Agentic Conversations (formally mlops.community)

MLOps Coffee Sessions #106 with Delina Ivanova, Associate Director, Data of HelloFresh, Building a Culture of Experimentation to Speed Up Data-Driven Value, co-hosted by Vishnu Rachakonda.

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// Abstract
Supply chain/manufacturing is are prime area where the use of data science/analytics/ ML is underdeveloped, and experimentation is required to collect data and enable data-driven solutions.
This talk encourages companies to conduct experiments and collect data over time in order to build accurate/scalable data-driven solutions.

// Bio
Delina has over 10 years of experience across data and analytics, consulting, and strategy with roles spanning financial services, public sector, and CPG industries. She is currently the Associate Director, Data & Insights at HelloFresh Canada, where she leads a full-service data team, including data engineering, data science, business intelligence, and automation. She is also a Data Science and Machine Learning instructor in the professional development programs at the University of Toronto and the University of Waterloo.

// MLOps Jobs board  
jobs.mlops.community

MLOps Swag/Merch
https://mlops-community.myshopify.com/

// Related Links
The Discourses of Epictetus book: https://www.amazon.com/Discourses-Epictetus/dp/1537427180
The Pyramid Principle: Logic in Writing and Thinking book by Barbara Minto:
https://www.amazon.com/Pyramid-Principle-Logic-Writing-Thinking/dp/0273710516

--------------- ✌️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, newsletters, 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 Delina on LinkedIn: https://www.linkedin.com/in/delina-ivanova/

Timestamps:
[00:00] Introduction to Delina Ivanova
[00:35] Takeaways
[03:46] Looking for People to organize local Meetups!
[04:30] Delina's career trajectories and growth in the corporate schema
[10:02] Telling stories with data
[13:23] Tricks for being a translator from the business side to data teams
[15:32] Technical engineering management and Delina's day-to-day role
[20:40] Giving up day-to-day individual contributing work and coding
[23:33] Good leadership for technical work
[31:05] Growing team growing productivity
[32:55] Pressured to grow
[35:23] HelloFresh
[39:39] Challenges of e-commerce, CPG, Logistics, and grocery combined
[41:08] Cultural differences
[46:04] Rapid-fire session
[52:20] Wrap up

MLOps Coffee Sessions #106 with Curtis Northcutt, CEO & Co-Founder of Cleanlab, Cleanlab: Labeled Datasets that Correct Themselves Automatically, co-hosted by Vishnu Rachakonda.

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// Abstract
Pioneered at MIT by 3 Ph.D. Co-Founders, Cleanlab is an open-source/SaaS company building the premier data-centric AI tools workflows for (1) automatically correcting messy data and labels, (2) auto-tracking of dataset quality over time, (3) automatically finding classes to merge and delete, (4) auto ml for data tasks, (5) obtaining and ranking high-quality annotations, and (6) training ML models with messy data.

Most of the prescriptive tasks (finding issues) can be done in one line of code with their open-source product: https://github.com/cleanlab/cleanlab.

// Bio
Curtis Northcutt is the CEO and Co-Founder of Cleanlab, focused on making AI work reliably for people and their messy, real-world data by automatically fixing issues in any ML dataset. Curtis completed his Ph.D. in Computer Science at MIT, receiving the MIT Thesis Award, NSF Fellowship, and the Goldwater Scholarship. Prior to Cleanlab, Curtis worked at AI research groups including Google, Oculus, Amazon, Facebook, Microsoft, and NASA.

// MLOps Jobs board  
jobs.mlops.community

MLOps Swag/Merch
https://mlops-community.myshopify.com/

// Related Links
https://github.com/cleanlab/cleanlab
https://cleanlab.ai/blog/cleanlab-history/
https://labelerrors.com/ https://l7.curtisnorthcutt.com/
https://nips.cc/Conferences/2021/ScheduleMultitrack?event=47102
https://www.youtube.com/watch?v=ieUOv1sQPlw
https://cleanlab.typeform.com/to/NLnU1XZF
Cameo cheating detection system: https://arxiv.org/ftp/arxiv/papers/1508/1508.05699.pdf  
The Cathedral & the Bazaar book: https://www.amazon.com/Cathedral-Bazaar-Musings-Accidental-Revolutionary/dp/0596001088

--------------- ✌️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, newsletters, 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 Curtis on LinkedIn: https://www.linkedin.com/in/cgnorthcutt/

