Agentic Conversations (formally mlops.community)

Latest Episodes:

The Journey from Data Scientist to MLOps Engineer // Ale Solano // MLOps Coffee Sessions #80 Feb 08, 2022

Show notes

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

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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.

--------------- ✌️Connect With Us ✌️ -------------
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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

Platform Thinking: A Lemonade Case Study // Orr Shilon // MLOps Coffee Sessions #79 Feb 04, 2022

Show notes

MLOps Coffee Sessions #79 with Orr Shilon, Platform Thinking: A Lemonade Case Study.

// Abstract
This episode is the epitome of why people listen to our podcast. It’s a complete discussion of the technical, organizational, and cultural challenges of building a high-velocity, machine learning platform that impacts core business outcomes.
Orr tells us about the focus on automation and platform thinking that’s uniquely allowed Lemonade’s engineers to make long-term investments that have paid off in terms of efficiency. He tells us the crazy story of how the entire data science team of 20+ people was supported by only 2 ML engineers at one point, demonstrating the leverage their technical strategy has given engineers.

// Bio
Orr is an ML Engineering Team Lead at Lemonade, currently working on an ML Platform, empowering Data Scientists to manage the ML lifecycle from research to development and monitoring.

Previously, Orr worked at Twiggle on semantic search, at Varonis on data governance, and at Intel. He holds a B.Sc. in Computer Science and Psychology from Tel Aviv University.
Orr also enjoys trail running and sometimes races competitively.

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

Timestamps:

[00:00] Takeaways

[05:31] Introduction to Orr Shilon

[06:00] Looking for editors

[07:29] What is Lemonade and what does it do?

[08:43] Machine Learning in Lemonade

[10:06] End-to-end process of ML in Lemonade

[13:00] Recycling features

[14:52] Slack bot, Cooper

[16:15] Importance of automation in Lemonade

[18:11] Circumstances when automation is unnecessary

[19:44] Slack bot platform

[20:31] ML tools used by Lemonade

[23:45] Areas of friction

[26:15] Model serving framework

[28:30] Ownership models

[30:36] Facing challenges

[31:52] Theory about lack of talents

[34:13] Orr Shilon's team

[37:51] Continuation of the building blocks

[39:12] Testing challenges

[42:30] Clear vision of the Lemonade team

[44:46] Demetrios was ghosted by the head of data science at Lemonade

[45:46] Platform thinking

[47:27] What is a "Business point in time"?

[50:24] Wrap up

Calibration for ML at Etsy - apply() special // Erica Greene and Seoyoon Park // MLOps Coffee Sessions #78 Jan 31, 2022

Show notes

MLOps Coffee Sessions #78 with Erica Greene and Seoyoon Park, Calibration for ML at Etsy - apply() special.

// Abstract
This is a special conversation about Machine Learning calibration at Etsy. Demetrios sat down with Erica Greene and Seoyoon Park to hear about how they implemented Calibration into the Etsy Machine Learning workflow.
The conversation is a pre-chat with these two before their presentation at the apply() conference on February 10th.
Register here: applyconf.com

// Bio
Erica Geen
Erica is an engineering manager with a background in machine learning. She's passionate about developing programs and policies that support women and other underrepresented groups in technology.

Seoyoon Park
Backend software engineer and aspiring software architect interested in producing scalable, performant, and fault-tolerant applications by keeping up to date with best practices and industry standards. Seoyoon strives to better himself and his peers by advocating for frequent knowledge transfers and promoting a culture of continuous learning. Constantly looking for opportunities to grow as a developer and become a leader of the industry.

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Erica on LinkedIn: https://www.linkedin.com/in/ericagreene/
Connect with Seoyoon on LinkedIn: https://www.linkedin.com/in/seoyoonpark/

Timestamps:

[00:00] Quick notes

[00:47] Introduction to Erica Greene and Seoyoon Park

[02:37] Looking for an editor

[04:23] Seoyoon Park's background

[06:01] Erica Greene's background

[08:07] Seoyoon's transition to ML

[10:25] Erica's take as team manager

[11:58] Additional points from Seoyoon

[13:17] Early wins in ML

[15:41] Seoyoon's start in ML

[17:54] Three core things for Erica

[19:07] What is calibration?

