Agentic Conversations (formally mlops.community)

MLOps community meetup #69! Last Wednesday, we talked to Emmanuel Raj, Senior Machine Learning Engineer at TietoEvry.

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// Abstract
The talk focuses on simplifying/demystifying MLOps, encourages others to take steps to learn this powerful SE method. We also talked about Emmanuel's journey in ML engineering, the evolution of MLOps, daily life, and SE problems, and what's next in MLOps (fusion of AIOps, EU AI regulations impact on MLOps workflow, etc).

// Bio
Emmanuel Raj is a Finland-based Senior Machine Learning Engineer. He is a passionate ML Researcher, Software engineer, speaker, and author. He is also a Machine Learning Engineer at TietoEvry and a Researcher at Arcada University of Applied Sciences in Finland. With over 6+ years of experience building ML solutions in the industry, he has worked on multiple domains such as Healthcare, Manufacturing, Finance, Retail, e-commerce, aviation, etc.   

Emmanuel is passionate about democratizing AI and bringing state-of-the-art research to the industry. He has a keen interest in R&D in technologies such as Edge AI, Blockchain, NLP, MLOps, and Robotics. He believes the best way to learn is to teach, and he is passionate about teaching new technologies; that's one reason for writing a book and making an online course on MLOps.   
Emmanuel is the author of the book "Engineering MLOps". The book covers industry best-case practices and hands-on implementation to rapidly build, test, and manage production-ready machine learning life cycles at scale. There is a big evolution happening in Data science for good, and we are moving away from notebooks and models sharing to a collaborative way of working via MLOps. We will discuss this big evolution of DevOps, MLOps, Data Engineering, Data Science, and Data-Driven business in the meetup.

----------- 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

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Emmanuel on LinkedIn: https://www.linkedin.com/in/emmanuelraj7/

// Related Links:  
www.emmanuelraj.com
https://www.youtube.com/watch?v=m32k9jcY4pY
https://www.youtube.com/watch?v=1sGECHbc9zg

[00:00] Introduction to Emmanuel Raj
[03:37] Emmanuel’s Background in Tech
[04:18] Software Beyond Deterministic Rules
[05:48] Common Machine Learning Failures
[09:07] Real-World Testing Importance
[10:10] Onyx vs. Jupyter for Production
[12:13] Keep Solutions Lean and Simple
[12:20] Building Robust CI/CD Pipelines
[14:58] Monitoring the ML Lifecycle
[15:48] Developing AIOps Systems
[16:20] AIOps in CI/CD Pipelines
[19:33] Starting MLOps Capability Building
[22:00] Company Legacy Considerations
[24:47] Optimal Solutions Over Tools
[26:47] Open-Source Tools Discussion
[29:08] Security as a Roadblock
[31:00] What’s Next for MLOps
[32:08] Three Core MLOps Blocks
[34:40] MLflow Live Coding Highlights
[38:00] FastAPI Microservice Overview
[40:28] FastAPI Terminal Demo
[40:40] Local Testing with Locust
[40:56] Running Image and Container
[41:10] Predict Endpoint Post Request
[41:54] Load Testing Process
[43:47] Running Locust on Server
[44:38] Specifying the Endpoint
[45:20] Starting Locust Tests
[47:40] Pushing to Production
[48:15] Automating the Workflow
[50:00] Engineering MLOps Release Announcement

MLOps community meetup #68! Last Wednesday, we talked to Veselina Staneva of TeachableHub, Simarpal Khaira of Intuit, and Korri Jones of Chick-fil-A, Inc.

⁠⁠⁠⁠⁠⁠⁠⁠⁠
// Abstract
Building, designing, or even just casting the vision for MLOps for your company, whether a large corporation or an agile start-up, shouldn't be a nigh-impossible task. Complex, but not an impossible mountain to climb.   
In this meetup, we talked about the steps necessary to unlock the potential of data science for your organization, regardless of size.

// Bio
Veselina Staneva - Co-founder & Head of Product, TeachableHub
Over the past few years, Vesi has worked at a product company called CloudStrap.io, where, together with her team, they are simplifying cloud technologies and crafting modern solutions that lay a solid foundation for digital transformation at scale.

