Agentic Conversations (formally mlops.community)

MLOps Coffee Sessions #87 with Kyle Morris, Don't Listen Unless You Are Going to Do ML in Production.

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// Abstract
Companies wanting to leverage ML specialize in model quality (architecture, training method, dataset), but face the same set of undifferentiated work they need to productionize the model. They must find machines to deploy their model on, set it up behind an API, make the inferences fast, cheap, and reliable by optimizing hardware, load-balancing, autoscaling, clustering launches per region, queueing long-running tasks... standardizing docs, billing, logging, CI/CD that integrates testing, and more.

Banana.dev's aim is to simplify this process for all. This talk outlines our learnings and the trials and tribulations of ML hosting.

// Bio
Hey all! Kyle did self-driving AI @ Cruise, robotics @ CMU, currently in business @ Harvard. Now he's building banana.dev to accelerate ML! Kyle cares about safely building superhuman AI. Our generation has the chance to build tools that advance society 100x more in our lifetime than in all of history, but it needs to benefit all living things! This requires a lot of technical + social work. Let's go!

// MLOps Jobs board  
jobs.mlops.community

// Related Links
kyle.af

--------------- ✌️Connect With Us ✌️ -------------
Join our Slack community: https://go.mlops.community/slack
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Catch all episodes, blogs, newsletter, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Adam on LinkedIn: https://www.linkedin.com/in/aesroka/
Connect with Kyle on LinkedIn: https://www.linkedin.com/in/kylejohnmorris/

Timestamps:
[00:00] Introduction to Kyle Morris
[02:42] banana.dev  
[04:43] banana.dev's vision
[06:22] banana.dev's goal beyond the competition
[07:28] Computer vision optimization
[08:46] Common pitfalls
[11:47] Machine Learning Engineering vs Software Engineering
[13:47] Who do you hire?
[15:12] Disconnect in operationalizing
[18:53] Meeting SLOs if stuff is breaking upstream
[19:48] Is breaking upstream a part of quality?
[21:16] Scenario of what to focus on
[24:02] Advice to people dealing with unrealistic expectations
[28:11] Hard truth
[30:42] Don't Listen Unless You Are Going to Do ML in Production
[33:15] Hurdle in productionizing ML systems
[37:56] Chaos engineering
[42:40] War stories
[45:54] Catalyst on changing the original post on Kyle's blog
[50:11] Wrap up
[51:02] Message banana.dev or Kyle if you have questions regarding production. It's free of charge!

MLOps Coffee Sessions #86 with Julien Bisconti, Building ML/Data Platform on Top of Kubernetes.  

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// Abstract
When building a platform, a good start would be to define the goals and features of that platform, knowing it will evolve. Kubernetes is established as the de facto standard for scalable platforms, but it is not a fully-fledged data platform.  

Do ML engineers have to learn and use Kubernetes directly?  

They probably shouldn't. So it is up to the data engineering team to provide the tools and abstractions necessary to allow ML engineers to do their work.  
The time, effort, and knowledge it takes to build a data platform is already quite an achievement. When it is built, one has to maintain it, monitor it, train people for on-call rotation, implement escalation policies and disaster recovery, optimize for usage and costs, secure it, and build a whole ecosystem of tools around it (front-end, CLI, dashboards).  

That cost might be too high and time-consuming for some companies to consider building their own ML platform as opposed to cloud offering alternatives. Note that cloud offerings still require some of those points, but most of the work is already done.

// Bio
Julien is a software engineer turned Site Reliability Engineer. He is a Google developer expert, certified Data Engineer on Google Cloud, and Kubernetes Administrator, mentor for Women Developer Academy and Google For Startups program. He is working on building and maintaining a data/ML platform.

