Agentic Conversations (formally mlops.community)

MLOps community meetup #63! Last Wednesday, we talked to Felipe Campos Penha, Senior Data Scientist, Cargill.

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// Abstract
Can one learn anything useful by creating content online? The usual answer is a sounding YES. But what about live coding an MLOps project on Twitch? Can anything good come out of it?

//Bio
Felipe Penha creates content about Data Science regularly on the Data Science Bits channel on YouTube and Twitch. He has 8+ years of experience with hands-on data-related work, starting with his doctorate in Astroparticle Physics. His career in the private sector has been devoted to bringing value to various segments of the Food and Beverages Industry through the use of Analytics and Machine Learning.

----------- 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 Felipe on LinkedIn: https://www.linkedin.com/in/fcpenha/

Timestamps:
[00:00] Introduction to Felipe Campos Penha
[01:30] Felipe's background
[05:36] Developing models in physics vs developing models for companies
[08:07] Felipe's transition from Jupyter Notebook to Operational ML
[09:34] "The thought of business basically for customers, they always wanted to see the value and try to roll out more manual work like spreadsheets so they could try out that model in the field."
[12:07] Felipe's software engineering development learning
[14:10] Catalyst on YouTube and Twitch
[18:06] Elements of Twitch
[20:02] Non-polished versions of Twitch
[21:16] "Twitch was not made for coding, it was for gamers."
[26:17] Felipe's audience impact on Twitch
[28:02] Logistical pieces
[30:43] Words of wisdom on live streaming
[30:56] "Don't be afraid to start. There are many streamers who are actually learning from scratch, and they are showing the process of learning online. They are learning faster because the help is faster."
[33:16] Blog post as another means to Twitch
[33:50] "I'm a perfectionist when I'm writing. The shorter it is, the harder it could get. You want to polish it a lot to make nice figures. I learned a lot, but for me, I feel that process is too slow because you're thinking about one subject for a long time, trying to polish it, while in live streaming, it's very dynamic and fast."
[34:25] Twitch affecting Felipe's career
[36:36] "Exposing yourself, showing your mistakes, vocalizing your thoughts, I think all of this makes you a better programmer."  
[37:12] Getting through a problem
[39:41] Recommended streamers that caught Felipe's interest
[41:00] Community aspect and importance of Twitch
[42:42] Role of community on Twitch
[45:16] "Twitch is becoming such a trend that even companies are following."

MLOps community meetup #62! Last Wednesday, we talked to Oguzhan Gencoglu, Co-founder & Head of AI, Top Data Science.

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// Abstract
Starting the AI adoption with AI Proof-of-Concepts (PoCs) is the most common choice for most companies. Yet, a significant percentage of AI PoCs do not make it into production, whether they were successful or not. Furthermore, running yet another AI PoC follows the law of diminishing returns in various aspects. This talk will revolve around this theme.

// Bio
Oguzhan "Ouz" Gencoglu is the Co-founder and Head of AI at Top Data Science, a Helsinki-based AI consultancy. With his team, he delivered more than 70 machine learning solutions in numerous industries for the past 5 years. Before that, he used to conduct machine learning research in several countries, including the USA, the Czech Republic, Turkey, Denmark, and Finland. Oguzhan has given more than 40 talks on machine learning to audiences of various backgrounds.

----------- 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 Oguzhan on LinkedIn: https://www.linkedin.com/in/ogencoglu/

Timestamps:
[00:00] Introduction to Oguzhan Gencoglu
[00:47] Ouz's background
[01:47] Recurring/repetitive problem patterns
[03:16] "When you solve a repetitive task in an automatic way, that's Scalability."
[04:32] Evolution expected of Machine Learning
[05:10] "People are quite confused about the titles and what's worse, those titles don't have a common definition in different companies. If you feel a little bit overwhelmed, that's normal."
[08:04] Proof-of-Concepts
[10:35] Successful PoCs but not Productionized
[16:03] Productionize as soon as possible
[16:47] "In your Proof-of-Concepts, it's not only technical, but it's also a mindset."
[20:00] Framework of a successful PoCs
[24:28] Taking too much on PoCs
[28:05] Proof-of-Concepts after Proof-of-Concepts and Proof-of-Concepts hell
[31:30] Wholistic view
[34:00] Operationalizing PoCs
[37:17] "The teams also need to adjust themselves to these new tools, new paradigms, and the different needs of the whole industry."  
[37:26] Horror stories
[39:54] Open communication tips
43:31] "Open communication should not only be from the technical perspective but also down to the business and strategy perspective."
[44:20] Translation tips
[44:39] "I believe the most crucial part of today's ML scientists' role is not building a machine learning model but translating a real-life problem into a machine learning problem. It's crucial because it's a scarce talent and skill."
[49:30] Realistic budget for small PoCs
[50:18] "You need at least 1 month of work of proof of value, but that doesn't mean things will go to production."
[51:40] Understanding the questions fully
[52:55] "That translation skill is the greatest skill to have in this industry because you can't auto ML that or whatever. It stands the test of time because that will be needed all the time."

