FastMCP with Adam Azzam and Jeremiah Lowin
The Model Context Protocol, or MCP, gives developers a common way to expose tools, data, and capabilities to large language models, and it has quickly become an important standard in agentic AI. FastMCP is an open source project stewarded by the team at Prefect, which is an orchestration platform for AI and data workflows. The FastMCP project builds on MCP to provide high-level, ergonomic abstractions for Python developers to rapidly build and deploy MCP servers and applications.
Jeremiah Lowin is the founder and CEO of Prefect, and Adam Azzam is the VP of Product at the company. In this episode, Jeremiah and Adam join Gregor Vand to discuss the origin story of FastMCP, the three pillars of the framework, the architectural decisions behind FastMCP 3.0, and much more.
Gregor Vand is a security-focused technologist, having previously been a CTO across cybersecurity, cyber insurance and general software engineering companies. He is based in Singapore and can be found via his profile at vand.hk or on LinkedIn.
Please click here to see the transcript of this episode.
Sponsorship inquiries: sponsor@softwareengineeringdaily.com
The post FastMCP with Adam Azzam and Jeremiah Lowin appeared first on Software Engineering Daily.
Jeremiah Lowin: Explaining the New AI Paradigm - [Invest Like the Best, EP.307]
My guest this week is Jeremiah Lowin. Jeremiah has been on the podcast a number of times over the years. He’s one of my oldest friends who has been a sounding board for me throughout my career. Today he is the founder and CEO of Prefect, which helps companies automate and orchestrate their dataflows. In full disclosure, Positive Sum is an investor in Prefect. We didn’t plan this conversation, but when OpenAI released ChatGPT, I called Jeremiah for a primer on what’s happening under the hood and how best to contextualize this product amidst the growing AI movement. We have these conversations often, but this time I decided to record it so we can all learn from someone I consider to be a leading mind in the fields of data science and machine learning. We start off in the weeds and zoom out as the discussion unfolds. Please enjoy this conversation with my friend, Jeremiah Lowin.
Listen to Founders podcast
Founders Episode #136 A Success Story: Estee Lauder
Invest Like the Best with David Senra: Passion & Pain
For the full show notes, transcript, and links to mentioned content, check out the episode page here.
-----
This episode is brought to you by Tegus. Tegus streamlines the investment research process so you can get up to speed and find answers to critical questions on companies faster and more efficiently. The Tegus platform surfaces the hard-to-get qualitative insights, gives instant access to critical public financial data through BamSEC, and helps you set up customized expert calls. It’s all done on a single, modern SaaS platform that offers 360-degree insight into any public or private company. As a listener, you can take Tegus for a free test drive by visiting tegus.co/patrick.
-----
Today's episode is brought to you by Brex. Brex is the integrated financial platform trusted by the world's most innovative entrepreneurs and fastest-growing companies. With Brex, you can move money fast for instant impact with high-limit corporate cards, payments, venture debt, and spend management software all in one place. Ready to accelerate your business? Learn more at brex.com/best.
-----
Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes.
Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here.
Follow us on Twitter: @patrick_oshag | @JoinColossus
Show Notes
[00:03:38] - [First question] - What a pre-trained transformer is
[00:06:12] - What latent representation means in the context of AI models
[00:09:57] - Models using math to interpret input data and generate images accurately
[00:11:43] - Whether or not understanding AI complexity in light of the results they arrive at will become a black box scenario
[00:14:13] - A high level history of the companies involved in generative AI
[00:17:51] - The precursory technology that makes generative AI art possible
[00:21:01] - What people are doing to improve AI models in between versions
[00:26:39] - Things that are literally happening during AI training
[00:33:38] - Whether or not AI models might one day function as a utility like electricity
[00:36:01] - Coding using GitHub Copilot and what it’s felt like to use it
[00:40:30] - How he’d approach starting an AI company from scratch
[00:44:40] - Developing this technology beyond general and into specific use cases
[00:49:44] - The secret sauce for defensibility in the AI model space
[00:53:02] - What he’s watching more closely as the story unfolds
[00:56:32] - Whether or not he thinks that these toolkits will eventually learn how to use other systems like Unreal Engine on our behalf
Chetan Puttagunta and Jeremiah Lowin – Open Source Crash Course - [Invest Like the Best, EP.188]
My guests this week are Jeremiah Lowin and Chetan Puttagunta. Jeremiah is the founder of Prefect.io, an open-source software company where my family and I are investors, and Chetan is a partner at Benchmark Capital. Both are past guests and good friends. I asked them on to help the audience understand the open source software business model. I’ve been fascinated with this model in which companies give a huge chunk of their work and value away for free to a community of developers, and then make money by building additional tools, functionality, and services on top of their free and open platform. While this may strike you as a wonky discussion on a niche software topic, I think it is valuable for everyone because the ideas can be applied to more than just code. I view much of my own activity as open-sourcing investment research and knowledge. It is also important because much of the world’s technology is built on top of open source projects. I hope you learn something new about this emerging category. Please enjoy.