// Related Links
https://portal.superwise.ai/
Crossing the River by Feeling the Stones • Simon Wardley • GOTO 2018: https://www.youtube.com/watch?v=2IW9L1uNMCs

--------------- ✌️Connect With Us ✌️ -------------
Join our Slack community: https://go.mlops.community/slack
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Sign up for the next meetup: https://go.mlops.community/register
Catch all episodes, blogs, newsletter, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Julien on LinkedIn: https://www.linkedin.com/in/julienbisconti/

Timestamps:
[00:00] French intro by Julien
[00:32] Introduction to Julien Bisconti
[03:35] Arriving at the non-technical side process of MLOps
[06:06] Envious of people with technological problems
[07:27] People problem bandwidth conversation
[11:04] Atomic decision making
[14:20] Advice to developers: either buy or build in their career potential
[18:23] Jobs board - https://mlops.pallet.xyz/jobs
[21:28] Chaos engineering
[26:33] Role of chaos engineering in building production machine learning systems
[32:59] Core challenge of MLOps
[37:04] Standardization on an industry level
[40:30] Reconciliation of trade-offs using Vertex and Sagemaker
[45:21] Crossing the River by Feeling the Stones talk by Simon Wardley  
[47:22] Wrap up

MLOps Coffee Sessions #85 with Emmanuel Ameisen, Continuous Deployment of Critical ML Applications.

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// Abstract
Finding an ML model that solves a business problem can feel like winning the lottery, but it can also be a curse. Once a model is embedded at the core of an application and used by real users, the real work begins. That's when you need to make sure that it works for everyone, that it keeps working every day, and that it can improve as time goes on. Just like building a model is all about data work, keeping a model alive and healthy is all about developing operational excellence.

First, you need to monitor your model and its predictions and detect when it is not performing as expected for some types of users. Then, you'll have to devise ways to detect drift and how quickly your models get stale. Once you know how your model is doing and can detect when it isn't performing, you have to find ways to fix the specific issues you identify. Last but definitely not least, you will now be faced with the task of deploying a new model to replace the old one, without disrupting the day of all the users that depend on it.
A lot of the topics covered are active areas of work around the industry and haven't been formalized yet, but they are crucial to making sure your ML work actually delivers value. While there aren't any textbook answers, there is no shortage of lessons to learn.

// Bio
Emmanuel Ameisen has worked for years as a Data Scientist and ML Engineer. He is currently an ML Engineer at Stripe, where he works on helping improve model iteration velocity. Previously, he led Insight Data Science's AI program, where he oversaw more than a hundred machine learning projects. Before that, he implemented and deployed predictive analytics and machine learning solutions for Local Motion and Zipcar. Emmanuel holds graduate degrees in artificial intelligence, computer engineering, and management from three of France’s top schools.

// Related Links
https://www.amazon.com/Building-Machine-Learning-Powered-Applications/dp/149204511X

https://www.oreilly.com/library/view/building-machine-learning/9781492045106/

--------------- ✌️Connect With Us ✌️ -------------
Join our Slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://go.mlops.community/register
Catch all episodes, blogs, newsletter, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Adam on LinkedIn: https://www.linkedin.com/in/aesroka/
Connect with Emmanuel on LinkedIn: https://www.linkedin.com/in/ameisen/

Timestamps:
[00:00] Introduction to Emmanuel Ameisen
[03:38] Building Machine Learning Powered Applications book inspiration
[05:19] The writing process
[07:04] Over-engineering NLP
[09:13] CV-driven development: intentional or natural
[11:09] Attribute to the machine learning team
[14:44] Shortening the iteration cycle
[16:41] Advice on how to tackle iteration
[20:00] Failure modes
[21:02] Infrastructure Iteration at Stripe
[27:06] Deployment Steps tests challenges
[29:34] "You develop operational excellence by exercising it." - Emmanuel Ameisen
[33:22] Death of a thousand cuts: Balance of work vs productionization piece balance
[36:15] Reproducibility headaches [40:04] Pipelines as software product
[41:25] Get the book by Emmanuel Ameisen!
[42:04] Takeaways and wrap up

MLOps Coffee Sessions #84 with Ernest Chan, Lessons from Studying FAANG ML Systems.

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// Abstract
Large tech companies invest in ML platforms to accelerate their ML efforts. Become better prepared to solve your own MLOps problems by learning from their technology and design decisions.
Tune in to learn about ML platform components, capabilities, and design considerations.