Coffee Sessions #38 with Adam Sroka of Origami Energy, Organisational Challenges of MLOps.

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// Abstract
Deploying data science solutions into production is challenging for both small and large organizations. From platform and tooling wars to architecture and design pattern trade-offs, it can get overwhelming for inexperienced teams. Furthermore, many organizations will only go through the painful discovery process once. Adam will share some of his experiences from consulting and leading data teams to successfully deploying machine learning solutions, highlighting some of the more difficult challenges to overcome. You might not be surprised to hear it’s not all down to the tech.

// Bio
Dr. Adam Sroka, Head of Machine Learning Engineering at Origami Energy, is an experienced data and AI leader helping organizations unlock value from data by delivering enterprise-scale solutions and building high-performing data and analytics teams from the ground up. Adam shares his thoughts and ideas through public speaking, tech community events, on his blog, and in his podcast.

--------------- ✌️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 Adam on LinkedIn: https://www.linkedin.com/in/aesroka/

Timestamps:
[00:00] Introduction to Adam Sroka
[01:53] Adam's background in tech
[08:06] 2 blog posts of Adam: Why So Many Data Scientists Quit Good Jobs at Great Companies and Why You Shouldn’t Hire More Data Scientists

[08:31] High turn rate Adam has in the data science role
[13:50] Avoiding hiring talents with deficits and coaching people
[16:05] "I can't teach you to care about the standard of your core of what you're doing. It's quite hard to teach people charisma. Everything else, you pick up."
[16:45] Resume-driven development, the idea of not playing the game, and politics in the workplace.
[17:57] "You have to realize, other people don't have the same experience in the context that you do."  
[19:59] Exit, Voice, Loyalty, and Neglect Model
[22:35] You probably don't need a data scientist
[23:40] "Data scientists can do everything slower and more expensively than everyone else, but they can do everything, and that's the important bit."
[27:54] "My success is just driven by who I am as much as what I can do." Vishnu
[28:24] Being Candor
[30:37] Disconnect between the senior stakeholders and data scientists
[32:30] "Before you come out to bring someone in an expensive talent search, engage with the consultancy. Do a four-week PRC, get them to tell you like."
[34:18] Educational experiences as a consultant
[37:35] Adam's journey into MLOps, productionize ML models when you are a data scientist, and tips
[43:16] "Beginners can help beginners. Your perspective is really important. The value is not in the content. The value is in your perspective of the content."
[45:21] Educating clients on uncertainty
[48:34] Decision-making process
[52:32] Organizational problems
[53:43] "All models are wrong, but some are useful." George Box

MLOps community meetup #61! Last Wednesday, we talked to Lex Beattie, Michael Munn, and Mike Moran.

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// Abstract
We started out talking about some of the main bottlenecks they have encountered over the years of trying to push data products into production environments. Then things started to heat up as we dove into the topic of monitoring ML, and inevitably, the word explainability started being thrown around.
Turns out Lex is currently doing a Ph.D. on the subject, so there was much to talk about. We had to ask if explainability is now table-stakes when it comes to monitoring solutions on the market? The short answer from the team. Yes!  

Please excuse the bit of sound trouble we had with Google Mike at the beginning.

// Bio
Lex Beattie - ML Engagement Lead, Spotify
In the last year, Lex has helped over 40 different teams across Spotify understand ML best practices, productionize ML workflows, and implement impactful ML in their products. Lex is also a Ph.D. candidate at the University of Oklahoma, focusing on feature importance and interpretability in deep neural networks. Beyond her passion for all things ML, she enjoys exploring the great outdoors in Montana with her German Wirehaired Pointer, Bridger.