Timestamps:
[00:00] Introduction to Curtis Northcutt
[00:30] Difference between MLOps and Data-Centric AI
[04:04] Realizing the problem of data quality in ML manifests
[05:11] Computer vision problems
[06:54] War story that got Curtis into Data-Centric AI
[13:50] Overview of Curtis' vision
[14:45] PU Learning
[21:25] Consistency Rate and Flipping Rate
[25:25] One line of code
[29:48] Models make mistakes  
[33:09] Cleanlab plays with the environment
[36:30] How ML Engineers should approach the data quality problem
[42:42] Quantum computing
[46:39] Result of confident learning
[52:31] Utility for small data sets
[53:53] Cleanlab's huge success stories
[56:13] Rapid-fire questions
[58:58] Cloudy and mystified space
[1:03:46] Cleanlab is hiring!
[1:05:06] Wrap up

MLOps Coffee Sessions #104 with the creator of Apache Airflow and Apache Superset, Maxime Beauchemin, Future of BI co-hosted by Vishnu Rachakonda.

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// Abstract
Maxime is one of the most influential thought leaders on the future of data engineering and how to drive business value from data with better data infrastructure and tooling. He’s worked in a number of the most cutting-edge data engineering environments. His articles have been read by thousands, and his creations (Superset and Airflow) power billions of market value. It’s no exaggeration to say that Maxime is one of the most essential contributors to the data and machine learning revolution underway.

// Bio
Maxime Beauchemin is the founder and CEO of Preset. Original creator of Apache Superset.  Max has worked at the leading edge of data and analytics his entire career, helping shape the discipline in influential roles at data-dependent companies like Yahoo!, Lyft, Airbnb, Facebook, and Ubisoft.

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MLOps Swag/Merch
https://mlops-community.myshopify.com/

// Related Links
Website: https://www.rungalileo.io/
Trade-Off: Why Some Things Catch On, and Others book by Kevin Maney:
https://www.amazon.com/Trade-Off-Some-Things-Catch-Others/dp/0385525958

--------------- ✌️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, newsletters, 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 Max on LinkedIn: https://www.linkedin.com/in/maximebeauchemin/

Timestamps:
[00:00] Introduction to Maxime Beauchemin
[01:28] Takeaways
[03:42] Paradigm of data warehouse
[06:38] Entity-centric data modeling
[11:33] Metadata for metadata
[14:24] Problem of data organization for a rapidly scaling organization
[18:36] Machine Learning tooling as a subset or of its own
[22:28] Airflow: The unsung hero of the data scientists
[27:15] Analyzing Airflow
[30:44] Disrupting the field
[34:45] Solutions to the ladder problem of empowering exploratory work and mortals' superpowers with data
[38:04] What to watch out for when building for data scientists  
[41:47] Rapid-fire questions
[51:12] Wrap up

MLOps Coffee Sessions #103 with Corey Zumar, MLOps Podcast on Making MLflow co-hosted by Mihail Eric.

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// Abstract
Because MLOps is a broad ecosystem of rapidly evolving tools and techniques, it creates several requirements and challenges for platform developers:

- To serve the needs of many practitioners and organizations, it's important for MLOps platforms to support a variety of tools in the ecosystem. This necessitates extra scrutiny when designing APIs, as well as rigorous testing strategies to ensure compatibility.  

- Extensibility to new tools and frameworks is a must, but it's important not to sacrifice maintainability. MLflow Plugins (https://www.mlflow.org/docs/latest/plugins.html) is a great example of striking this balance.  

- Open source is a great space for MLOps platforms to flourish. MLflow's growth has been heavily aided by: 1. meaningful feedback from a community of ML practitioners with a wide range of use cases and workflows & 2. collaboration with industry experts from a variety of organizations to co-develop APIs that are becoming standards in the MLOps space.

// Bio
Corey Zumar is a software engineer at Databricks, where he’s spent the last four years working on machine learning infrastructure and APIs for the machine learning lifecycle, including model management and production deployment. Corey is an active developer of MLflow. He holds a master’s degree in computer science from UC Berkeley.