Vesi's main focus currently is on their new product, TeachableHub.com - an ML deployment and serving platform for teams, where she heads Product and Customer Development. In the past, Vesi had quite a diverse experience in managing projects for global enterprise companies such as telecommunications and internet service provider GTT and managed printing services giant HPInc, as well as deep-diving into e-commerce business development while running online stores on 7 Amazon markets as well as WordPress shops, where she managed to get from 0 to $30K MRR in less than a year without a dollar spent on paid advertising.
In Vesi's free time, she enjoys spending the rest of her energy doing all kinds of sports, as well as participating in non-professional triathlons and mountain bike ultra races.

Simarpal Khaira - Senior Product Manager, Intuit
Simarpal is the product manager driving product strategy for Feature Management and Machine Learning tools at Intuit. Prior to Intuit, he was at Ayasdi, a machine learning startup, leading product efforts for machine learning solutions in the financial services space. Before that, he worked at Adobe as a product manager for Audience Manager, a data management platform for digital marketing.

Korri Jones - Senior Lead Machine Learning Engineer, Chick-fil-A, Inc.
Korri Jones is a Sr Lead Machine Learning Engineer and Innovation Coach at Chick-fil-A, Inc. in Atlanta, Georgia, where he is focused on MLOps. Prior to his work at Chick-fil-A, he worked as a Business Analyst and product trainer for NavMD, Inc., was an adjunct professor at Roane State Community College, and an instructor for the Project GRAD summer program at Pellissippi State Community College and the University of Tennessee, Knoxville.
Korri's accolades are just as diverse, and he was in the inaugural 40 under 40 for the University of Tennessee in 2021, Volunteer of the year with the Urban League of Greater Atlanta with over 1000 hours in a single calendar year and has received the “Looking to the Future” award within his department at Chick-fil-A among many others, including best speaker awards in business case competitions.  However, the best award he has received so far is being a loving husband to his wife, Lydia.

----------- 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

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Vesi on LinkedIn: https://www.linkedin.com/in/veselina-d-staneva/
Connect with Simar on LinkedIn: https://www.linkedin.com/in/simarpal-khaira-6318959/
Connect with Korri on LinkedIn: https://www.linkedin.com/in/korri-jones-mba-780ba56/

Coffee Sessions #43 with Kyle Gallatin of Etsy, Maturing Machine Learning in Enterprise.

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// Abstract
The definition of Data Science in production has evolved dramatically in recent years. Despite increasing investments in MLOps, many organizations still struggle to deliver ML quickly and effectively. They often fail to recognize an ML project as a massively cross-functional initiative and confuse deployment with production. Kyle will talk about both the functional and non-functional requirements of production ML and the organizational challenges that can inhibit companies from delivering value with ML.

// Bio
Kyle Gallatin is currently a Software Engineer for Machine Learning Infrastructure at Etsy. He primarily focuses on operationalizing the training, deployment, and management of machine learning models at scale. Prior to Etsy, Kyle delivered ML microservices and led the development of MLOps workflows at the pharmaceutical company Pfizer. In his spare time, Kyle mentors data scientists and writes ML blog posts for Towards Data Science.

--------------- ✌️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

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

// Takeaways
Data science is still poorly defined, and there is a large variance in organizational maturity  
Basically, everything we need for mature ML in modern organizations exists technically, except for the strategy, mentality, organization, and governance
Organizations that poorly define data science often overburden their data scientists, but there are expectations that data scientists know some engineering
Operationalizing data science is not that different from software engineering, and software engineering can be one of the most valuable skill sets for a data scientist.

// Q&A with Kyle as a data science mentor:  
https://www.youtube.com/watch?v=7byRQGHD39w&t=1s

Timestamps:

[00:00] Introduction to Kyle Gallatin
[01:00] Kyle’s Path into Tech
[02:04] Data Analyst to Engineer
[03:45] Reflections on Learning CS
[04:04] SAS App with ML Services
[05:13] Python’s Strength in Machine Learning
[06:43] Working Effectively with YAML
[07:10] Choosing Technologies and Plug-ins
[08:43] Take the Easy Way
[09:00] Favorite Plug-ins Overview
[09:07] VS Code Remote SSH
[09:44] Future of Machine Learning
[11:12] MLOps Growth and Buzzword Status
[12:08] Exploring Heuristics and Next Steps
[15:19] Navigating Unknown ML Territory
[15:33] Monitoring and Observability Practices
[16:21] Specialized and Customized Solutions
[17:43] Balancing Commonality and Specificity
[17:54] Integrations Across ML Systems
[20:00] Measuring Time to Production
[20:22] Data Scientists’ Team Fit
[21:34] One Size Doesn’t Fit
[22:40] Building Depends on People
[23:40] Defining Data Science Roles
[24:00] Platform Engineering Perspective
[25:00] Optimizing Model Serving Value
[25:21] Model Serving Platforms
[27:13] Importance of Standardization
[29:00] Exercising Good Judgment
[29:57] Breaking Work into Pieces
[30:30] Data Access Regulations
[33:32] Technical Standpoint Discussion
[34:37] Defining Use Cases Clearly
[36:04] Next Big Thing: MLOps
[37:50] Modern Scaling Approaches
[38:46] Nontechnical Companies Stepping Up
[41:18] Defining Core Problems
[42:38] Considering Value and Needs

MLOps community meetup #66! Last Wednesday, we talked to Alfredo Deza, Author and Speaker.

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// Abstract
In this episode, the MLOps community talks about the importance of bringing DevOps principles and discipline into Machine Learning. Alfredo explains insights around creating the MLOps role, automation, constant feedback loops, and the number one objective - to ship Machine Learning models into production.   
Additionally, we covered some aspects of getting started with Machine Learning that are critical, in particular, how democratization ML knowledge is critical to a better environment, from libraries to courses, to production results. Spreading the knowledge is key!

// Bio
Alfredo Deza is a passionate software engineer, speaker, author, and former Olympic athlete. With almost two decades of DevOps and software engineering experience, he teaches Machine Learning Engineering and gives lectures around the world about software development, personal development, and professional sports.   

Alfredo has written several books about DevOps and Python, including Python for DevOps and Practical MLOps. He continues to share his knowledge about resilient infrastructure, testing, and robust development practices in courses, books, and presentations.  
Alfredo Deza is the author of Python for DevOps and Practical MLOps.

----------- 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

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Chris on LinkedIn: https://www.linkedin.com/in/chrisbergh/

Timestamps:
[00:00] Introduction to Alfredo Deza
[03:00] Alfredo's background in tech
[13:15] Who is this book for?
[14:15] "The reason why we need a Machine Learning book is that there's definitely a knowledge gap."
[16:05] Hierarchy of MLOps
[17:16] "Automation has to be the basis of pretty much everything."
[19:03] Logging - "When in doubt, log it out!"
[24:50] Maturity
[29:55] "The notion of self-healing is very appealing."
[31:20] Learning Test
[37:40] "Catch things as early as possible. Anything that comes at the end of the process, the closer you are to the production, the more expensive it could get."  
[37:54] "Expensive can be the dollar amount in engineering time, or it can be the dollar amount in services that you're using to produce, and the dollar amount on how long it would take to ship the version that fixes the problem."
[39:20] "Why not scan your containers before they hit the production and catch anything that has a critical vulnerability announced?"
[40:08] Interruption standards and pains
[42:34] "It is critical that we make it easier. How about we no longer point fingers and stigmatize people who don't do Machine Learning? The more people doing Machine Learning today, the better we're off."
[45:50] Simple and opinionated or flexible and complex  
[46:45] "You have to strike a balance, but you have to stay true to your principles."  
[50:38] Abstraction Layers
[56:57] Take a risk or stay safe?
[57:20] "I think you're gonna have risk everywhere you are. You're gonna have risk when you hire a Machine Learning Engineer. You're gonna have a risk with a Data Scientist. You're gonna have a risk with a Software Engineer."

MLOps community meetup #65! Last Wednesday, we talked to Kseniia Melnikova, Product Owner (Data/AI), SoftwareOne.

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// Abstract
In this MLOps Meetup, we talked about the Machine Learning model lifecycle and development stages, and then analyzed the main mistakes that everybody makes at each stage. Kseniia also provided the audience with solutions to the mistakes, and we discussed existing tools for experiment management.