This episode of Invest Like The Best is sponsored by Canalyst. Canalyst is the leading destination for public company data and analysis.
If you’re a professional equity investor and haven’t talked to Canalyst recently, you should give them a shout. Learn more and try Canalyst for yourself at canalyst.com/Patrick.
For more episodes go to InvestorFieldGuide.com/podcast.
Sign up for the book club and new email newsletter called “Inside the Episode” at InvestorFieldGuide.com/bookclub.
Follow Patrick on Twitter at @patrick_oshag
Show Notes
(2:40) – (First question) – Originator business in open source software; Redhat
(5:51) – Why open source is valuable in building a business
(7:40) – Examples of the benefits of open source projects
(10:27) – Open source business models that produce the best results
(17:04) – Defensibility of open source companies
(25:02) – Mentoring younger founders on using open-source
(30:54) – The benefits of launching open-source
(36:41) – Building a digital community
(41:31) – Lessons from Open Source that can be applied to other businesses
(50:04) – The opportunity sets available in the open source space
(53:33) – Future of open source
(56:31) – Tobi Lutke Podcast Episode
Learn More
For more episodes go to InvestorFieldGuide.com/podcast.
Sign up for the book club and new email newsletter called “Inside the Episode” at InvestorFieldGuide.com/bookclub.
Follow Patrick on Twitter at @patrick_oshag
The Workflow Engine For Data Engineers And Data Scientists
Summary
Building a data platform that works equally well for data engineering and data science is a task that requires familiarity with the needs of both roles. Data engineering platforms have a strong focus on stateful execution and tasks that are strictly ordered based on dependency graphs. Data science platforms provide an environment that is conducive to rapid experimentation and iteration, with data flowing directly between stages. Jeremiah Lowin has gained experience in both styles of working, leading him to be frustrated with all of the available tools. In this episode he explains his motivation for creating a new workflow engine that marries the needs of data engineers and data scientists, how it helps to smooth the handoffs between teams working on data projects, and how the design lets you focus on what you care about while it handles the failure cases for you. It is exciting to see a new generation of workflow engine that is learning from the benefits and failures of previous tools for processing your data pipelines.
Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data management
When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management.For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat
Your host is Tobias Macey and today I’m interviewing Jeremiah Lowin about Prefect, a workflow platform for data engineering
Interview
Introduction
How did you get involved in the area of data management?
Can you start by explaining what Prefect is and your motivation for creating it?
What are the axes along which a workflow engine can differentiate itself, and which of those have you focused on for Prefect?
In some of your blog posts and your PyData presentation you discuss the concept of negative vs. positive engineering. Can you briefly outline what you mean by that and the ways that Prefect handles the negative cases for you?
How is Prefect itself implemented and what tools or systems have you relied on most heavily for inspiration?
How do you manage passing data between stages in a pipeline when they are running across distributed nodes?
What was your decision making process when deciding to use Dask as your supported execution engine?
For tasks that require specific resources or dependencies how do you approach the idea of task affinity?
Does Prefect support managing tasks that bridge network boundaries?
What are some of the features or capabilities of Prefect that are misunderstood or overlooked by users which you think should be exercised more often?
What are the limitations of the open source core as compared to the cloud offering that you are building?
What were your assumptions going into this project and how have they been challenged or updated as you dug deeper into the problem domain and received feedback from users?
What are some of the most interesting/innovative/unexpected ways that you have seen Prefect used?
When is Prefect the wrong choice?
