MLOps Coffee Sessions #72 with Vishnu Rachakonda and Demetrios Brinkmann, 2021 MLOps Year in Review.

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// Abstract
Vishnu and Demetrios sit down to reflect on some of the biggest news and learnings from 2021, from the biggest funding rounds to the best insights. The two finish out the chat by talking about what to expect in 2022.

// Bio
Demetrios Brinkmann
At the moment, Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building LEGO houses with his daughter.

Vishnu Rachakonda
Vishnu is the operations lead for the MLOps Community and co-hosts the MLOps Coffee Sessions podcast. He is a machine learning engineer at Tesseract Health, a 4Catalyzer company focused on retinal imaging. In this role, he builds machine learning models for clinical workflow augmentation and diagnostics in on-device and cloud use cases. Since studying bioengineering at Penn, Vishnu has been actively working in the fields of computational biomedicine and MLOps. In his spare time, Vishnu enjoys suspending all logic to watch Indian action movies, playing chess, and writing.

//Related links
Dr. Angela Duckworth's book on Grit featuring Cody Coleman:
https://www.scribd.com/book/311311935/Grit?utm_medium=cpc&utm_source=google_search&utm_campaign=3Q_Google_DSA_NB_RoW&utm_device=c&gclid=CjwKCAjw0a-SBhBkEiwApljU0klle1jhwhK1hrCtdOzR2NIqNu1Y1D9kkGhFg5k2jvo5cCft7UOCqBoCsigQAvD_BwE
You don't need Kafka Vicki Boykis' blog:  
https://vicki.substack.com/p/you-dont-need-kafka?s=r
The Informed Company book:  
https://www.amazon.com/Informed-Company-Cloud-Based-Explore-Understand/dp/1119748003

--------------- ✌️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/

Timestamps:
[01:03] Campfire
[01:31] What are you most interested in learning about?
[02:00] Learning about serving models
[03:42] 2021 MLOps Community growth
[04:22] Engaging people coming back to the community
[05:41] Consistently high-quality interactions
[07:07] Vishnu's 2021 favorite moment in the Coffee Sessions
[10:05] Dr. Angela Duckworth's book on Grit featuring Cody Coleman
[11:43] Biggest surprise over the year for Demetrios
[13:48] You don't need Kafka Vicki Boykis' blog
[16:26] What excites Vishnu in 2022
[18:04] The Informed Company book
[20:48] What excites Demetrios in 2022
[26:28] News and blurbs  
[33:25] Spinouts
[34:30] Last year's cool events
[36:02] Community progress
[38:47] Community highlights
[41:28] New projects
[44:26] A controversial blog post
[46:03] Milestones
[46:57] Lessons
[50:00] Shout out and thanks to our sponsors!

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Loblaws is one of Canada’s largest grocery store chains. Mefta's team at Loblaw Digital runs several ML systems, such as search, recommendations, inventory, and labor prediction on production. In this conversation, he shares his experience setting up their ML platform on GCP using Vertex AI and open-source tools. 

The goal of this platform is to help all the data science teams within their organization to take ML projects from EDA to production rapidly, while ensuring end-to-end tracking of these ML pipelines. We also talk about our overall platform architecture and how the MLOps tools fit into the end-to-end ML pipeline.

//Bio
Mefta Sadat is a Senior ML Engineer at Loblaw Digital. He has been here for over three years, building the Data Engineering and Machine Learning platform. He focuses on productionizing ML services, tools, and data pipelines. Previously, Mefta worked at a Toronto-based Video Streaming Company and designed and built the recommendation system for the Zoneify App from scratch. He received his MSc in Computer Science from Ryerson University, focusing on research to mitigate risk in Software Engineering using ML.

