MLOps Coffee Sessions #133 {Podcast BTS} with Chip Huyen, Real-time Machine Learning with Chip Huyen co-hosted by Vishnu Rachakonda. // Abstract Forcing functions and how you can supercharge your learning by putting yourself into a situation where you know you either have a responsibility to others to learn or accountability on you so you have to learn. It's not that hard when you think about streaming machine learning. It's not that big of a mental barrier to cross. It is simple in theor...
Nov 22, 2022•59 min•Season 1Ep. 133
MLOps Coffee Sessions #132 {Podcast BTS} with Ian Schweer, What is Data / ML Like on League? co-hosted by Skylar Payne. // Abstract If you're not an avid gamer yourself, you have never really seen how ML might be used in the gaming space. It's so interesting to see the things that are different like full stories of players' games from start to finish. // Bio On the surface, Ian is an excellent developer who gets things done. Underneath, he is much more. Ian is a reliable and trustw...
Nov 15, 2022•1 hr 1 min•Season 1Ep. 132
MLOps Coffee Sessions #131 {Podcast BTS} with Ethan Rosenthal, Let's Continue Bundling into the Database co-hosted by Mike Del Balso. // Abstract The relationship between ML Engineers and Product Managers is something that we don't talk about enough. We've got to get this right. If we don't get this right, either you're not focusing on the business problems in the right way or the Product Managers are not going to understand the tech appropriately to help make the right decisions. // Bio Ethan w...
Nov 08, 2022•52 min•Season 1Ep. 131
MLOps Coffee Sessions #130 {Podcast BTS} with Andrew Yates, Adversarial MLOps on Other People's Money: Lessons Learned from Ad Tech co-hosted by Abi Aryan. // Abstract Design ML to be components in a larger system with stable interfaces is not tracible to monitor the entire stack as a black box. You need intermediate ground-truth signals. Ads are designed in this way. You can go from profitable to non-profitable real quick with ads. This will determine whether your company is around a year or tw...
Oct 31, 2022•45 min•Season 1Ep. 130
MLOps Coffee Sessions #129 {Podcast BTS} with Catherin Breslin, Voice and Language Tech co-hosted by Adam Sroka. // Abstract Back in the day, Speech Recognition was its own thing. It's a very different flavor of Data Science. You could not use a lot of the tools. It wouldn't cross over to this type of machine learning. Now, with the advancements, Speech Recognition and Machine learning are coming in together. It's interesting to hear right from someone with a Ph.D. level working with some of the...
Oct 21, 2022•50 min•Season 1Ep. 129
MLOps Coffee Sessions #128 with Simon Thompson, Managing Machine Learning Projects co-hosted by Abi Aryan. // Abstract It's a cliche to say that choosing and running the algorithms is only a small part of a typical ML project but despite that it's true! Setting up and organizing the project, dealing with the data asset, getting to the heart of the business problem, assessing and choosing the models, and integrating them with the business processes in production are all at least as time-consuming...
Oct 19, 2022•45 min•Season 1Ep. 128
MLOps Coffee Sessions #127 with Niall Murphy & Todd Underwood, Reliable ML co-hosted by David Aponte. // Abstract By applying an SRE mindset to machine learning, authors and engineering professionals Cathy Chen, Kranti Parisa, Niall Richard Murphy, D. Sculley, Todd Underwood, and featured guest authors show you how to run an efficient and reliable ML system. Whether you want to increase revenue, optimize decision-making, solve problems, or understand and influence customer behavior, you'll l...
Oct 07, 2022•1 hr 2 min•Season 1Ep. 127
MLOps Coffee Sessions #126 with George Mathew, ML Unicorn Start-up Investor Tells-IT-All. // Abstract What's so enticing about enterprise software? It's incredible to see George's idea and vision to invest in generationally enduring companies. Let's look at the way how George likes to structure deals with companies while he's reviewing them and let's look at the MLOps ecosystem through the eyes of the investors. // Bio George Mathew joins Insight Partners as a Managing Director focused on...
Oct 04, 2022•51 min•Season 1Ep. 126
MLOps Coffee Sessions #125 with Rafael Pierre, Deploying Real-time ML Models in Minutes with Databricks Model Serving V2 co-hosted by Ryan Russon. // Abstract From our experience helping customers in the Data and AI field, we learned that the most challenging part of Machine Learning is deploying it. Putting models into production is complex and requires additional pieces of infrastructure as well as specialized people to take care of it - this is especially true if we are talking about real-tim...