In your experience working on Airflow and Prefect, what are some of the common challenges and anti-patterns that arise in data engineering projects?
What are some best practices and industry trends that you are most excited by?
What do you have planned for the future of the Prefect project and company?
Contact Info
LinkedIn
@jlowin on Twitter
Parting Question
From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
Prefect
Airflow
Dask
Podcast Episode
Prefect Blog
PyData Presentation
Tensorflow
Workflow Engine
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Support Data Engineering Podcast
Jeremiah Lowin – Machine Learning in Investing – [Invest Like the Best, EP.105]
My guest this week is one of my best and oldest friends, Jeremiah Lowin. Jeremiah has had a fascinating career, starting with advanced work in statistics before moving into the risk management field in the hedge fund world. Through his career he has studied data, risk, statistics, and machine learning—the last of which is the topic of our conversation today.
He has now left the world of finance to found a company called Prefect, which is a framework for building data infrastructure. Prefect was inspired by observing frictions between data scientists and data engineers, and solves these problems with a functional API for defining and executing data workflows. These problems, while wonky, are ones I can relate to working in quantitative investing—and others that suffer from them out there will be nodding their heads. In full and fair disclosure, both me and my family are investors in Jeremiah’s business.
You won’t have to worry about that potential conflict of interest in today’s conversation, though, because our focus is on the deployment of machine learning technologies in the realm of investing. What I love about talking to Jeremiah is that he is an optimist and a skeptic. He loves working with new statistical learning technologies, but often thinks they are overhyped or entirely unsuited to the tasks they are being used for. We get into some deep detail on how tests are set up, the importance of data, and how the minimization of error is a guiding light in machine learning and perhaps all of human learning, too. Let’s dive in.
For more episodes go to InvestorFieldGuide.com/podcast.
Sign up for the book club, where you’ll get a full investor curriculum and then 3-4 suggestions every month at InvestorFieldGuide.com/bookclub.
Follow Patrick on Twitter at @patrick_oshag
Show Notes
2:06 - (First Question) – What do people need to think about when considering using machine learning tools
3:19 – Types of problems that AI is perfect for
6:09 – Walking through an actual test and understanding the terminology
11:52 – Data in training: training set, test set, validation set
13:55 – The difference between machine learning and classical academic finance modelling
16:09 – What will the future of investing look like using these technologies
19:53 – The concept of stationarity
21:31 – Why you shouldn’t take for granted label formation in tests
24:12 – Ability for a model to shrug
26:13 – Hyper parameter tuning
28:16 – Categories of types of models
30:49 – Idea of a nearest neighbor or K-Means Algorithm
34:48 – Trees as the ultimate utility player in this landscape
38:00 – Features and data sets as the driver of edge in Machine Learning
40:12 – Key considerations when working through time series
42:05 – Pitfalls he has seen when folks try to build predictive market investing models
44:36 – Getting started
46:29 – Looking back at his career, what are some of the frontier vs settled applications of machine learning he has implemented
49:49 – Does intereptability matter in all of this
52:31 – How gradient decent fits into this whole picture
Learn More
For more episodes go to InvestorFieldGuide.com/podcast.
Sign up for the book club, where you’ll get a full investor curriculum and then 3-4 suggestions every month at InvestorFieldGuide.com/bookclub
Follow Patrick on twitter at @patrick_oshag
Jeremiah Lowin – Machine Intelligence and Risk Management - [Invest Like the Best, EP.20]
Jeremiah Lowin is probably the smartest guy I know, and that is saying something. He is an expert in the fields of statistics, artificial intelligence, and risk management—among many other things. He is currently the Director of Risk Management for a private investment firm in the New York area, but has spent years working with machine learning and AI. This conversation is broken up into two parts. In the first part, we explore artificial intelligence, machine learning, and models. Then we shift to what risk means in a portfolio and how it can be managed or at least redistributed (which starts around 40 minutes into the conversation). Please enjoy!
For comprehensive show notes on this episode go to investorfieldguide.com/lowin/
For more episodes go to InvestorFieldGuide.com/podcast.
Sign up for the book club, where you’ll get a full investor curriculum and then 3-4 suggestions every month at InvestorFieldGuide.com/bookclub
Follow Patrick on twitter at @patrick_oshag