--------------- ✌️Connect With Us ✌️ -------------
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Follow us on Twitter: @mlopscommunity
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Catch all episodes, blogs, newsletter, and more: https://mlops.community/

Timestamps:
[00:00] Introduction to Mefta Sadat
[01:04] Mefta's background  
[02:45] Mefta's journey in ML Engineering
[04:19] Use cases of Machine Learning at Loblaws
[06:00] Loblaws' team operation
[07:37] Number of people in the team and number of users in the platform
[08:40] Software engineering process
[10:47] Data platform vs ML platform
[13:10] Timeline leveraging machine learning in Loblaws products and business
[15:01] Transition from legacy systems to the cloud
[16:47] Recommendation System use case - Legacy Style Stack and its impact on the business
[21:01] Biggest challenges and pain points
[24:31] Choices of tools to use  
[27:31] Dealing with data access
[30:39] The good, the bad, and the ugly  
[32:48] Setting up alerts on image classification models
[33:53] Productionizing ML passion
[36:00] Post-deployment monitoring of recommendation systems
[37:47] Wrap up

MLOps Coffee Sessions #70 with Reah Miyara, 2022 Predictions for MLOps and the Industry.

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// Abstract

MLOps has moved fast in the last year. What will 2022 be like in the MLOps ecosystem? Raeh from Arize AI comes on to talk to us about what he expects for the new year.  
Arize is kindly offering 20 free subscriptions to their tool. No marketing BS, these are design partners. First-come first first-served https://arize.com/mlops-signup/!

// Bio
Reah Miyara is a Senior Product Manager at Arize AI, a leading ML monitoring and observability platform counted on by top enterprises to track billions of predictions daily. Reah joins Arize from Google AI, where he led product strategy for the Algorithms and Optimization organization. His experience as a team and product leader is extensive, touching a broad cross-section of the AI technology landscape.

Reah played pivotal roles in ML and AI initiatives at Google, IBM Watson, Intuit, and NASA Jet Propulsion Laboratory, and his work has directly contributed to many important innovations and successes that have moved the broader industry forward. Reah also co-led the Google Research Responsible AI initiative, confronting the risks of AI being misused and taking steps to minimize AI’s negative influence on the world.

// Relevant Links
Subscription - https://arize.com/mlops-signup/
https://arize.com/blog/welcome-to-arize-reah/
https://arize.com/blog/best-practices-in-ml-observability-for-monitoring-mitigating-and-preventing-fraud/
https://www.reah.me/

--------------- ✌️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 Reah on LinkedIn: https://www.linkedin.com/in/reah/

Timestamps:
[00:00] Introduction to Reah Miyara
[01:57] Wrong predictions
[03:41] Real predictions for 2022
[04:00] One: AI fairness and bias issues will get worse before they get better.
[07:27] Two: Enterprises will stop shipping AI blinds
[10:51] Three: The Citizen Data Scientist will rise
[17:07] Four: The ML infrastructure ecosystem will get more complex
[22:28] Five: Unleash the power of unstructured data
[26:34] Six: Robustness of ML Models against changes
[33:18] We want to have the best ML monitoring and observability tool out there.
[34:07] Demetrios' prediction: More talks about laws and regulations will happen, but nothing will actually get done.
[35:27] Wrap up

MLOps Coffee Sessions #69 with James Lamb, Building for Small Data Science Teams, co-hosted by Adam Sroka.

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// Abstract
In this conversation, James shares some hard-won lessons on how to effectively use technology to create applications powered by machine learning models.

James also talks about how making the "right" architecture decisions is as much about org structure and hiring plans as it is about technological features.

// Bio
James Lamb is a machine learning engineer at SpotHero, a Chicago-based parking marketplace company. He is a maintainer of LightGBM, a popular machine learning framework from Microsoft Research, and has made many contributions to other open-source data science projects, including XGBoost and prefect.

Prior to joining SpotHero, he worked on a managed Dask + Jupyter + Prefect service at Saturn Cloud and as an Industrial IoT Data Scientist at AWS and Uptake. Outside of work, he enjoys going to hip hop shows, watching the Celtics / Red Sox, and watching reality TV (he wouldn’t object to being called “Bravo Trash”).

// Relevant Links
James keeps track of conference and meetup talks he has given at https://github.com/jameslamb/talks#gallery. The audience for this podcast might be most interested in "Scaling LightGBM with Python and Dask" and "How Distributed LightGBM on Dask Works".