Michael Munn - ML Solutions Engineer, Google
Michael is an ML Solutions Engineer with Google Cloud and Google's Advanced Solutions Lab. In his role, he works with customers to build and deploy end-to-end ML solutions with Google Cloud. Within the Advanced Solutions Lab, he teaches these skills to customers.
Mike Moran - Principal Engineer, Skyscanner
Mike has worked across many dimensions; in large/tiny companies, back-end/front-end, with many languages, and as a sys-admin /engineer/manager. Mike has a healthy skepticism for most things and likes solving problems through applying systems thinking.

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Lex on LinkedIn: https://www.linkedin.com/in/lexbeattie/
Connect with Michael on LinkedIn: https://www.linkedin.com/in/munnm/
Connect with Mike on LinkedIn: https://www.linkedin.com/in/mrmikemoran/

Timestamps:
[00:00] Introduction to Lex, Michael, & Mike
[02:46] Common roadblocks
[05:25] Consolidating knowledge
[07:02] Bottlenecks on failures
[09:58] Don't go on a detour
[12:22] Bringing on complexity signs
[19:33] Explainable AI
[21:34] "There are different ways to approach Explainable AI. It starts to get more complicated when you start working with more complicated models." Lex
[24:43] "If there are a lot of disparate sources out there about Explainability, I'd found myself hunting down various resources to simplify it for customers I'd worked with." Michael
[26:46] "Being clear about who you're explaining it to because in our context, sometimes the organization needs to explain it to a regulator." Mike [28:04] Monitoring solution
[31:00] ML Canvas
[33:24] Explainable AI Resources
[34:48] Explainable Predictions by Michael
[36:48] Purpose of Explainable Model
[39:40] Work in the same language
[42:46] Use of War Stories
[49:11] Hot seat!
[49:15] Mike - Skyscanner pricing
[50:30] Lex - Spotify recommendation sudden stop
[51:35] Michael - NLP models on emails

Coffee Sessions #37 with Ariel Biller of ClearML, MLOps Memes.

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// Abstract
The Meme king of MLOps joins us to talk about why we need more MLOps memes and how he got so damn good at being able to zoom out and see things from a metta level, then make a meme about it!

// Bio
A researcher first, developer second, in the last 5 years, Ariel worked on various projects from the realms of quantum chemistry, massively parallel supercomputing, and deep-learning computer vision. With AllegroAi, he helped build an open-source R&D platform (Allegro Trains), and later went on to lead a data-first transition for a revolutionary nanochemistry startup (StoreDot). Answering his calling to spread the word on state-of-the-art research best practices, He recently took up the mantle of Evangelist at ClearML. Ariel received his Ph.D. in Chemistry in 2014 from the Weizmann Institute of Science. With a broad experience in computational research, he made the transition to the bustling startup scene of Tel-Aviv and to cutting-edge Deep Learning research.

--------------- ✌️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 Ariel on LinkedIn: https://www.linkedin.com/in/LSTMeow/ 

// Related Links
https://youtu.be/1C_l5ICJlEo
https://youtu.be/yTtTrwXEhN4
https://youtu.be/F4Ghp-phFuI

Timestamps:
[00:00] Introduction to Ariel Biller
[01:20] Ariel's background
[03:40] Story behind Memeing
[06:36] "Memes can be as extreme as you want because people don't know if they're going to take you seriously or you're joking."
[07:21] MLOps memes and more
[10:15] MLOps fear
[13:00] MLOps is being more complicated than DevOps.
[13:10] "A meme material is a social commentary about what there is and what there is now."
[16:00] Standardization
[18:18] "Would we have MLOps' code in a sweeping way or not?"
[18:26] "I'm not sure as a community of builders, we have the right perspective that will work for all the cases."
[20:26] Journey into evangelism
[26:45] "Feature stores are a big meme."
[27:08] "Memeing is like a muscle. If you flex it daily, it creates tensions."
[31:26] We need to de-jargonize MLOps and ML engineering
[35:55] Current Israeli tech scene
[39:16] "The deficit is that there's a limited number of people doing MLOps right now."
[43:14] Tooling space
[46:57] "Concentrate on the basic stuff that will survive forever, and if you need to reach out for a tool, don't reach out for a tool, reach out for obstruction."
[51:47] Standardization of ID Tree
[52:43] "Everybody is doing whatever they want because it works for them. Someday, someone will come out with some good obstruction and a good toolchain that works across the board that will click for everyone and will use it from that time on."
[55:20] Ecosystem support

Coffee Sessions #36 with Luigi Patruno of 2U, Luigi in Production Part 2.   