// Bio
Ernest is a Data Scientist at Duo Security. As part of the core team that built Duo's first ML-powered product, Duo Trust Monitor, he faced many (frustrating) MLOps problems first-hand. That led him to advocate for an ML infrastructure team to make it easier to deliver ML products at Duo. Prior to Duo, Ernest worked at an EdTech company, building data science products for higher-ed. Ernest is passionate about MLOps and using ML for social good.

// Related Links  
Lessons on ML Platforms — from Netflix, DoorDash, Spotify, and more: https://ernestklchan.medium.com/lessons-on-ml-platforms-from-netflix-doordash-spotify-and-more-f455400115c7    
Paper Highlights-Challenges in Deploying Machine Learning: a Survey of Case Studies https://towardsdatascience.com/paper-highlights-challenges-in-deploying-machine-learning-a-survey-of-case-studies-cafe61cfd04c    
Choose boring technologies Slideshare by Dan McKinley: https://www.slideshare.net/danmckinley/choose-boring-technology

--------------- ✌️Connect With Us ✌️ -------------
Join our Slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://go.mlops.community/register
Catch all episodes, blogs, newsletter, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Ernest on LinkedIn: https://www.linkedin.com/in/ernest-chan-68245773/

Timestamps:
[00:00] Introduction to Ernest Chan
[01:07] Takeaways
[02:58] Ernest's Lessons on ML Platforms — from Netflix, DoorDash, Spotify, and more blog post
[05:55] Five components of an ML Platform  
[10:09] Limitations highlighted in the blog post
[14:41] Level of maturity or completion observed in company efforts
[16:17] Platform/Architecture admired the most
[17:46] Advice to big tech companies
[22:03] Process of needing an infrastructure and aiming towards having a platform
[24:23] Paper Highlights-Challenges in Deploying Machine Learning: a Survey of Case Studies blog post
[26:24] Takeaways from Paper Highlights-Challenges in Deploying Machine Learning
[30:33] Prioritization
[33:04] Delta Lake
[35:27] Model rollouts and shadow mode
[39:23] Are you an ML Engineer or a Data Scientist?  
[40:15] Simple route platform vs flexible platform trade-offs
[41:08] Opinionated and simple vs less opinionated and flexible
[43:22] Choose boring technologies Slideshare by Dan McKinley
[44:36] Wrap up

MLOps Coffee Sessions #83 with Vincent Warmerdam, Better Use Cases for Text Embeddings.

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// Abstract
Text embeddings are very popular, but there are plenty of reasons to be concerned about their applications. There's algorithmic fairness, compute requirements, as well as issues with the datasets that they're typically trained on.
In this session, Vincent gives an overview of some of these properties while also talking about an underappreciated use-case for the embeddings: labeling!

// Bio
Vincent D. Warmerdam is a senior data professional who has worked as an engineer, researcher, team lead, and educator in the past. He's especially interested in understanding algorithmic systems so that one can prevent failure. As such, he has a preference for simpler solutions that scale, as opposed to the latest and greatest from the hype cycle. He currently works as a Research Advocate at Rasa, where he collaborates with the research team to explain and understand conversational systems better.

Outside of Rasa, Vincent is also well known for his open-source projects (scikit-lego, human-learn, doubtlab, and more), collaborations with open source projects like spaCy, his blog over at koaning.io, and his calm code educational project.

--------------- ✌️Connect With Us ✌️ -------------
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Sign up for the next meetup: https://go.mlops.community/register
Catch all episodes, blogs, newsletter, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Skylar on LinkedIn: https://www.linkedin.com/in/skylar-payne-766a1988/
Connect with Vincent on LinkedIn: https://www.linkedin.com/in/vincentwarmerdam/

Timestamps:

[00:00] Takeaways

[04:10] Favorite purchases this pandemic

[05:05] What drives Vincent to understand how ML can fail?

[08:33] How and why to make systems simpler?

[11:37] Techniques shared by Vincent in his talks

[15:51] ML as a UI problem

[17:02] Figuring out rules in your data

[20:01] Detecting bad labels

[23:53] Labeling isn't necessarily easy

[25:48] Fraud use case

[27:42] How does Vincent stay sane looking for frauds?