--------------- ✌️Connect With Us ✌️ -------------
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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 James on LinkedIn: https://www.linkedin.com/in/jameslamb1/

Timestamps:
[00:00] Introduction to James Lamb
[01:11] James' background in the machine learning space
[03:24] LightGBM
[09:56] Community behind LightGBM
[13:36] Background of James in SpotHero
[20:06] Experience in Maturity Models
[22:40] Bottlenecks of tradeoffs between speed and confidence
[28:28] Tools to be excited about
[31:46] To code your own that's already out there
[36:33] Building design decisions  
[39:36] Risk of the unicorn
[42:44] Cross-team empathy
[47:18] Proudest technical accomplishment and/or biggest frustration, less proud of lessons learned
[50:53] SpotHero is hiring!
[51:20] Wrap up
[51:53] Please like, subscribe, and you can leave a review!

MLOps Coffee Sessions #68 with Chris Albon, Wikimedia MLOps co-hosted by Neal Lathia.

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// Abstract
Chris Alban (Wikimedia ML team lead) and Neil Lithia discuss Alban's high-output drive, Wikimedia's open-source ML infrastructure, and a six-person team's role in maintaining editor-assist models like article quality prediction and mobile "Add-a-Link" features. Key topics include agile workflows for rapid model deployment via Kubeflow, repeatability through enforced policies, open-source tooling challenges (e.g., AMD GPUs), ethical governance with model cards, and community-trained models. The session highlights Wikimedia's lean, donation-funded scale and calls for contributions.

// Bio
Chris spent over a decade applying statistical learning, artificial intelligence, and software engineering to political, social, and humanitarian efforts. He is the Director of Machine Learning at the Wikimedia Foundation. Previously, Chris was the Director of Data Science at Devoted Health, Director of Data Science at the Kenyan startup BRCK, cofounded the AI startup Yonder, created the data science podcast Partially Derivative, was the Director of Data Science at the humanitarian non-profit Ushahidi, and was the director of the low-resource technology governance project at FrontlineSMS. Chris also wrote Machine Learning for Python Cookbook (O’Reilly 2018) and created Machine Learning Flashcards.

Chris earned a Ph.D. in Political Science from the University of California, Davis, researching the quantitative impact of civil wars on health care systems. He earned a B.A. from the University of Miami, where he triple majored in political science, international studies, and religious studies.

// Relevant 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 Neal on LinkedIn: https://www.linkedin.com/in/nlathia/
Connect with Chris on LinkedIn: https://www.linkedin.com/in/chrisralbon/

Timestamps:
[00:00] Introduction to Chris Albon
[00:28] Do you sleep? :-)  
[02:43] ML at Wikimedia
[09:27] Wikimedia workflow
[15:00] Creating a repeatable process
[19:11] Wikimedia element team size
[20:47] Wikimedia workflow and hardware
[23:56] Evaluating open source
[29:20] Lacking in ML source tooling
[33:11] Wikimedia's separate data platform
[38:14] Abstractions
[41:50] Experimentation aspect of getting models into production
[44:05] Stack of Abstraction in ML
[47:16] Chris' proudest model
[49:10] How Wikimedia works with communities
[55:24] Large language models
[1:02:16] Beautiful vision
[1:03:23] Wrap up

MLOps Coffee Sessions #67 with John Crousse, ML Stepping Stones: Challenges & Opportunities for Companies, co-hosted by Adam Sroka.

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// Abstract
In this coffee session, John shares his observations after working with multiple companies that were in the process of scaling up their ML capabilities.

John's observations are mostly around changes in practices, successes, failures, and bottlenecks identified when building ML products and teams from scratch. John shares a few thoughts on building long-term products vs short-term projects, on the important non-ML components, and the most common missing pieces he sees in today's ecosystem. John also elaborates on how those challenges and solutions can differ for different company sizes.

// Bio
John always liked CS/ML/AI, but it wasn't such a hot topic back then. He found opportunities to work on models in the Financial industry as a consultant from 2007 to 2017, then he went freelance to move outside of the financial industry and focus on AI/ML.  

John likes to do things efficiently, and MLOps is the bottleneck, so he ended up spending more time on MLOps than on models lately.  

John finished his CS degree in 2007.  