// MLOps Jobs board  
jobs.mlops.community

//MLOps Swag/Merch
https://mlops-community.myshopify.com/

// 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, blogs, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Mihail on LinkedIn: https://www.linkedin.com/in/mihaileric/
Connect with Corey on LinkedIn: https://www.linkedin.com/in/corey-zumar/

Timestamps:
[00:00] Origin story of MLFlow
[02:12] Spark as a big player
[03:12] Key insights
[04:42] Core abstractions and principles of MLFlow's success
[07:08] Product development with open-source
[09:29] Fine line between competing principles
[11:53] Shameless way to pursue collaboration
[12:24] Right go-to-market open-source
[16:27] Vanity metrics
[18:57] First gate of MLOps drug
[22:11] Project fundamentals
[24:29] Through the pillars
[26:14] Best in breed or one tool to rule them all
[29:16] MLOps space is mature with the MLOps tool
[30:49] Ultimate vision for MLFlow
[33:56] Alignment of end-users and business values
[38:11] Adding a project abstraction separate from the current ML project
[42:03] Implementing bigger bets in certain directions
[44:54] Log in features to the experiment page
[45:46] Challenge when operationalizing MLFlow in their stack
[48:34] What would you work on if it weren't MLFlow?
[49:52] Something to put on top of MLFlow
[51:42] Proxy metric
[52:39] Feature Stores and MLFlow
[54:33] Lightning round

[57:36] Wrap up

MLOps Coffee Sessions #102 with Yash Sheth, Fixing Your ML Data Blindspots, co-hosted by Adam Sroka.  

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

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// Abstract
Improving your dataset quality is absolutely critical for effective ML. Finding errors in your datasets is generally a slow, iterative, and painstaking process.    
Data scientists should be proactively fixing their models’ blind spots by improving their training data. In this talk, Yash discusses how Galileo helps data scientists identify, fix, and track data across the entire ML workflow.  

// Bio
Co-founder and VP of Engineering. Prior to starting Galileo, Yash spent the last decade working on Automatic Speech Recognition (ASR) at Google, leading their core speech recognition platform team, which powers speech-to-text across 20+ products at Google in over 80 languages, along with thousands of businesses through their Cloud Speech API.  

// MLOps Jobs board  
jobs.mlops.community

MLOps Swag/Merch
https://mlops-community.myshopify.com/

// Related Links
Website: https://www.rungalileo.io/
Trade-Off: Why Some Things Catch On, and Others book by Kevin Maney:
https://www.amazon.com/Trade-Off-Some-Things-Catch-Others/dp/0385525958

--------------- ✌️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, newsletters, 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 Yash on LinkedIn: https://www.linkedin.com/in/yash-sheth-72111216/

Timestamps:
[00:00] Introduction to Yash Sheth
[02:53] Takeaways
[04:35] Why unstructured data?
[06:59] Fitting in the workflow
[10:56] Digging into the different pains
[18:23] Vision around the democratization of machine learning
[24:31] Unstructured data problem
[25:49] Galileo handling unified tools
[27:21] Calculus for ML
[28:45] Gatekeep
[29:49] Synthetic data in the unstructured data world of Galileo
[33:10] Tips for data scientists who have unstructured data but a small data set
[35:00] Benefits of users from Galileo
[37:15] Business case for dummies
[42:36] War stories
[44:49] Rapid-fire questions
[50:55] Wrap up

MLOps Coffee Sessions #101 with Piero Molino, Declarative Machine Learning Systems: Big Tech Level ML Without a Big Tech Team, co-hosted by Vishnu Rachakonda.

// Abstract
Declarative Machine Learning Systems are the next step in the evolution of Machine Learning infrastructure.

With such systems, organizations can marry the flexibility of low-level APIs with the simplicity of AutoML.
Companies adopting such systems can increase the speed of machine learning development, reaching the quality and scalability that only big tech companies could achieve until now, without the need for a team of several thousand people.

Predibase is the turnkey solution for adopting declarative ML systems at an enterprise scale.

// Bio
Piero Molino is CEO and co-founder of Predibase, a company redefining ML tooling. Most recently, he has been a Staff Research Scientist at Stanford University, working on Machine Learning systems and algorithms in Prof. Chris Ré's Hazy group. Piero completed a Ph.D. in Question Answering at the University of Bari, Italy. Founded QuestionCube, a startup that built a framework for semantic search and QA. Worked for Yahoo Labs in Barcelona on learning to rank, IBM Watson in New York on natural language processing with deep learning, and then joined Geometric Intelligence, where he worked on grounded language understanding.

After Uber acquired Geometric Intelligence, Piero became one of the founding members of Uber AI Labs. At Uber, he worked on research topics including Dialogue Systems, Language Generation, Graph Representation Learning, Computer Vision, Reinforcement Learning, and Meta-Learning. He also worked on several deployed systems like COTA, an ML and NLP model for Customer Support, Dialogue Systems for driver's hands-free dispatch, the Uber Eats Recommender System with graph learning and collusion detection. He is the author of Ludwig, a Linux-Foundation-backed open source declarative deep learning framework.