[29:12] How does Vincent produce so many packages?

[31:23] Vincent's favorite package

[33:24] Explosion AI

[36:14] Python all the way

[37:44] Shift from model-centric to data-centric AI

[39:35] Talking about the problem is necessary

[40:40] Vincent's war stories

[44:04] Adding constraints to the system

[47:49] Wrap up

MLOps Reading Group meeting on February 11, 2022  

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Reading Group Session about Feature Stores with Matt Delacour and Mike Moran  

--------------- ✌️Connect With Us ✌️ -------------
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Timestamps:
[00:05] Matt's intro
[00:26] Mike's intro
[01:09] Matt’s talk: Feature store system at Shopify
[01:45] What is Shopify?
[02:05] Shopify Use Case
[02:38] Choosing a solution
[03:19] Managed service vs In-house vs Open-source (Feast)
[06:01] Why did we choose Feast?
[11:25] Implementation Strategy (multi-repo vs mono-repo approaches)
[13:01] Mono-repo approach breakdown
[14:30] Internal SDK
[17:01] Q&A: Does Feast satisfy scalability for online inference of Shopify's latency requirements?
[19:05] Q&A: Do you rely on Feast to serialize data to the online store?
[20:13] Q&A: Is your mono-repo library a subset of Feast?
[21:18] Q&A: Did you consider using git submodules for a multi-repo?
[23:02] Q&A: Are you storing embeddings with Feast?
[24:30] Q&A: Regarding the mono-repo, which modules are responsible for feature engineering? How do you guarantee that different feature engineering can be used across many DS?
[27:58] Mike’s talk (Feature store at Skyscanner)
[28:08] Kaleidoscope System
[28:25] Background and context of the Feature store
[29:30] Initial state of the feature store
[30:13] How does the marketing team also leverage the feature store
[31:04] Current state of the feature store (marketing & machine learning)
[31:44] SDK approach of creating schemas with dataframes (easy access)
[32:16] Reusability across teams among the marketing and DS team
[33:06] GDPR constraints
[33:34] Data updates at the feature store
[36:09] Q&A: When a DS updates a feature, how are you communicating that across teams?
[38:25] Q&A: Are you applying different levels of feature engineering to increase the likelihood of a DS going back to a previous checkpoint of processing?
[40:55] Q&A: In what languages are you implementing the feature store?
[44:28] Q&A: Regarding performance-wise, how do you decide what code remains in Apache Spark vs SQL?
[49:00] Wrap-up

MLOps Community Meetup #93! Two weeks ago, we talked to Chad Sanderson, Trustworthy Data for Machine Learning.

Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

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// Abstract
The most common challenge for ML teams operating at scale is data quality.
In this talk, Chad discusses how Convoy invested in a large-scale data quality effort to treat data as an API and provide a data change management surface to enable trustworthy machine learning.

// Bio
Chad Sanderson is the Product Lead for Convoy's Data Platform team, which includes the data warehouse, streaming, BI & visualization, experimentation, machine learning, and data discovery.

Chad has built everything from feature stores, experimentation platforms, metrics layers, streaming platforms, analytics tools, data discovery systems, and workflow development platforms. He’s implemented open source, SaaS products (early and late-stage) and has built cutting-edge technology from the ground up. Chad loves the data space, and if you're interested in chatting about it with him, don't hesitate to reach out.

// Related links   

----------- ✌️Connect With Us ✌️-------------   
Join our Slack community:  https://go.mlops.community/slack
Follow us on Twitter:  @mlopscommunity
Sign up for the next meetup:  https://go.mlops.community/register
Catch all episodes, Feature Store, Machine Learning Monitoring, and Blogs: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Chad on LinkedIn: https://www.linkedin.com/in/chad-sanderson/

Timestamps:

[00:00] Introduction to Chad Sanderson

[00:30] Chad's journey to Convoy

[04:25] Evolution of Convoy's platform

[10:33] Definition and measurement data quality of KPI's

[13:36] COVID-19 effect on the distribution data supply chain

[17:15] Justifying investments in ML

[20:00] Examples of data Convoy deals with and models they build

[20:50] Examples of techniques Convoy uses to maintain quality data

[21:00] Concept of a Data Contract

[21:53] Enterprise Data Model

[25:13] Feature store and reuse or use by the business

[28:32] Impact of COVID-19 on the data quality process

[31:54] Software engineers' reactions to the implementation of ideas

[33:21] Other value props

[37:54] Point of a framework to step back from full automation

[41:26] War stories

[45:49] Metrics layer

[50:17] Convoy is hiring!!!

