ​ In this fireside chat, Chip joins Hugo Bowne-Anderson to explore the unique challenges and opportunities in productionizing foundation models compared to traditional machine learning approaches. As AI systems become increasingly advanced and open-ended, ML engineers must adapt their strategies and techniques to ensure reliable, efficient, and scalable deployments. ​Key topics of discussion will include:

• ​From closed-ended to open-ended evaluation: Developing robust evaluation methodologies for foundation models, which can generate novel outputs and exhibit emergent behaviors;
• ​From feature engineering to context construction: Techniques for effectively prompting and guiding foundation models to perform desired tasks across diverse domains;
• Adapting to unstructured data: Strategies for processing and integrating the vast amounts of unstructured data required to train and operate foundation models;
• Infrastructure and tooling challenges: Scaling compute resources, optimizing workflows, and building reliable pipelines for foundation model deployment;
• The evolving role of the ML engineer: New skills, collaborations, and best practices for succeeding in the era of foundation models and AI-driven products.

​Join us for a conversation on the forefront of AI engineering, and discover strategies for navigating the shift to foundation models in your organization.

Sponsors: We are actively seeking sponsors to support our community. Whether it is by offering venue spaces, providing food, or cash sponsorship. Sponsors will not only speak at our meetups, receive prominent recognition as sponsors, 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 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.

** Important RSVP here. (Due to room capacity, it's required to pre-register at the link for admission).

Along with our friends at Explosion and Rasa, we are hosting a pre-party for PyCon DE where the teams behind Metaflow, SpaCy, and Rasa will discuss their journey so far, what’s new, and where these projects are headed in 2024! ​ Ines Montani (spaCy, Explosion) will talk about "Incorporating LLMs into practical NLP workflows", giving us a behind the scenes look at her PyCon DE talk "The AI Revolution Will Not Be Monopolized: How open-source beats economies of scale, even for LLMs";

​Tanja Bunk (Rasa) will speak about "CALM Down, It's Just a Paradigm Shift: Moving from Intent-Based NLU to LLM-Powered Dialogues"; ​ Hugo Bowne-Anderson (Metaflow, Outerbounds) will talk about "Building GenAI and ML systems with OSS Metaflow." ​ More detailed agenda to come!

* The event is hosted by AICamp partner: Outerbounds.

Join us for the upcoming PyData Amsterdam meetup that we host in collaboration with Adyen.

Schedule

18.00-19.00: Walk in with drinks and food (🍕 /🍺) 19.00-19.45: Fraud or no Fraud: sounds simple, right? 19.45-20:00: short break 20.00-20:45: Building GenAI and ML systems with OSS Metaflow 20.45-21.30: Networking + drinks and bites

[Talk 1]: Fraud or no Fraud: sounds simple\, right? by Sophie van den Berg The surge in online payments has brought a surge in fraudsters looking to exploit the system. To combat this, we're leveraging machine learning (ML) models to identify and block fraudulent transactions. While this may seem like a straightforward supervised learning task, there's a key challenge: how do we confirm if a blocked transaction was truly fraudulent? This talk delves into counterfactual evaluation and other obstacles encountered when building an ML model for fraud detection at Adyen.

[Talk 2]: Building GenAI and ML systems with OSS Metaflow by Hugo Bowne-Anderson This talk explores a framework for how data scientists can deliver value with Generative AI: How can you embed LLMs and foundation models into your pre-existing software stack? How can you do so using Open Source Python? What changes about the production machine learning stack and what remains the same?

We motivate the concepts through generative AI examples in domains such as text-to-image (Stable Diffusion) and text-to-speech (Whisper) applications. Moreover, we’ll demonstrate how workflow orchestration provides a common scaffolding to ensure that your Generative AI and classical Machine Learning workflows alike are robust and ready to move safely into production systems.

This talk is aimed squarely at (data) scientists and ML engineers who want to focus on the science, data, and modeling, but want to be able to access all their infrastructural, platform, and software needs with ease!