// Bio
Kseniia is a product owner for Data/AI-based products. Right now, she is working mostly with numeric data analysis, customer insights, and product recommendations.
Previously, Kseniia worked at Samsung Research with the biometrics team. She was studying computer science in Russia (Moscow) and a little bit of management in South Korea (Seoul). One of the most interesting directions of research - Model Lifecycle Management Systems and Reproducibility.

----------- 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

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Kseniia on LinkedIn: https://www.linkedin.com/in/kseniia-melnikova/

Timestamps:
[00:00] Introduction to Kseniia Melnikova
[02:00] MLOps World Conference Announcement
[03:40] AI Development Process: Common Mistakes
[07:45] Step 1: Planning
[07:48] Mistake #1: Personal Decisions - Teamwork
[08:31] Mistake #1: Cases
[09:00] Mistake #1: Solution
[11:52] Scrum
[12:50] "In Scrum, it's hard to plan because, especially in research, you don't know which result affects new tasks; that's why it might be a little slow for Machine Learning."
[14:28] Step 2: Data Processing
[14:34] Mistake #2: Chaos with Datasets
[15:26] Mistake #2: Cases
[16:48] Mistake #2: Solution
[20:12] Step 3: Experiments
[20:21] Mistake #3: Lack of Experiment Tracking
[22:13] Mistake #3: Case - Manual Experiments Tracking
[24:10] Mistake #3: Solutions
[25:57] Experiments Tracking Tools Example: MLFlow UI
[26:46] Awareness of Existing Tools
[28:21] Tools' Features
[29:21] Possible Combination
[29:48] Another Possible Combination
[30:24] Best Practice
[34:18] Find Your Mistakes  
[35:35] Audio Data
[41:38] "I prefer reproducibility tools because it's automatic, and it also takes a lot of time to manually upload the results into the conference."
[43:03] AI Development Check-list
[43:40] Check-list Results
[44:52] "I think it's always interesting to rate yourself to share the results with other people to compete out of it."
[45:10] Why to Implement
[45:17] "If we have more automation on experimentations for data sets versioning, it will lead to less manual work."
[45:28] "AI Development process implementation will have the possibility to reproduce and compare experiments."
[45:37] "AI Development process implementation will make you comfortable with solving the issues you'll face every day."
[45:52] "AI Development process implementation will lead to a faster commercialization cycle because you will take less time on the process and more time for the results."
[46:03] "If we take all the principles of the AI Development process implementation, it will lead to easy communication between team members. You'll gain trust, have great teamwork, and everyone will have respect for each other."  
[49:50] Calculating the lost money

Coffee Sessions #42 with Amit Paka of Fiddler AI, Model Performance Monitoring.

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// Abstract
Machine Learning accelerates business growth but is prone to performance degradation due to its high reliance on data. Moreover, MLOps is often fragmented in many organizations, causing friction in debugging models in production. With new rules from the EU that focus on trust and transparency, it’s becoming more important to keep track of model performance. But how? We propose a new framework, a centralized ML Model Performance Management powered by Explainable AI. Learn more about how you can stay compliant while maximizing your model performance at all times with explainability and continuous monitoring.

// Bio
Amit is the co-founder and CPO of Fiddler, a Machine Learning Monitoring company that empowers companies to efficiently monitor and troubleshoot ML models with Explainable AI. Prior to founding Fiddler, Paka led the shopping apps product team at Samsung. Paka founded Parable, the Creative Photo Network, now part of the Samsung family. He also led PayPal's consumer in-store mobile payments, launching innovations like hardware beacon payments, and has developed successful startup products, particularly in online advertising - paid search, a contextual ad exchange, and display advertising. Paka has a passion for actualizing new concepts, building great teams, and pushing the envelope, and aims to leverage these skills to help define how AI can be fair, ethical, and responsible.

