Coffee Sessions #32 with D. Sculley of Google, The Godfather Of MLOps. //Bio D is currently a director in Google Brain, leading research teams working on robust, responsible, reliable and efficient ML and AI. In his time at Google, D worked on nearly every aspect of machine learning, and have led both product and research teams including those on some of the most challenging business problems. // Links to D. Sculley's Papers ML Test Score: https://research.google/pubs/pub46555/ Machine Learning:...
Mar 23, 2021•52 min•Season 1Ep. 32
MLOps community meetup #56! Last Wednesday we talked to Daniel Stahl, Head of Data and Analytic Platforms, Regions Bank. // Abstract: The Data Science practice has evolved significantly at Regions, with a corresponding need to scale and operationalize machine learning models. Additionally, highly regulated industries such as finance require a heightened focus on reproducibility, documentation, and model controls. In this session with Daniel Stahl, we will discuss how the Regions team...
Mar 19, 2021•1 hr 5 min•Season 1Ep. 56
MLOps community meetup #55! Last Wednesday we talked to Igor Lushchyk, Data Engineer, Adyen. // Abstract: Building Data Science and Machine Learning platforms at a scale-up. Having the main difficulty in finding correct processes and basically being a toddler who learns how to walk on a steep staircase. The transition from homegrown platform to open source solutions, supporting old solutions and maturing them with making data scientists happy. // Bio: Igor is a software engineer wi...
Mar 12, 2021•58 min•Season 1Ep. 55
MLOps community meetup #54! Last Wednesday we talked to Laszlo Sragner, Founder, Hypergolic. // Abstract: How my experience in quant finance and software engineering influenced how we ran ML at a London Fintech Startup. How to solve business problems with incremental ML? What's the difference between academic and industrial ML? // Bio: Laszlo worked as a quant researcher at multiple investment managers and as a DS at the world's largest mobile gaming company. As Head of Data Science at Arkera, h...
Mar 05, 2021•58 min•Season 1Ep. 54
This is a deep dive into the most recent MLOps Engineering Labs from the point of view of Team 3. // Diagram Link: https://github.com/dmangonakis/mlops-lab-example-yelp --------------- ✌️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 Las...
Mar 02, 2021•1 hr 4 min•Season 1Ep. 31
MLOps community meetup #53! Last Wednesday we talked to Krishna Gade, CEO & Co-Founder, Fiddler AI. // Abstract: Training and deploying ML models have become relatively fast and cheap, but with the rise of ML use cases, more companies and practitioners face the challenge of building “Responsible AI.” One of the barriers they encounter is increasing transparency across the entire AI lifecycle to not only better understand predictions, but also to find problem drivers. In this session with Kri...
Mar 01, 2021•57 min•Season 1Ep. 53
This is a deep dive into the most recent MLOps Engineering Labs from the point of view of Team 1. // Diagram Link: https://github.com/mlops-labs-team1/engineering.labs#workflow --------------- ✌️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 Alexey on LinkedIn: htt...
Feb 23, 2021•1 hr•Season 1Ep. 30
MLOps community meetup #52! Last Wednesday we talked to Luke Feeney and Gavin Mendel-Gleason, TerminusDB. // Abstract: A look at the open-source 'Git for Data' landscape with a focus on how the various tools fit into the pipeline. Following that scene-setting, we will delve into how and why TerminusDB builds a revision control database from the ground up. // Takeaways - Understanding the 'git for data' offering and landscape - See how to technically approach a revision control database implement...
Feb 19, 2021•58 min•Season 1Ep. 52
MLOps community meetup #51! Last Wednesday we talked to Pamela Jasper, AI Ethicist, Founder, Jasper Consulting Inc. // Abstract: One of the challenges to the widespread adoption of AI Ethics is not only its integration with MLOps, but the added processes to embed ethical principles will slow and impede Innovation. I will discuss ways in which DS and ML teams can adopt Agile practices for Responsible AI. // Bio: Pamela M. Jasper, PMP is a global financial services technology leader with over 30 y...
Feb 12, 2021•1 hr 7 min•Season 1Ep. 51
Coffee Sessions #29 with Jet Basrawi of Satalia, Culture and Architecture in MLOps. //Bio Jet started his career in technology as a game designer but became interested in programming. He found he loved it. It was endlessly challenging and deeply enjoyable "Flow" activity. It was also nice to be in demand and earn a living. In the last several years, Jet been passionate about DevOps as a key strategic practice. About a year ago, he came into the AI world and it is a great place to be for someone ...
Feb 08, 2021•54 min•Season 1Ep. 29
MLOps community meetup #50! Last Wednesday we talked to Michael Del Balso, Willem Pienaar and David Aronchick, // Abstract: The MLOps tooling landscape is confusing. There’s a complicated patchwork of products and open-source software that each cover some subset of the infrastructure requirements to get ML to production. In this session - we’ll focus on the two most important platforms: model management platforms and feature stores. Model management platforms such as Kubeflow help you get models...