// MLOps Jobs board  
jobs.mlops.community

// MLOps Swag/Merch
https://mlops-community.myshopify.com/

// Related Links
Website: http://w4nderlu.st
http://ludwig.ai https://medium.com/ludwig-ai
Declarative Machine Learning Systems paper by Piero Molino, Christopher Ré: https://cacm.acm.org/magazines/2022/1/257445-declarative-machine-learning-systems/fulltext
Slip of the Keyboard by Sir Terry Pratchett: https://www.terrypratchettbooks.com/books/a-slip-of-the-keyboard/
The Listening Society book series by Hanzi Freinacht: https://www.amazon.com/Listening-Society-Metamodern-Politics-Guides-ebook/dp/B074MKQ4LR

--------------- ✌️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, newsletters, 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 Piero on LinkedIn: https://www.linkedin.com/in/pieromolino/?locale=en_US

Timestamps:

[00:00] Introduction to Piero Molino

[01:09] Takeaways

[02:52] Blogpost ideas of Demetrios and Vishnu

[03:31] MLOps Swag/Merch

[04:37] What does Predibase do?

[07:40] Valuable paradigm of configuration over code

[10:31] Predibase for ML business outcome

[12:50] Query language to apply and configure models on top of data

[13:17] Query meaning in Predibase

[16:43] Training phase

[19:20] Predibase Pequel System

[20:30] Building Predibase?

[22:52] Perception of one configuration is the right way to do things

[26:10] Predibase edges and limits

[30:09] Strong opinions about Predibase

[32:56] Open-sourcing Ludwig

[35:47] Future of work in the context of Predibase

[40:27] Broadening skill sets

[44:38] Declarative Machine Learning Systems paper

[49:49] Lightning round

[57:26] Predibase is hiring!

[57:49] Wrap up

Lightning Sessions #1 with Peeyush Agarwal, Scaling Real-time Machine Learning at Chime.

MLOps Coffee Sessions #100 with Matthijs Brouns, MLOps Critiques co-hosted by David Aponte.

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

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// Abstract
MLOps is too tool-driven; don't let FOMO drive you to pick the latest feature/model/evaluation/ store, but pay closer attention to what you actually need to release more safely and reliably.

// Bio
Matthijs is a Machine Learning Engineer, active in Amsterdam, The Netherlands. His current work involves training MLEs at Xccelerated.io. This means Matthijs divides his time between building new training materials and exercises, giving live trainings, and acting as a sparring partner for the Xccelerators at their partner firms, as well as doing some consulting work on the side.
Matthijs spent a fair amount of time contributing to their open scientific computing ecosystem through various means. He maintains open source packages (scikit-lego, seers) as well as co-chairs the PyData Amsterdam conference and meetup.

// MLOps Jobs board  

jobs.mlops.community

// Related Links
https://www.youtube.com/watch?v=appLxcMLT9Y
https://www.youtube.com/watch?v=Z1Al4I4Os_A

--------------- ✌️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, newsletters, and more: https://mlops.community/

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/
Connect with Matthijs on LinkedIn: https://www.linkedin.com/in/mbrouns/

Timestamps:
[00:00] Introduction to Matthijs Brouns
[00:28] Takeaways
[03:09] Best of Slack Newsletter
[03:38] AI MLFlow
[04:43] Nanny ML
[05:08] Best confinement buy over the last 2 years
[06:35] Matthijs' day-to-day
[08:24] What's hot right now?  
[09:36] ML space, orchestration, deployment
[10:21] Scaling
[13:20] Low-risk releases
[15:27] Scale Limitations or Fundamental in API
[16:33] MLOps maturity to a certain point
[18:57] Interdisciplinary leverage needed
[21:11] PyScript  
[22:41] Next pipeline tools  
[24:02] General pattern to build your own tools
[30:25] Technology recommendation to a chaotic space
[33:46] Structured data vs tabular data  
[35:52] Big barriers in production
[37:57] Standardization
[39:20] Automation tension between the engineering side and the data science side
[41:50] Low-hanging fruit
[42:30] Human check
[43:43] Rapid-fire questions
[48:30] PyData Meetups

MLOps Coffee Sessions #99 with Ronen Dar and Gijsbert Janssen van Doorn, Getting the Most Out of Your AI Infrastructure, co-hosted by Vishnu Rachakonda.  

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

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// Abstract
Run: AI is building a cloud-based platform for building with AI. In this talk, we hear all about why this need exists, how this works, and what value it creates.