--------------- ✌️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

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

Timestamps:
[00:00] Thank you to Fiddler AI!
[00:46] Introduction to Amit Paka
[05:04] Amit's background in tech
[09:55] EU Regulation
[12:39] "The goal that the EU seems to be going for is they want to go for helping build human-centric and responsible AI."  
[13:28] 4 AI Categories:              
1. Unacceptable risk applications
2. High-risk applications
3. Limited risk applications
4. Minimal risk applications  
[14:58] Deep dive into High-risk applications
[17:28] Digital Services Act (DSA) and Digital Marketing Act (DMA)
[19:02] Military  
[19:33] "They don't know what they don't know, and they probably wanted the door open."  
[21:13] US on JIC Team - transparency and increasing trustworthiness on AI
[23:06] Diversity of industries and Explainability  
[24:22] "The urgent need for Explainability comes from verticals that are facing the problems today on the ground and cannot run their business." [30:09] Model Performance Management (MPM)
[34:05] "When your model is facing issues, you now have to root-cause it within life."
[35:40] Control Theory
[36:10] "Control Theory means that you do not just measure it, but you can influence it so you can actually keep it."
[38:14] Abstraction into being useful
[43:23] "You can train a model that accurately represents reality."
[44:00] Data scientist doing ML Flow
[53:04] Banking and Insurance adoption of ML
[55:48] Advise ML Scientists and Data Scientists in terms of Explainable AI
[58:25] "Models are incredibly hard to debug. You're just training a model for high accuracy, but you don't know how that accuracy is distributed."
[59:49] Linking of EU Regulation and MPM

Coffee Sessions #41 with Monmayuri Ray of Gitlab, CI/CD in MLOPS.

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// Abstract
We are all familiar with the concept of MVP. In the world of DevOps, one is also familiar with Minimal Viable Feature and further Minimal Viable Change. CI/CD is the orchestrator and the underlying base to enable automated experimentation, to start small, and build an idea for production. Now, if we use the same fundamentals in MLOps, what does that mean?
The podcast will take the audience on a journey in understanding the fundamentals of orchestrating machine predictions using responsible CI/CD in MLOps in this ever-changing, agile world of software development. One shall hope to learn how to excel at the craft of CI for Machine Learning (ML), lowering the cost of deployment through a robust CI/CD/CT/CF framework.

// Bio
Monmayuri is an advisor,  data scientist, and researcher specializing in MLops/DevOps at GitLab in Sydney. She builds creative products to solve challenges for companies in industries as diverse as financial services, healthcare, and human capital.

Along the way, Mon has built expertise in Natural Language Processing, scalable feature engineering, MLOps transformation and digitization, and the humanization of technology. With a background in applied mathematics in biomedical engineering, she likes to describe the essence of AI as “low-cost prediction” and MLOps as “low-cost transaction” and believes the world needs the collaboration of poets, historians, artists, psychoanalysts and scientists, engineers to unlock the potential of these emerging technologies where one works in making a machine think like humans and be efficient automated fortune tellers.

//Takeaways
Key Takeaways include how to incorporate the best CI/CD practice in your MLOPS lifecycle. Things to do and things not to do. How best to get the DevOps engineer, ML engineer, and data scientists to speak the same language and automate CI for the pipeline and models?

--------------- ✌️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

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Mon on LinkedIn: https://www.linkedin.com/in/monmayuri-ray-713164a0/

Timestamps:
[00:00] Introduction to Monmayuri Ray
[00:57] Mon's background in tech
[02:50] MLOps being approached at Gitlab
[07:00] CI/CD for MLOPS Definition
[07:57] "AI is the dropping cost of machine prediction."
[10:25] MLOps and other tools fitting into GitLab
[12:18] "If you want to have an MLOps first strategy, anything you are putting first needs to be substituted with what you had before. It's really important then to know your priorities."
[15:24] Process of how to build
[18:16] "Before getting into even understanding the maturity, understand the outcome."
[18:45] Challenges in CI/CD for MLOps
[19:50]" Automation also empowers collaboration."
[24:15] Keeping up
[28:33] "I think the best tools and frameworks are to give people the freedom to be the best version of who they are. As a system, being governed, having that controlled freedom, you can be more Human."
[31:20] Resources to suggest in terms of MLOps Education
[35:57] Effectiveness of understanding the business outcomes of MLOps to Gitlab customers.
[40:00] Enabling vs Keeping the guardrails on
[43:26] Best practices

MLOps community meetup #64! Last Wednesday, we talked to Christopher  Bergh, CEO, DataKitchen.

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

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// Abstract
Working on a technically difficult problem, there will be some things that are important no matter what industry you are in. Whether it's building cars in a factory, using agile or scrum methodology, or productionizing ML models, you need a few basics. Chris gives us some of his best practices in the conversation.