// Relevant Links  

--------------- ✌️Connect With Us ✌️ -------------
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Connect with Adam on LinkedIn: https://www.linkedin.com/in/aesroka
Connect with John on LinkedIn: https://www.linkedin.com/in/john-crousse-31219b9

Timestamps:
[00:00] Introduction to John Crousse
[01:11] Main trends in Machine Learning
[03:07] Symptoms of a Machine Learning product
[05:05] Proper product with limited resources
[08:52] Going into production mindsets
[11:22] Bottlenecks and challenges
[14:55] Business case for Machine Learning or MLOps in small organizations
[17:04] Gathering feedback is best suited to product owners
[19:14] More substantial role  
[20:11] Data factory
[24:03] Delivery patterns or tech stacks
[26:06] Bottleneck metrics
[27:28] Concept of evaluation store
[32:18] The biggest gap to bridge
[34:42] Hindrance to people's development
[35:23] "The last mile of the machine learning projects"
[36:40] MLOps assessment survey
[40:10] Who owns the product and the path to recommend
[41:34] Datamesh community
[44:41] Tips on balancing between pure autonomy
[45:58] Wrap up

MLOps Coffee Sessions #66 with Jacopo Tagliabue, Machine Learning at Reasonable Scale.

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// Abstract
We believe that immature data pipelines are preventing a large portion of industry practitioners from leveraging the latest research on ML: the truth is, outside of Big Tech and advanced startups, ML systems are still far from producing the promised ROI.

The good news is that times are changing: thanks to a growing ecosystem of tools and shared best practices, even small teams can be incredibly productive at a “reasonable scale”. Based on our experience as founders and researchers, we present our philosophy for modern, no-nonsense data pipelines, highlighting the advantages of a "PaaS-like" approach.

// Bio
Educated in several acronyms across the globe (UNISR, SFI, MIT), Jacopo Tagliabue was co-founder and CTO of Tooso, an A.I. company in San Francisco acquired by Coveo in 2019. Jacopo is currently the Director of AI at Coveo, shipping models to hundreds of customers and millions of users. When not busy building products, he is exploring topics at the intersection of language, reasoning, and learning: his research and industry work are often featured in the general press and premier A.I. venues. In previous lives, he managed to get a Ph.D., do sciency things for a pro basketball team, and simulate a pre-Columbian civilization.

// Relevant Links
Bigger boat repo: https://github.com/jacopotagliabue/you-dont-need-a-bigger-boat
TDS series: https://towardsdatascience.com/tagged/mlops-without-much-ops (ep 3 and a NEW open-source contribution on data ingestion coming up)
Open datasets for e-commerce and MLops experiments:  https://github.com/coveooss/SIGIR-ecom-data-challenge

--------------- ✌️Connect With Us ✌️ -------------
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Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Jacopo on LinkedIn: https://www.linkedin.com/in/jacopotagliabue/

Timestamps:

[00:00] Introduction to Jacopo Tagliabue

[01:35] What Reasonable Scale means

[06:40] Biggest disconnects from Reasonable Scale

[12:32] Engineers need to do and tools to use at a Reasonable Scale

[15:25] Importance of maintenance

[17:27] Bigger boat repo demonstration of Reasonable Scale

[23:09] The Four Pillars

[27:27] ETL Paradigm

[30:16] Best practices around dragons in generic decisions and comparing the new outputs and saved snapshots

[33:32] Creating a knowledge hub

[36:28] Continuation of principles

[38:06] Distributed road

[42:24] Current state-of-the-art recommender systems

[49:04] What Kovio and TUSU do in recommender systems in the world

[53:19] Stack in recommender system

[59:11] Being optimistic in the current ecosystem we're living in

[1:01:43] Wrap up

MLOps Coffee Sessions #65 with Skylar Payne, The Future of Data Science Platforms is Accessibility.

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// Abstract
The machine learning and data science space is blowing up -- new tools are popping up every day. While we seem to have every type of "Flow" and "Store" you could imagine, few people really understand how to glue this stuff together. Despite all the tools we have available, we still see companies failing to leverage data science effectively to drive business results.
Instead of spending time driving business results, data scientists spend their time fiddling with Kubernetes, trying to debug that Spark serialization error figuring out how to map their code into the awkward "AI Pipeline" SDK. We have an industry filled with tools built by engineers... for engineers, rather than for data scientists. It's deeply disempowering.
Meanwhile, data is still used effectively to drive decisions in many companies. Analysts have been solving very similar problems on the back of applications like Excel, Tableau, and Mode for literally decades. While there are still challenges in analytics, the MLOps space could learn something from analytics tools. Analytics tools better understand how to make their tools accessible. Analytics tools better understand the value of iterability. Analytics tools better understand that data problems are wicked problems:  

- We have to iterate on the formulation and solution simultaneously

- They involve many stakeholders with different opinions
- There's no "right" answer
- The problems are never 100% solved.