Reach out to Chad at https://www.linkedin.com/in/chad-sanderson/ or [email protected]

MLOps Coffee Sessions #82 with Donna Schut and Christos Aniftos, Practitioners' Guide to MLOps.

Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

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// Abstract
The "Practitioners Guide to MLOps" introduced excellent frameworks for how to think about the field. Can we talk about how you've seen the advice in that guide applied to real-world systems? Is there additional advice you'd add to that paper based on what you've seen since its publication and with new tools being introduced?
Your article about selecting the right capabilities has a lot of great advice. It would be fun to walk through a hypothetical company case and talk about how to apply that advice in a real-world setting.

GCP has had a lot of new offerings lately, including Vertex AI. It would be great to talk through what's new and what's coming down the line. Our audience always loves hearing how tool providers like GCP think about the problems customers face and how tools are correspondingly developed.

// Bio
Donna Schut
Donna is a Solutions Manager at Google Cloud, responsible for designing, building, and bringing to market smart analytics and AI solutions globally. She is passionate about pushing the boundaries of our thinking with new technologies and creating solutions that have a positive impact. Previously, she was a Technical Account Manager, overseeing the delivery of large-scale ML projects, and part of the AI Practice, developing tools, processes, and solutions for successful ML adoption. She managed and co-authored Google Cloud’s AI Adoption Framework and Practitioners' Guide to MLOps.

Christos Aniftos
Christos is a machine learning engineer with a focus on the end-to-end ML ecosystem. On a typical day, Christos helps Google customers productionize their ML workloads using Google Cloud products and services with special attention to scalable and maintainable ML environments.

Christos made his ML debut in 2010 while working at DigitalMR, where he led a team of data scientists and developers to build a social media monitoring & analytics tool for the Market Research sector.

// Related links:  
Select the Right MLOps Capabilities for Your ML Use Case
https://cloud.google.com/blog/products/ai-machine-learning/select-the-right-mlops-capabilities-for-your-ml-use-case
Practitioner's Guide to MLOps white paper
https://services.google.com/fh/files/misc/practitioners_guide_to_mlops_whitepaper.pdf

--------------- ✌️Connect With Us ✌️ -------------
Join our Slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://go.mlops.community/register
Catch all episodes, blogs, newsletter, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Donna on LinkedIn: https://www.linkedin.com/in/donna-schut/
Connect with Christos on LinkedIn: https://www.linkedin.com/in/aniftos/

Timestamps:
[00:00] Introduction to Donna Schut and Christos Aniftos
[05:52] Inspiration of Practitioner's Guide to MLOps paper
[06:57] Model for working with customers
[08:14] Where are we at MLOps?
[10:20] Working with customers
[11:30] Practitioner's Guide to MLOps paper
[16:16] Training maturity levels
[22:37] Context about the discovery process
[25:21] Disciplines and security
[26:12] Is there a level up in maturity?
[29:50] Successes or failures that stand out
[38:00] War stories  
[43:16] Wrap up

MLOps Coffee Sessions #81 with Davis Treybig and Leigh Marie Braswell, Machine Learning from the Viewpoint of Investors.

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// Abstract
Machine learning is a rapidly evolving space that can be hard to keep track of. Every year, thousands of research papers are published in the space, and hundreds of new companies are built both in applied machine learning as well as in machine learning tooling.

In this podcast, we interview two investors who focus heavily on machine learning to get their take on the state of the machine learning industry today: Leigh-Marie Braswell at Founders Fund and Davis Treybig at Innovation Endeavors. We discuss their perspectives on opportunities within MLOps and applied machine learning, common pitfalls and challenges seen in machine learning startups, and new projects they find exciting and interesting in the space.