[22:39] Calibration uses

[24:46] Shift in different models

[26:11] On Calibration of Modern Neural Networks

[26:52] Importance of Calibration to models

[28:31] Implementation of Calibrating

[31:24] Calibration metrics

[35:07] Calibration testing

[36:50] The bug encounter

[39:09] Debugging the fault

[43:16] Erica's war story

[46:01] Seoyoon's war story

[48:31] Wrap up

Data Mesh - The Data Quality Control Mechanism for MLOps? // Scott Hirleman // MLOps Coffee Sessions #77 Jan 28, 2022

Show notes

MLOps Coffee Sessions #77 with Scott Hirleman, Data Mesh - The Data Quality Control Mechanism for MLOps?

// Abstract
Scott covers what a data mesh is at a high level for those not familiar. Data mesh is potentially a great win for ML/MLOps as there is very clear guidance on creating useful, clean, well-documented/described, and interoperable data for "unexpected use". So instead of data spelunking being a harrowing task, it can be a very fruitful one. And that one data set that was so awesome?
Well, it wasn't a one-off; it's managed as a product with regular refreshes! And there is a LOT more ownership/responsibility on data producers to make sure the downstream doesn't break. Might sound like kumbaya for MLOps (or total BS?) re far cleaner data and fewer upstream breaks, so let's discuss the realities and limitations!

// Bio
A self-professed "chaotic (mostly) good character", Scott is focused on helping the data mesh community accelerate towards finding solutions for some of data management's hardest challenges. He founded the Data Mesh Learning community specifically to gather enough people to exchange ideas, much of which is patterned after the MLOps community. He hosts the Data Mesh Radio podcast, where he dives deep into topics related to data mesh to provide the data community with useful perspectives and thoughts on data mesh.

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

Timestamps:

[00:00] Takeaways

[04:47] Merchandise

[05:50] What is data mesh?

[08:17] What is a data product?

[11:14] Second layer of data mesh

[13:15] Data standards

[15:51] Third layer of data mesh

[17:13] Cultural aspect of data mesh

[21:56] Data mesh documentation

[24:29] Tooling challenges

[27:55] Data mesh in practice

[31:40] Difference in experiences

[36:05] Baby steps to a fully pledged data mesh

[42:05] How data mesh relates to ML

[48:30] Data mesh vs data mess jokes

[49:02] High risks in data mesh

[52:47] Quick wins

[56:10] Wrap up

Build a Culture of ML Testing and Model Quality // Mohamed Elgendy // MLOps Coffee Sessions #76 Jan 25, 2022

Show notes

MLOps Coffee Sessions #76 with Mohamed Elgendy, Build a Culture of ML Testing and Model Quality.

Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠⁠

// Abstract
Machine learning engineers and data scientists spend most of their time testing and validating their models’ performance. But as machine learning products become more integral to our daily lives, the importance of rigorously testing model behavior will only increase.

Current ML evaluation techniques are falling short in their attempts to describe the full picture of model performance. Evaluating ML models by only using global metrics (like accuracy or F1 score) produces a low-resolution picture of a model’s performance and fails to describe the model's performance across types of cases, attributes, and scenarios.

It is rapidly becoming vital for ML teams to have a full understanding of when and how their models fail and to track these cases across different model versions to be able to identify regression. We’ve seen great results from teams implementing unit and functional testing techniques in their model testing. In this talk, we’ll cover why systematic unit testing is important and how to effectively test ML system behavior.

// Bio
Mohamed is the Co-founder & CEO of Kolena and the author of the book “Deep Learning for Vision Systems”. Previously, he built and managed AI/ML organizations at Amazon, Twilio, Rakuten, and Synapse. Mohamed regularly speaks at AI conferences like Amazon's DevCon, O'Reilly's AI conference, and Google's I/O.