If we're going to really drive the most business value from data science, we need to understand how to design our teams and tools to effectively work against such problems.

The future of data science platforms is accessibility and iterability.

// Bio
Data is a superpower, and Skylar has been passionate about applying it to solve important problems across society. For several years, Skylar worked on large-scale, personalized search and recommendation at LinkedIn -- leading teams to make step-function improvements in our machine learning systems to help people find the best-fit role. Since then, he shifted my focus to applying machine learning to mental health care to ensure the best access and quality for all. To decompress from his workaholism, Skylar loves lifting weights, writing music, and hanging out at the beach!

--------------- ✌️Connect With Us ✌️ -------------
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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 Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Skylar on LinkedIn: https://www.linkedin.com/in/skylar-payne-766a1988/

Timestamps:

[00:00] Introduction to Skylar Payne

[00:25] Skylar's blog post overview

[00:55] Data is Wicked

[02:22] Bundling & unbundling

[05:48] ML world vs Analytics world

[08:40] Startups from various perspectives

[11:27] Setting the right building blocks

[15:05] Defining process and interfaces

[19:51] KubeFlow success stories accessibility

[21:17] Machine Learning + Data Science

[26:48] Where to spend more time?

[28:19] Privacy

[34:28] Measuring Apps Feeds

[38:46] Difficult trade-offs

[42:46] Tools improvement in workflow

[47:24] Accessibility & Iterability

MLOps Reading Group meeting on November 20, 2021

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MLOps Coffee Sessions #64 with Slater Victoroff, The Future of AI and ML in Process Automation.

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// Abstract
The Unstructured Imperative
Recent advances in AI have dramatically advanced the state of the art around unstructured data, especially in the spaces of NLP and computer vision. Despite this, the adoption of unstructured technologies has remained low. Why do you think that is? How have the dynamics changed in the last five years?

Multimodal AI  
Historic AI approaches have generally been constrained to one data modality (i.e., text or image). Recently, a wide range of papers in image captioning and document understanding have emphasized the need for more sophisticated "multimodal" techniques that can fuse information from multiple modalities. What is multimodal learning, and why is it so promising? Why are we seeing such an explosion of activity? What is Indico doing in this space?

Machine Teaching
As methods of supervision become more complex and multifaceted, many researchers have begun investigating the inverse problem. That is how do we design supervision systems that more naturally follow human processes? What are some interesting trends in "the space", and where can we expect this field to go in the next few years?

// Bio
Slater Victoroff is the Founder and CTO of Indico, an enterprise AI solution for unstructured content that emphasizes document understanding.  
Slater has been building machine learning solutions for startups, governments, and Fortune 100 companies for the past seven years and is a frequent speaker at AI conferences.  

Indico’s framework requires 1000x less data than traditional machine learning techniques, and they regularly beat the likes of AWS, Google, Microsoft, and IBM in head-to-head bake-offs.

// Relevant Links
https://indico.io

--------------- ✌️Connect With Us ✌️ -------------
Join our Slack community: https://go.mlops.community/slack
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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 Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Slater on LinkedIn: https://www.linkedin.com/in/slatervictoroff

Timestamps:

[00:00] Introduction to Slater Victoroff

[03:52] Slater's journey into ML

[06:52] Birth of Indico

[07:04] "Unstructured data"

[09:47] Historical perspective journey of Indico

[11:13] Adoption of unstructured technologies

[16:05] Technology techniques

[21:40] Unstructured data challenges

[26:35] Changing Supervision

[28:35] Synthetic data and its role

[32:41] Human overfitting

[34:15] Intuition in productive systems

[38:21] Data flows and information flows

[40:17] Typical Indico client

[42:34] Client Accessibility

[43:08] Process Configurability

[45:20] Indico clients dealing with workflow

[47:26] "Models won't fix a broken process."

[49:19] Model understandability and explainability

[50:03] Programming data rather than programming models

[50:55] Modeling limiting factor

[51:44] "The shift is happening!"

[52:40] Machine Learning Engineering vs Software Engineering

[53:48] Advice to 2021 Machine Learning Engineers to focus on