Sep 30, 2022•55 min•Season 1Ep. 125
Lightning Sessions #2 with Aparna Dhinakaran, Co-Founder and Chief Product Officer, and Jason Lopatecki, CEO and Co-Founder of Arize. Lightning Sessions is sponsored by Arize // Abstract Monitoring embeddings on unstructured data is not an easy feat let's be honest. Most of us know what it is but don't understand it one hundred percent. Thanks to Aparna and Jason of Arize for breaking down embedding so clearly. At the end of this Lightning talk, we get to see a demo of how Arize de...
Sep 27, 2022•11 min•Season 1Ep. 2
MLOps Coffee Sessions #124 with Kush Varshney, Distinguished Research Staff Member and Manager IBM Research, Trustworthy Machine Learning co-hosted by Krishnaram Kenthapadi. // Abstract Trustworthy ML is a way of thinking and something to be worked on and operationalized throughout the entire machine learning development lifecycle, starting from the problem specification phase that should include diverse stakeholders. // Bio Kush R. Varshney was born in Syracuse, New York in 1982. He received a ...
Sep 21, 2022•59 min•Season 1Ep. 124
MLOps Coffee Sessions #123 with Gleb Abroskin, Machine Learning Engineer at Funcorp, RECOMMENDER SYSTEM: Why They Update Models 100 Times a Day co-hosted by Jake Noble. // Abstract FunCorp was a top 10 app store. It was a very popular app that has a ton of downloads and just memes. They need a recommendation system on top of that. Memes are super tricky because they're user-generated and they evolve very quickly. They're going to live and die by the Recommender System in that product. It's incre...
Sep 16, 2022•52 min•Season 1Ep. 123
MLOps Coffee Sessions #122 with Hannes Hapke, Machine Learning Engineer at Digits Financial, Inc., Scaling Similarity Learning at Digits co-hosted by Vishnu Rachakonda. // Abstract Machine Learning in a product is a double-edged sword. It can make a product more useful but it depends on assumed and strictly defined behavior from users. Hannes walks through the entirety of their machine learning pipeline, how they implemented it, what the elements are, what the learning looks like, and wha...
Sep 09, 2022•57 min•Season 1Ep. 122
MLOps Coffee Sessions #121 with Luis Ceze, CEO and Co-founder of OctoML, Bringing DevOps Agility to ML co-hosted by Mihail Eric. // Abstract There's something about this idea where people see a future where you don't need to think about infrastructure. You should just be able to do what you do and infrastructure happens. People understand that there is a lot of complexity underneath the hood and most data scientists or machine learning engineers start deploying things and shouldn't...
Sep 06, 2022•1 hr 5 min•Season 1Ep. 121
MLOps Coffee Sessions #120 with David Stein, Senior Staff Software Engineer at LinkedIn, Feathr: LinkedIn's Enterprise-Grade, High-Performance Feature Store co-hosted by Skylar Payne. // Abstract When David started building Feathr, Feature Stores did not exist. That was not a term floating around at all. This was definitely one of the OG Feature Stores for sure! We hear how the LinkedIn team got to this point, having an open source release, and how they used LinkedIn as an incubator to bu...
Sep 02, 2022•54 min•Season 1Ep. 120
MLOps Coffee Sessions #119 with Hien Luu, Sr. Engineering Manager of DoorDash, MLOps at DoorDash: 3 Principles for Building an ML Platform That Will Sustain Hypergrowth co-hosted by Skylar Payne. // Abstract Machine Learning plays a big part at DoorDash in terms of what they do on a daily basis. It powers many of their core infrastructures. When it comes to DoorDash's business, they have to be leveraging machine learning and it is such a huge piece of the business that it is critical. // ...
Aug 30, 2022•46 min•Season 1Ep. 119
MLOps Coffee Sessions #118 with Olalekan Elesin, Director of Data Platform & Data Architect at HRS Product Solutions GmbH, co-hosted by Vishnu Rachkonda. // Abstract You don't have infinite resources? Call out your main metrics! Focus on the most impactful things that you could do for your data scientists. Olalekan joined us to talk about his experience previously building a machine learning platform at Scaleout24. From our standpoint, this is the best demonstration and explanat...