Feb 05, 2021•57 min•Season 1Ep. 50
MLOps community meetup #49! Last Wednesday we talked to Lak Lakshmanan, Data Analytics and AI Solutions, Google Cloud. // Abstract: Design patterns are formalized best practices to solve common problems when designing a software system. As machine learning moves from being a research discipline to a software one, it is useful to catalogue tried-and-proven methods to help engineers tackle frequently occurring problems that crop up during the ML process. In this talk, I will cover five patterns (W...
Feb 02, 2021•57 min•Season 1Ep. 49
Coffee Sessions #28 with Charlie You of Workday, Lessons learned from hosting the Machine Learning Engineered podcast //Bio Charlie You is a Machine Learning Engineer at Workday and the host of ML Engineered, a long-form interview podcast aiming to help listeners bring AI out of the lab and into products that people love. He holds a B.S. in Computer Science from Rensselaer Polytechnic Institute and previously worked for AWS AI. Charlie is currently working as a Machine Learning Engineer at Workd...
Jan 29, 2021•1 hr 5 min•Season 1Ep. 28
Coffee Sessions #27 with Noah Gift of Pragmatic AI Labs, Practical MLOps // A “Gift” from Above This week, Demetrios and Vishnu got to spend time with inimitable Noah Gift. Noah is a data science educator, who teaches at Duke, Northwestern, and many other universities, as well as a technical leader through his company Pragmatic AI Labs and past companies. His bio alone would take up this section of the newsletter, so we invite you to check it out here, as well as the rest of his educationa...
Jan 26, 2021•59 min•Season 1Ep. 27
MLOps community meetup #48! Last Wednesday, we talked to Manoj Agarwal, Software Architect at Salesforce. // Abstract: Serving machine learning models is a scalability challenge at many companies. Most applications require a small number of machine learning models (often < 100) to serve predictions. On the other hand, cloud platforms that support model serving, though they support hundreds of thousands of models, provision separate hardware for different customers. Salesforce has a uniq...
Jan 24, 2021•56 min•Season 1Ep. 48
**Private data, Data Science friendly** Data Scientists are always eager to get their hands on more data, in particular, if that data has any value that can be extracted. Nevertheless, in real-world situations, data does not exist in the abundance that we thought existed, in other situations, the data might exist, but not possible to share it with different entities due to privacy concerns, which makes the work of data scientists not only hard, but sometimes even impossible. // Abstract: In the ...
Jan 21, 2021•42 min•Season 1Ep. 9
Coffee Sessions #26 with Vishnu Rachakonda of Tesseract Health, Daniel Galinkin of iFood, Matias Dominguez of Rappi & Simarpal Khaira of Intuit, Feature Store Master Class. //Bio Vishnu Rachakonda Machine Learning Engineer at Tesseract Health. Coffee sessions co-host but this time his role is one of the all-stars guest speakers. Daniel Galinkin One of the co-founders of Hekima, one of the first companies in Brazil to work with big data and data science, with over 10 years of experience in th...
Jan 19, 2021•1 hr 6 min•Season 1Ep. 26
MLOps community meetup #47! Last Wednesday, we talked to Adrià Romero, Founder and Instructor at ProductizeML. // Abstract: In this talk, we tackled: - Motivations and mission behind ProductizeML. - Common friction points and miscommunication between technical and management/product teams, and how to bridge these gaps. - How to define ML product roadmaps, (and more importantly, how to get it signed off by all your team). - Best practices when managing the end-to-end ML lifecyc...
Jan 18, 2021•51 min•Season 1Ep. 47
The revolution of Federated Learning - And we're back with another episode of the podcast When Machine Learning meets Privacy! For the episode #8 we've invited Ramen Dutta, a member of our community and founder of TensoAI. // Abstract: In this episode, Ramen explain us the concept behind Federated Learning, all the amazing benefits and it's applications in different industries, particularly in agriculture. It's all about not centralizing the data, sound awkward? Just listen to the episode....
Jan 14, 2021•29 min•Season 1Ep. 8
Coffee Sessions #25 with Marian Ignev of CloudStrap.io & SashiDo.io, Most Underrated MLOps Topics. //Bio Marian a passionate entrepreneur, backend dude & visionary. These are the three main things described to Marian very well: Marian's everyday routines include making things happen and motivating people to work hard and learn all the time because I think success is a marathon, not just a sprint! Marian loves to communicate with bright creative minds who want to change things. His...
Jan 12, 2021•54 min•Season 1Ep. 25
MLOps community meetup #46! Last Wednesday, we talked to Hendrik Brackmann, Director of Data Science and Analytics at Tide. // Abstract: Tide is a U.K.-based FinTech startup with offices in London, Sofia, and Hyderabad. It is one of the first, and the largest business banking platform in the UK, with over 150,000 SME members. As of 2019, one of Tide’s main focuses is to be data-driven. This resulted in the forming of a Data Science and Analytics Team with Hendrik Brackmann at its head. Let's wit...