// Bio
Davis Treybig
Davis (email: [email protected]) is currently a principal on the investment team at Innovation Endeavors, an early-stage venture firm focused on highly technical companies. He primarily focuses on software infrastructure, especially data tooling and security. Prior to Innovation Endeavors, Davis was a product manager at Google, where he worked on the Pixel phone and the developer platform for the Google Assistant. Davis studied computer science and electrical engineering in college.

Leigh Marie Braswell
Leigh Marie (Twitter: @LM_Braswell) is an investor at Founders Fund. Before joining Founders Fund, she was an early engineer & the first product manager at Scale AI, where she originally built & later led product development for the LiDAR/3D annotation products, used by many autonomous vehicles, robots, and AR/VR companies as a core step in their machine learning lifecycles. She has also done software development at Blend, machine learning at Google, and quantitative trading at Jane Street.

--------------- ✌️Connect With Us ✌️ -------------
Join our Slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://go.mlops.community/register
Catch all episodes, blogs, newsletter, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Leigh on LinkedIn: https://www.linkedin.com/in/leigh-marie-braswell/
Connect with Davis on LinkedIn: https://www.linkedin.com/in/davistreybig/

Timestamps:

[00:00] Introduction to Leigh Marie Braswell and Davis Treybig

[03:23] Where are we now in MLOps?

[05:50] Ripe for consolidation

[13:08] Real pain to solve

[18:20] Modern data stack vs modern ML stack

[25:25] Strong use cases for ML with a huge sea of long-tail

[28:43] A funny meme with a Huggingface

[32:23] Looking at open-source as an investment

[36:44] Tips and tricks to rally a team and a vision for Startups

[43:55] What surprised you over the last year?

[47:16] Societal norms and acceptance of things

[47:55] Where to get in touch with Leigh Marie and Davis: Leigh Marie - Twitter: @LM_Braswell

Davis - email: [email protected]

MLOps Coffee Sessions #80 with Ale Solano, The Journey from Data Scientist to MLOps Engineer.

Join the Community: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTJoinIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

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// Abstract
After years of failed POCs, then all of a sudden, one of our models is accepted and will be used in production. The next morning, we are part of the main scrum stand-up meeting, and a DevOps guy is assisting us. A strange feeling, unknown to us until then, starts growing on the AI team: we are useful!

Deploying models to production is challenging, but MLOps is more than that. MLOps is about making an AI team useful and iterative from the beginning. And it requires a role that takes care of the technical challenges that this implies, given the experimental nature of the ML field, while also serving the product and business needs. If your AI team does not include this role, maybe it's your time to step up and do it yourself! Today, we will chat with Ale about the transition from being a data scientist to a self-called MLOps engineer. And yes, you'll need to study computer science.

// Bio
Ale was born and raised in a mid-sized town near Malaga in southern Spain. Ale did his bachelor's degree in robotics because it sounded cool, and then he got into machine learning because it was even cooler.
Ale worked in two companies as an ML developer. Now he's on a temporary hiatus to study business and computer science and get a motivation boost.

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Adam on LinkedIn: https://www.linkedin.com/in/aesroka/
Connect with Ale on LinkedIn: https://www.linkedin.com/in/alesolano/

Timestamps:

[00:00] Brief Introduction to Adam Sroka

[00:47] Takeaways

[04:27] Support the community!

[05:37] Introduction to Ale Solano

[06:52] How Ale Solano got into MLOps

[09:16] Getting aboard the ML train

[10:51] Robotics to Computer Science

[14:54] Early MLOps headaches

[16:54] SPRINT by Jake Knapp

[17:58] Starting to implement MLOps

[19:44] Major adjustment

[21:34] Biggest wins

[22:49] Importance of CICD

[24:50] Major Stakeholders of Ale's ML team

[26:55] The dream the community must have

[30:33] Recognizing the foundational pieces and the evolution

[33:13] What is Ale excited about, and what is missing

[34:36] Different fields of expertise

[36:49] Ale's take on "80% of the models don't make it to production"

[39:15] Wrap up