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// Abstract
Learning Voraciously: We talk a lot in the community about how to learn and upskill in an efficient way. Luigi provided great insight into how he applies certain principles to his learning practices. One tip he shared is to rigorously read and digest books. Luigi himself has used books to address his knowledge gaps in areas like product, finance, etc. I appreciated the emphasis on books. A lot of the reason we feel inundated by new learning resources is that they are online. Emphasizing books, which are often far higher-quality than blog posts, can slow things down and focus our learning.  

Leadership Patience: Lately, Luigi has been spending more time managing projects and the data science team at 2U. He shared a lot of his insights into how to manage data science and machine learning properly. One of the most important things he emphasized to us was his patient attitude towards solving problems important to leadership. Turning around organizations is hard work. It's slow, it takes energy, and it is a nonlinear process. As he has course-corrected at various times as a data science leader, Luigi has brought admirable patience to the task, which has helped him be more successful on the things that matter to the entire company.  

Communication Flows: It's easy to imagine Luigi as a great communicator, given his experience running MLInProduction.com. In our conversation, he showed us how he puts it to use in his management style. Luigi shared the importance of understanding how communication flows across an organization. Being aware of this is crucial to working on the right, most impactful things. Having a pulse on what different groups and leaders are thinking about can help you evaluate your impact as a team.

// Bio
Luigi Patruno is a Data Scientist focused on helping companies utilize machine learning to create competitive advantages for their business. As the Director of Data Science at 2U, Luigi leads the development of machine learning models and MLOps infrastructure for predicting student success outcomes across 2U’s portfolio of university partners. As the Founder of MLinProduction.com, Luigi creates and curates content to educate machine learning practitioners about best practices for running resilient machine learning systems in production. Luigi has consulted on data science and machine learning at Fortune 500 companies and start-ups and has taught graduate-level courses in Statistics and Big Data Engineering. He has an M.S. in Computer Science and a B.S. in Mathematics.

--------------- ✌️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 Luigi on LinkedIn: https://www.linkedin.com/in/luigipatruno91/

Timestamps:
[00:00] Introduction to Luigi Patruno
[01:12] Update about Luigi
[04:08] Luigi's transition
[07:18] Problem-focused
[11:00] New problem
[12:51] Rational platform strategy
[18:18] Bringing the learnings to the team
[20:57] Formulating and communicating vision
[25:40] Problem-driven mindset
[35:53] Organizational blind spots
[41:12] Continuous learning  
[42:46] "Default to reading."
[44:44] The Lindy effect
[46:20] "You'll fail less often on the easy problems."  
[46:25] Act upon reading
[51:48] Ethical implications of ML
[53:24] Wrap up

Coffee Sessions #35 with Nick Masca of Marks and Spencer, War Stories Productionising ML.

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// Abstract
A conversation with MLOps war stories. Better said, a war story conversation. The kind that informs modern MLOps best practices.  

Nick shared how to make MLOps organizational changes at large companies. I loved one tidbit he mentioned--"it's an evolution, not a revolution". That's a frank observation about the speed of practical change. As we all know, it doesn't happen overnight.  

Another great learning Nick shared focused on the value of delivering incremental results regularly. Oftentimes, ML projects suffer because of a focus on delivering too much too soon. This can then lead to a trough of disappointment with the way things actually pan out. Nick shared his experience on how to avoid such pitfalls with us, so you don't have to learn the hard way.

// Bio
Nick currently serves as a Head of Data Science at Marks and Spencer, a large retailer based in the UK.  With a background originally in statistics, he transitioned into data science in 2014 and has picked up many battle scars and learnings since.