// Bio
Chris Bergh is the CEO and Head Chef at DataKitchen. Chris has more than 25 years of research, software engineering, data analytics, and executive management experience. At various points in his career, he has been a COO, CTO, VP, and Director of Engineering. Chris is a recognized expert on DataOps. He is the co-author of the "DataOps Cookbook” and the “DataOps Manifesto,” and a speaker on DataOps at many industry conferences.

// Takeaways
Your model is not an island. For success, Data science requires a high level of technical collaboration with other parts of the data organization.

// Related Links
On-Demand Webinar - Your Model is Not an Island:  Operationalizing Machine Learning at Scale with ModelOps  
https://info.datakitchen.io/watch-on-demand-webinar-operationalize-machine-learning-at-scale-with-modelops

----------- 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

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Chris on LinkedIn: https://www.linkedin.com/in/chrisbergh/

Timestamps:
[00:00] Introduction to Christopher Bergh
[02:57] MLOps community in partnership with MLOps World Conference
[04:34] Chris' Background
[07:59] "When we started with the company, I realized that the problem I have is generalizable to everyone. I'm getting enough there in years, and I wanted to remove the amount of pain that other people have."
[09:53] DataOps vs MLOps
[10:15] "I don't really honestly care what Ops you use, right? Hahaha! Call it your favorite Ops, 'cause first of all, as an engineer, I want precise definitions. I look at it from a completely odd-ball way, so you could call it whatever Ops term you want."
[12:45] Best practices of companies
[14:16] "When that code runs in production, monitor and check to see if it's right. Absorb it, monitor it, because the model could go out of tune. The data going into it could be wrong. The data transformation could break. Shit happens, and don't trust your data providers."
[19:00] The whole is still greater than its part
[20:26] "It is harder to focus on the results than just on a piece of the task. Don't spend too much time doing the wrong thing."
[23:50] DevOps Principles and Agile
[27:17] DataOps Manifesto - DataOps is Data Management reborn
[27:45] "The 'Ops' term is ending up encompassing the work that you do in addition to the system you build to do the work."
[30:45] Standardization  
[32:22] "I think that there's a lack of perception of the need to spend time on doing the operations part of the equation."
[34:15] Tools as Lego blocks
[34:49] "Good interphases make good neighbors."
[36:23] "Standards can help, but they're not the panacea."
[36:30] Cultural side - You build it, you own it, you ship it
[39:28] Value chain
[44:19] Ripple effect of testing
[48:03] Google on "One tool to rule them all"
[49:50] "Legacy happens if you're gonna live in the real world and not start greenfield projects."
[53:47] Starting MLOps in the legacy system

Coffee Sessions #40 with Srivatsan Srinivasan of AIEngineering, Scaling AI in Production.  

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

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// Abstract
This Coffee Session is a Collaboration with @AIEngineering. Srivatsan Srinivasan, the founder of the popular AI Engineering channel on YouTube and a senior partner at a major technology consultancy. Sri has a true passion for building ML systems, and his channel engages in some of the most detailed, thorough, and complete treatments of the MLOps topics we often discuss in the community. It's no surprise, then, that his coffee session with us went similarly!

This conversation became pretty technical. We delved into details of how to set up CI/CD, how to set up the right kinds of tests, which cloud tools stand out to us on GCP, AWS, etc., and many other topics. Sri had a wealth of knowledge on all these fronts!

// Bio
20+ years of intense passion for building data-driven applications and products for top financial customers. Srivatsan has been a trusted advisor to a senior-level executive from business and technology, helping them with complex transformations in the data and analytics space. Srivatsan also runs a YouTube Channel (AIEngineering) where he talks about data, AI, and MLOps.
// Related Links
AI and MLOps free courses - https://github.com/srivatsan88
YouTube channel: bit.ly/AIEngineering

--------------- ✌️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
Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/
Connect with Srivatsan on LinkedIn: https://www.linkedin.com/in/srivatsan-srinivasan-b8131b/