Jan 08, 2021•58 min•Season 1Ep. 46
Coffee Sessions #24 with Sara Robinson of Google, Machine Learning Design Patterns co-hosted by Vishnu Rachakonda. //Bio Sara is a Developer Advocate for Google Cloud, focusing on machine learning. She inspires developers and data scientists to integrate ML into their applications through demos, online content, and events. Before Google, she was a Developer Advocate on the Firebase team. Sara has a Bachelor’s degree from Brandeis University. When she’s not writing code, she can be found on a spi...
Dec 29, 2020•1 hr•Season 1Ep. 24
Coffee Sessions #23 with Todd Underwood of Google, Followups from OPML Talks on ML Pipeline Reliability co-hosted by Vishnu Rachakonda. //Bio Todd is a Director at Google and leads Machine Learning for Site Reliability Engineering Director. He is also Site Lead for Google’s Pittsburgh office. ML SRE teams build and scale internal and external ML services and are critical to almost every Product Area at Google. Before working at Google, Todd held a variety of roles at Renesys. He was in cha...
Dec 22, 2020•1 hr 12 min•Season 1Ep. 23
MLOps community meetup #45! Last Wednesday, we talked to Joe Reis, CEO/Co-Founder of Ternary Data. // Abstract: The fact is that most companies are barely doing BI, let alone AI. Joe discussed ways for companies to build a solid data foundation so they can succeed with machine learning. This meetup covers the continuum from cloud data warehousing to MLOps. // Bio: Joe is a Data Engineer and Architect, Recovering Data Scientist, 20 years in the data game. Joe enjoys helping companies make s...
Dec 20, 2020•54 min•Season 1Ep. 45
Coffee Sessions #22 with Carl Steinbach of LinkedIn, Deep in the Heart of Data. //Bio Carl is a Senior Staff Software Engineer and currently the Tech Lead for LinkedIn's Grid Development Team. He is a contributor to Emerging Architectures for Modern Data Infrastructure //Other links referenced by Carl: https://rise.cs.berkeley.edu/wp-content/uploads/2017/03/CIDR17.pdf https://www.youtube.com/watch?v=-xIai_FvcSk&ab_channel=WePayEngineering https://softwareengineeringdaily.com/2019/10/23/linke...
Dec 18, 2020•56 min•Season 1Ep. 22
ML and Encryption - It's all about secure insights #7! In this episode, we've invited Théo Ryffel, Founder of Arkhn and founding member of the Open-Mined community. // Abstract: In this episode, Théo introduces us to the concept of encrypted Machine Learning, when and the best practices to have it applied in the development of Machine Learning based solutions, and the challenges of building a community. //Other links to check on Théo: https://twitter.com/theoryffel https://arkh...
Dec 17, 2020•36 min•Season 1Ep. 7
**Privacy-preserving ML with Differential Privacy** Differential privacy is without a question one of the most innovative concepts that came around in the last decades, with a variety of different applications even when it comes to Machine Learning. Many are organizations already leveraging this technology to access and make sense of their most sensitive data, but what is it? How does it work? And how can we leverage it the most? To explain this and provide us a brief intro on Differential Priva...
Dec 14, 2020•36 min•Season 1Ep. 6
MLOps community meetup #44! Last Wednesday, we talked to Savin Goyal, Tech lead for the ML Infra team at Netflix. // Abstract: In this conversation, Savin talked about some of the challenges encountered and choices made by the Netflix ML Infrastructure team while developing tooling for data scientists. // Bio: Savin is an engineer on the ML Infrastructure team at Netflix. He focuses on building generalizable infrastructure to accelerate the impact of data science at Netflix. // Other links to ch...
Dec 14, 2020•56 min•Season 1Ep. 44
Coffee Sessions #21 with Benjamin Rogojan of Seattle Data Guy, A Conversation with Seattle Data Guy //Bio Ben has spent his career focused on all forms of data. He has focused on developing algorithms to detect fraud, reduce patient readmission and redesign insurance provider policy to help reduce the overall cost of healthcare. He has also helped develop analytics for marketing and IT operations in order to optimize limited resources such as employees and budget. Ben privately consults on data ...
Dec 08, 2020•47 min•Season 1Ep. 21
Coffee Sessions #20 with Neal Lathia of Monzo Bank, talking about Monzo Bank - An MLOps Case Study //Bio Neal is currently the Machine Learning Lead at Monzo in London, where his team focuses on building machine learning systems that optimise the app and help the company scale. Neal's work has always focused on applications that use machine learning - this has taken him from recommender systems to urban computing and travel information systems, digital health monitoring, smartphone sensors, and ...
Dec 07, 2020•1 hr 4 min•Season 1Ep. 20