//Link to the MLOps War Stories
https://www.linkedin.com/posts/dpbrinkm_what-is-your-mlops-war-story-activity-6772604800971370496-LxtX

--------------- ✌️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 Nick on LinkedIn: www.linkedin.com/in/nick-masca-09454956/

Timestamps:
[00:00] Introduction to Nick Masca
[01:36] Nick's background in tech
[05:01] Nick's current job
[06:19] Building the basics
[08:18] "If you can gain trust and demonstrate value early, you could also freeze yourself up to the tidy marks later."
[09:19] Strategy on long-running vision
[10:25] "Historically, the legacy waterfall processes in the business where teams have specialist responsibilities."
[11:14] KPI's
[12:36] KPI translations into action plans
[15:43] Data scientists call
[17:13] Nick's nightmarish story  
[22:52] Making the case on such a nightmarish story
[25:06] Tools used by Marks and Spencer in 2015  
[27:15] More complicated process
[28:08] Takeaways from experience
[30:57] Obstacles in deploying
[34:53] Simplifying models
[37:31] Combining environments into one
[38:45] "Having written standards can be quite helpful to take ownership and responsibility around that."  
[40:23] M&S team interaction
[41:31] "It's an evolution, it's not a revolution, I'd say at the moment, but there's definitely real emphasis where we are to improve things and work towards goals to enable our team to work quicker, empower them."
[42:10] Team moralizing
[43:11] Takeaways from war stories
[43:30] "The biggest takeaway for me is to start small, keep things simple, try things, and it can be surprising sometimes what you'll find. Something simple can give you surprising results."
[44:35] Opinions on Data Science and Machine Learning businesses democratize and commoditize

MLOps community meetup #60! Last Wednesday, we talked to Vishnu Prathish, Director of Engineering, AI Products, Innovyze.

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

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// Abstract
The way Data Science is done is changing. Notebook sharing and collaboration were messy, and there was minimal visibility or QA into the model deployment process. Vishnu will talk about building an ops platform that deploys hundreds of models at scale every month. A platform that supports typical features of MLOps (CI/CD, Separated QA, Dev, and PROD environment, experiments tracking, Isolated retraining, model monitoring in real-time, Automatic Retraining with live data) and ensures quality and observability without compromising the collaborative nature of data science.

// Bio
With 10 years in building production-grade data-first software at BBM & HP Labs, I started building Emagin's AI platform about three years ago with the goal of optimizing operations for the water industry. At Innovyze post-acquisition, we are part of the org building a world-leading water infrastructure data analytics product.

//Takeaways
Why is MLOps necessary for model building at scale?  
What are various cloud-based models for MLOps?  
Where can ops help in various points in the ML pipeline: Data Prep, Feature Engineering, Model building, Training, Retraining, Evaluation, and inference

----------- 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/vishnuprathish/

Timestamps:
[00:00] Introduction to Vishnu Prathish
[00:16] Vishnu's background
[04:18] Use cases on wooden pipes for freshwater
[04:55] Virtual representation of actual, physical, tangible assets
[06:56] Platform built by Vishnu
[08:30] Build a reliable representation of the network
[11:52] Pipeline architecture
[16:17] "MLOps is still an evolving discipline. You need to try and fail many times before you figure out what's right for you."
[17:11] Open-sourcing
[18:17] Platform for virtual twin
[20:02] Entirely Amazon Stagemaker
[20:43] Data quality issues
[23:21] Reproducibility
[23:40] "Reproducibility is important for everybody. Most of the frameworks do that for you."
[25:00] Reproducibility as Innovyze's core business.
[26:38] Each model is individual to each customer
[27:50] Solving reproducibility problems
[28:24] "Reproducibility applies to the process of training pipelines. It starts with collecting historical raw data from customers. In real-time, there's also this data being collected directly from sensors coming from a certain pipeline."
[31:55] "Reusable training is step one to attaining automated retraining."
[32:17] Collaboration of Vishnu's team
[36:23] War stories
[41:36] Data prediction
[44:24] "A data scientist is the most expensive hire you can make."
[47:55] 3 Tiers
[48:53] MLOps problems
[52:25] Automatically retraining
[52:34] "Because of the number of models that go through this pipeline, it's impossible for somebody to manually monitor and retrain as necessary. It's not easy, it takes a lot of time."
[54:22] Metrics on retraining
[56:42] "Retraining is a little less prevalent for our industry compared to a turned prediction model that changes a lot. There are external factors that depend on it, but a pump is a pump."