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

Timestamps:

[00:00] Takeways

[04:41] Why do ML Testing?

[08:41] Kolena's main goal

[09:41] Difference of ML Testing from others

[13:12] Importance of a knowledge base in the organization

[17:53] Computational cost issues from testing

[20:48] Convincing people to do more testing

[23:13] Testing resources recommendations

[25:15] How to get good at testing

[28:19] Dealing with ML regulations

[30:57] Identifying failure modes

[38:57] Test-centric development for production ML

[40:53] Identifying scenarios

[43:37] Computer vision samples in structured data

[46:10] "Deep Learning for Vision Systems" by Mohamed Elgendy

[49:36] Wrap up

Towards Observability for ML Pipelines // Shreya Shankar // MLOps Coffee Sessions #75 Jan 21, 2022

Show notes

MLOps Coffee Sessions #75 with Shreya Shankar, Towards Observability for ML Pipelines.

Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠

// Abstract
Achieving observability in ML pipelines is a mess right now. We are tracking thousands of means, percentiles, and KL divergences of features and outputs in a haphazard attempt to figure out when and how to retrain models.

In this session, we break down current unsuccessful approaches and discuss the path towards effectively maintaining ML models in production. Along the way, we introduce mltrace -- a preliminary open source project striving towards "bolt-on" observability in ML pipelines.

// Bio
Shreya Shankar is a computer scientist living in the Bay Area. She's interested in building systems to operationalize machine learning workflows. Shreya's research focus is on end-to-end observability for ML systems, particularly in the context of heterogeneous stacks of tools.

Currently, Shreya is doing her Ph.D. in the RISE lab at UC Berkeley. Previously, she was the first ML engineer at Viaduct, did research at Google Brain, and completed her BS and MS in computer science at Stanford University.

// Related Links
Shreya Shankar's blog posts: https://www.shreya-shankar.com/
Shreya Shankar's Podcasts: https://www.listennotes.com/top-episodes/shreya-shankar/
The deployment phase of machine learning by Benedict Evans: https://www.ben-evans.com/benedictevans/2019/10/4/machine-learning-deployment
Shreya Shrankar's mltrace blogpost: https://www.shreya-shankar.com/introducing-mltrace/

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

Timestamps:
[00:00] Introduction to Shreya Shankar
[01:12] Shreya's background
[03:22] Contrast in scale influence
[05:28] Embedding ML and building machine learning infused products
[07:26] Management structure and professional incentive
[08:25] Organizational side of MLOps retros
[10:15] Tooling implementations
[12:00] Structured rational investment hardships
[13:17] Working at a start-up
[14:02] Academic work and entrepreneurial ambitions
[16:00] ML Monitoring Observability interest
[17:14] Where to get started
[20:47] Realization while at Viaduct
[23:30] Preventing alert fatigue
[27:04] Tooling bridging the gap
[30:40] Juncture at the overall MLOps ecosystem
[33:58] The deployment phase of machine learning - it's the new SQL by Benedict Evans
[35:30] Model monitoring
[36:16] mltrace
[38:28] Introducing the mltrace blog post series
[41:25] Tips to our content creators/writers
[43:47] Monitoring through the lens of the database
[47:37] Advice about picking up ML engineering and ML systems development in 2022
[49:36] Database low down the stack
[50:51] Most excited about 2022
[52:13] What MLOps space/ecosystem should change?
[53:21] Funding has changed the incentives around innovation
[54:52] Competition in million-dollar rounds
[55:25] Starting a company
[56:30] Wrap up

Scaling Biotech // Jesse Johnson // MLOps Coffee Sessions #74 Jan 19, 2022

Show notes

MLOps Coffee Sessions #74 with Jesse Johnson, Scaling Biotech.

Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter

// Abstract
Scaling a biotech research platform requires managing organization complexity - teams, functions, projects - rather than just the traditional volume, velocity, and variety. By examining the processes and experiments that drive the platform, you can focus your work where it matters the most by finding the ideal balance for each type of experiment, along with a number of common trade-offs.