Timestamps:
[00:00] Introduction to Srivatsan Srinivasan
[01:41] Background on YouTube AIEngineering
[03:17] Tips on learning MLOps and starting with the field
[06:00] "Focus on your key challenges, and that will drive the capability that you need to implement."
[06:50] Tips on starting CI/CD
[08:46] "Start with DevOps and see what additional capabilities you will require for the Machine Learning aspect of it."
[09:24] Staying general in different environments
[10:43] "Focus on the core concepts of it. The concepts are similar."   
[12:10] Testing systems robustly
[20:00] Trends within MLOps space
[20:31] "Everybody can fail fast, but you need to fail smart because Machine Learning is a huge investment."
[23:21] GCP Auto ML
[26:54] Deployment
[27:06] "It's not only the tools, but it's also the patterns."
[29:34] Kubernetes perspective
[31:21] Favorite model release strategy
[36:22] Annotation, labeling, and concept of ground truth
[38:10] Best practices in Architecture and systems design in the context of ML
[41:29] "You learn a lot, at the same time the complexity also increases, so work with multiple teams in this process to learn it."  
[42:35] "Your speed increases based on the way you envision your architecture."
[42:55] Software engineering lifecycle vs machine learning development life cycle
[44:55] Youtube experience
[45:50] "My focus has always been from intermediate to experts."
[46:24] Content creation
[47:17] "You cannot do everything in MLOps at one stretch. You have to see what is critical for you."
[47:23] "For me, continuous training is not that critical because I don't want to take the freedom out of the data scientists."
[48:31] New contents planned
[48:40] IoT and Edge Analytics - Predictive maintenance  
[50:21] Wrap up

Coffee Sessions #39 with Stephen Galsworthy of Quby, MLOps: A leader's perspective.

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

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// Abstract  

Demetrios and Vishnu sit down with Stephen Goldsworthy, former Chief Data and Product Officer at Quby, to explore the evolving intersection of machine learning, organizational culture, and leadership. The discussion traces Stephen’s journey from data scientist to executive board member, highlighting how the toughest challenges in scaling ML aren’t technical—they’re organizational. He shares lessons from embedding data science into core product teams, aligning executives around AI literacy, and navigating the cultural transformation of merging a fast-moving tech company with a traditional utility. This episode unpacks how true MLOps maturity depends less on tools and more on communication, structure, and shared understanding across every level of the business.

//Bio
Dr. Stephen Galsworthy is a data leader skilled at building high-performing teams and passionate about developing data-powered products with lasting impact on users, businesses, and society.  

Most recently, he was the Chief Data and Product Officer at Quby, an Amsterdam-based tech company offering data-driven energy services. He oversaw its transformation from a hardware-based business to a digital organization with data and AI at its core. He put in place a central cloud-based data infrastructure and unified analytics platform to collect and take advantage of petabytes of IoT data. His team deployed real-time monitoring and energy insight services for 500k homes across Europe.   

Stephen has a Master’s degree and Ph.D. in Mathematics from Oxford University and has been leading data science teams since 2011.

//Takeaways
MLOps as a process, people, and technological problem.  
Experiences from a team working at the forefront of data and AI.

--------------- ✌️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

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

Timestamps:

[00:00] Introduction to Stephen Galsworthy
[01:28] Stephen’s Background in Tech
[03:53] ML at Scale and Production
[05:28] Production Is Not Final
[06:15] From Zero to One
[07:35] Non-Technical Challenges in ML
[09:13] Technology No Longer Stumbling Block
[09:37] Maximizing Value from Teams
[10:20] Focusing on Business Impact
[10:30] Organizational View of MLOps
[18:00] Importance of Labeled Data
[20:43] Aligning with Stakeholders Effectively
[21:05] Different Approaches for Stakeholders
[25:34] Filtering Noise for Strategy
[26:54] Stephen’s Role and Mandate
[28:30] Beyond Traditional Data Leadership
[31:37] MLOps Organizational Challenge Project
[32:15] Lessons from First Projects
[35:37] Speed Through Team Discipline
[37:00] Processes Enable Smooth Operations
[38:07] Communicating Effectively with Stakeholders
[41:34] Transparency with Leadership Peers
[43:25] Ensuring End-User Benefits
[43:44] Sharing Success Inside and Out
[46:06] Prioritizing High-Impact Problems
[47:05] Simple Solutions Over Machine Learning