Coffee Sessions #34 with Geoff Sims of Atlassian, Machine Learning at Atlassian.

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

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// Abstract
As one of the world's most visible software companies, Atlassian's vast data and deep product suite pose an interesting MLOps challenge, and we're grateful to Geoff for taking us behind the curtain.

//Bio
Geoff is a Principal Data Scientist at Atlassian, the software company behind Jira, Confluence & Trello. He works with the product teams and focuses on delivering smarter in-product experiences and recommendations to our millions of active users by using machine learning at scale. Prior to this, he was in the Customer Support & Success division, leveraging a range of NLP techniques to automate and scale the support function.

Prior to Atlassian, Geoff has applied data science methodologies across the retail, banking, media,  and renewable energy industries. He began his foray into data science as a research astrophysicist, where he studied astronomy from the coldest & driest location on Earth: Antarctica.

--------------- ✌️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 Geoff on https://www.linkedin.com/in/geoff-sims-0a37999b/

Timestamps:
[00:00] Introduction to Geoff Sims
[01:20] Geoff's background
[04:00] Evolution of ML Ecosystem in Atlassian
[06:50] Figure out by necessity
[08:47] Machine Learning is not priority number one and disconnected from MLOps
[11:53] Atlassian being behind or advanced?
[16:38] Serious switch of Atlassian around machine learning
[17:47] What data org did it come from?
[20:00] Consolidation of the stack
[21:21] Tooling - blessing and curse
[24:37] Tackling play out
[29:38] Staying on the same page
[30:48] Priority of needs
[31:55] How did it evolve?
[35:12] Where is Atlassian now?
[40:21] "Architecturally, Tecton is very, very similar (to ours), it was just way more mature."
[41:17] What unleashed you to do now?
[41:36] "The biggest thing is independence from a data science perspective. Less reliance and less dependence on an army of engineers to help deploy features and models."
[44:25] Have you bought other tools?
[45:43] "At any given time, there's something that's a bottleneck. Look where the bottleneck is, then fix it and move on to the next thing."  
[48:20] Atlassian is bringing a model into production
[50:01] "When we undertake whatever the project is, it's days or weeks to go to a prototype rather than months or quarters."
[53:10] "Conceptually, you're struggling walking towards that place because that's the place you want to be. If that's your problem, that's good. That's the promised land."
[54:45] "Using our own tools is paramount because we are customers as well. So we see and feel the pain, which helps us identify the problems and understand them."

MLOps community meetup #59! Last Wednesday was the celebration of the MLOps Community's 1 Year Anniversary! This has been a conversation of Demetrios Brinkmann, David Aponte, and Vishnu Rachkonda!

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

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//Abstract
Over the past year, Demetrios, David, and Vishnu have interviewed many of the top names in MLOps. During this time, they have been able to apply these learnings at their jobs and see what works for them. In this one-year anniversary meetup, the three of them will discuss some of the most impactful advice they have received in the last year and how they have put them into practice.

//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.

David Aponte
David is one of the organizers of the MLOps Community. He is an engineer, teacher, and lifelong student. He loves to build solutions to tough problems and share his learnings with others. He works out of NYC and loves to hike and box for fun. He enjoys meeting new people, so feel free to reach out to him!

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 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 David on LinkedIn: https://www.linkedin.com/in/aponteanalytics/
Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/

Timestamps:
[01:07] Big shoutout to everybody that's in these meetups!
[02:03] Big shoutout to Ivan Nardini for leading the Engineering Labs and to everyone who took part in the Engineering Labs!
[02:26] Big shoutout to Charlie, you're leading the Reading Group, and to everyone who takes part in it!
[02:39] Big shoutout to everyone who takes part in the Office Hours!
[02:49] Big shoutout to the people who are helping with shaping the website!
[03:34] Thanks to all the people in Slack! Laszlo, Ariel, and people answering Slack questions.
[04:10] Big thanks to all our Sponsors FiddlerAI, Algorithmia, and Tecton!  
[06:13] David's Background
[08:08] Vishnu's Background
[09:55] High-Level Points
[15:57] Starting small
[24:05] Over-optimization - the root of all evil
[26:42] Keeping text deck open
[36:45] Missing from current MLOps tooling
[48:00] How to communicate in these data products?