// Bio
Jesse Johnson is head of Data Science and Data Engineering at Dewpoint Therapeutics, an R&D-stage biotech startup. His interest in exploring complex systems, understanding what makes them tick, then using this understanding to improve and scale them led him from academic mathematics into software engineering (Google, Verily Life Sciences), and then to Biotech (Sanofi, Cellarity, Dewpoint). His goal is to identify ways to scale biotech research through better software and organizational design.

// Related Links
Jessie's blogposts: scalingbiotech.com

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Jesse on LinkedIn: https://www.linkedin.com/in/jesse-johnson-51619a7/

Timestamps:
[00:00] Introduction to Jesse Johnson
[05:10] Jesse's background
[05:52] Biotech environments
[06:31] Jesse's background in Biotech companies
[09:21] Jesse's journey from academic to software engineering
[12:20] Transition from primary output insights/research into writing code
[14:54] Actual hands-on use case in practice
[19:19] Jesse's career trajectory
[23:57] Where we're at, state-of-the-art data engineering and its outstanding challenges
[26:50] Dewpoint's data and machine learning challenges and tooling
[29:04] Dewpoint's team structure
[30:20] Jesse, the VP of Data Science and Data Engineering
[33:24] New biotech data makes it hard to design a data platform
[35:35] Changes in how biotech data is viewed
[35:54] Experiment data output
[40:19] Solving challenges in structuring real-world context into interpretable data fields
[44:16] Maturity between the current data engineering and MLOps tooling space
[47:31] Achieving a blogpost mission in 2022
[49:50] Wrap up

On Structuring an ML Platform 1 Pizza Team //Breno Costa & Matheus Frata //MLOps Coffee Sessions #73 Jan 07, 2022

Show notes

MLOps Coffee Sessions #73 with Breno Costa and Matheus Frata, On Structuring an ML Platform 1 Pizza Team.

Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter

// Abstract
Breno and Matheus were part of an organizational change at Neoway in recent years. With the creation of cross-functional and platform teams in order to improve the value stream generated by these teams. They share their experience in creating a machine learning platform team. The challenges they faced along the way, how they approached using product thinking, and the results achieved so far.

// Bio
Matheus Frata Matheus is an Electronics Engineer who got into Data Science by accident! During his graduation, Matheus joined Neoway as a Data Scientist, but during that time, he saw a lot of problems that were related to engineers! This was Matheus' beginning with MLOPS. Today, Matheus works as a Machine Learning Engineer helping their Data Scientists to FLY!!!

Breno Costa
Breno uses his mixed background in Computer Science and Mathematical Modeling to design and develop ML-based software products. A brief period as an entrepreneur gives a different look at how to approach problems and generate more value. He has worked at Neoway for three years and currently works as a machine learning engineer on the Platform team.

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Breno on LinkedIn: https://www.linkedin.com/in/breno-c-costa/
Connect with Matheus on LinkedIn: https://www.linkedin.com/in/matheus-frata/

Timestamps:
[00:00] Introduction to Breno Costa & Matheus Frata
[02:08] Breno's background in Neoway
[03:23] What does Neoway do, and Matheus' background in Neoway
[05:43] Organizational structure of Neoway
[07:31] Concept of redesign
[10:47] Getting the structure right as a priority
[15:26] Designing the teams
[20:28] Three different ways of setting up the cell interaction
[23:58] Platform differences
[25:33] Technical components before redesigning and organizational overhauling
[31:50] Supporting platform teams
[33:23] Settling tech stack, managing technical needs
[42:10] Building internal tools
[50:10] Wrap up

2021 MLOps Year in Review // Vishnu Rachakonda and Demetrios Brinkmann // MLOps Coffee Sessions #72 Jan 03, 2022

Show notes

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

Get the newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://go.mlops.community/YTNewsletter⁠

// 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.

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!

Setting up an ML Platform on GCP: Lessons Learned // Mefta Sadat // MLOps Coffee Sessions #71 Dec 28, 2021

Show notes

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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.

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

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