// Bio
Ronen Dar
Run: AI Co-founder and CTO Ronen was previously a research scientist at Bell Labs and has worked at Apple and Intel in multiple R&D roles. As CTO, Ronen manages research and product roadmap for Run: AI, a startup he co-founded in 2018. Ronen is the co-author of many patents in the fields of storage, coding, and compression. Ronen received his B.S., M.S., and Ph.D. degrees from Tel Aviv University.

Gijsbert Janssen van Doorn
Gijsbert is Director of Technical Product Marketing at Run: AI. He is a passionate advocate for technology that will shape the future of how organizations run AI. Gijsbert comes from a technical engineering background, with six years in multiple roles at Zerto, a Cloud Data Management and Protection vendor.

// MLOps Jobs board  
jobs.mlops.community

// Related Links
The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers by Ben Horowitz ebook:  https://www.scribd.com/book/211302755/The-Hard-Thing-About-Hard-Things-Building-a-Business-When-There-Are-No-Easy-Answers?utm_medium=cpc&utm_source=google_search&utm_campaign=3Q_Google_DSA_NB_RoW&utm_term=&utm_device=c&gclid=Cj0KCQjw1ZeUBhDyARIsAOzAqQLnUzXlgFT1PjU_M6jGqRZmwLbcK-mbfKQI4XrZJBRwgUs4x5j2hQ4aAmt1EALw_wcB

--------------- ✌️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, newsletters, 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 Ronen on LinkedIn: https://www.linkedin.com/in/ronen-dar/
Connect with Gijsbert on LinkedIn: https://www.linkedin.com/in/gijsbertjvd/

Timestamps:
[00:00] Introduction to Ronen Dar & Gijsbert Janssen van Doorn
[01:25] Takeaways
[04:24] Thank you, Run: AI, for sponsoring this episode!
[05:13] Run: AI products and components
[09:27] Companies coming to Run: AI and problems they solve
[13:30] Why is this problem hard?
[18:56] Run: AI's Vision
[22:12] Run-of-the-mill workload
[25:36] Engineering challenges and requirements building Run: AI  
[32:47] Process of solving problems on the same page
[35:45] Power to give data scientists
[37:38] Avoiding horror stories that might cost a lot of money
[44:23] Running multiple models on a single GPU
[47:17] Never scale down to zero
[48:28] So many ML Start-ups in Israel
[53:00] Vision for the future at GPUs and how Kubernetes will advance
[55:55] Future of AI accelerators
[57:03] Lightning round
[1:02:26] Wrap up

MLOps Coffee Sessions #98 with Brannon Dorsey, Racing the Playhead: Real-time Model Inference in a Video Streaming Environment, co-hosted by Vishnu Rachakonda.

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

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// Abstract
Runway ML is doing an incredibly cool workaround, applying machine learning to video editing. Brannon is a software engineer there, and he’s here to tell us all about machine learning in video and how Runway maintains its machine learning infrastructure.

// Bio
Brannon Dorsey is an early employee at Runway, where he leads the Backend team. His team keeps infrastructure and high-performance models running at scale and helps to enable a quick iteration cycle between the research and product teams.
Before joining Runway, Brannon worked on the Security Team at Linode. Brannon is also a practicing artist who uses software to explore ideas of digital literacy, agency, and complex systems.

// MLOps Jobs board  
jobs.mlops.community

// Related Links
Website: https://brannon.online
Blog: https://runwayml.com/blog/distributing-work-adventures-queuing-and-autoscaling/

--------------- ✌️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, newsletters, 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 Brannon on LinkedIn: https://www.linkedin.com/in/brannon-dorsey-79b0498a/

Timestamps:
[00:00] Introduction to Brannon Dorsey
[00:56] Takeaways
[05:42] Runway ML
[07:00] Replacement for Imovie?
[09:07] Machine Learning use cases of Runway ML
[10:40] Journey from starting as a model zoo to a video editor
[14:42] Rotoscoping  
[16:23] Intensity of ML models in Runway ML and engineering challenges
[19:55] Deriving requirements
[23:10] Runway's model perspective
[25:25] Why browser hosting?
[27:19] Abstracting away hardware
[32:04] Kubernetes is your friend
[35:29] Statelessness is your friend
[38:17] Merge to master quickly
[42:57] Brannon's winding history of becoming an engineer
[46:49] How much do you use Runway?
[49:37] Last book read
[50:36] Last bug smashed
[52:21] MLOps marketing that made eyes roll
[54:11] Bullish on technology that might surprise people
[54:39] Spot by NetApp
[56:42] Implementing Spot by NetApp
[56:55] How do you want to be remembered?
[57:22] Wrap up