Aug 26, 2022•53 min•Season 1Ep. 118
MLOps Coffee Sessions #117 with Chad Sanderson, Head of Product, Data Platform at Convoy, Data Engineering for ML co-hosted by Josh Wills. // Abstract Data modeling is building relationships between core concepts within your data. The physical data model shows how the relationships manifest in your data environment but then there's the semantic data model, the way that entity relationship design is extracted away from any data-centric implementation. Let's do the good old fun of talking a...
Aug 19, 2022•58 min•Season 1Ep. 117
MLOps Coffee Sessions #116 with Shawn Kyzer, Principal Data Engineer at Thoughtworks (Spain), Scaling Machine Learning with Data Mesh co-hosted by Adam Sroka. // Abstract You can't just get something done by using tools. You need to go much deeper than that and it is very clear that Data Mesh is the same thing. You have to educate the organization about the movement. In this session, Shawn broke down the cultural piece of data mesh and how many parallels there are with the MLOps Movement ...
Aug 17, 2022•54 min•Season 1Ep. 116
MLOps Coffee Sessions #115 with Flaviu Vadan, Senior Software Engineer at Dyno Therapeutics, How Hera is an Enabler of MLOps Integrations co-hosted by Vishnu Rachakonda. // Abstract Flaviu talks about the internal ML platform at Dyno Therapeutics called Hera. His team uses Hera as an internal innovation engine to help discover new breakthroughs with machine learning in the biotech healthcare industry. / Bio Flaviu is a Senior Software Engineer at Dyno Therapeutics, the leading organization in th...
Aug 14, 2022•42 min•Season 1Ep. 115
MLOps Coffee Sessions #114 with Marc Lindner, Co-Founder COO and Amr Mashlah, Head of Data Science of eezylife Inc., Product Enrichment and Recommender Systems co-hosted by Skylar Payne. // Abstract The difficulties of making multi-modal recommender systems. How it can be easy to know something about a user but very hard to know the same thing about a product and vice versa? For example, you can clearly know that a user wants an intellectual movie, but it is hard to accurately classify a movie a...
Aug 10, 2022•57 min•Season 1Ep. 114
MLOps Coffee Sessions #113 with Leanne Fitzpatrick, Director of Data Science of Financial Times, Building Better Data Teams co-hosted by Mihail Eric. // Abstract We spent a lot of time talking about data tooling but we maybe spent not as much time talking about data organizations and efficiently running and organizing data teams. What about starting with limitations instead of aspirations? Right constraints instead of the north star? In this session, let's learn more about a realist...
Aug 06, 2022•1 hr 2 min•Season 1Ep. 113
MLOps Coffee Sessions #112 with Xiangrui Meng, Principal Software Engineer of Databricks, MLX: Opinionated ML Pipelines in MLflow co-hosted by Vishnu Rachakonda. // Abstract MLX is to enable data scientists to stay mostly within their comfort zone utilizing their expert knowledge while following the best practices in ML development and delivering production-ready ML projects, with little help from production engineers and DevOps. // Bio Xiangrui Meng is a Principal Software Engineer at Databrick...
Aug 03, 2022•50 min•Season 1Ep. 112
MLOps Coffee Sessions #111 with Samuel Partee, Principal Applied AI Engineer of Redis, More than a Cache: Turning Redis into a Composable, ML Data Platform co-hosted by Mihail Eric. This episode is sponsored by Redis. // Abstract Pushing forward the Redis platform to be more than just the web-serving cache that we've known it up to now. It seems like a natural progression for the platform, we see how they're evolving to be this AI-focused, AI native serving platform that does vector similarity, ...
Jul 30, 2022•49 min•Season 1Ep. 111
MLOps Coffee Sessions #110 with David Bayliss, Chief Data Scientist of LexisNexis Risk Solutions, Just Fetch the Data and then... co-hosted by Vishnu Rachakonda. // Abstract Composing data to extract features can be a significant problem. Key factors are the data size, compliance restrictions, and real-time data. Ethics (and law) can drive extremely complex audit requirements. In the cloud, you can do anything - at a price. // Bio One of the creators of the world's first big data platform (HPCC)...
Jul 29, 2022•52 min•Season 1Ep. 110
MLOps Coffee Sessions #109 with Ketan Umare, Co-founder and CEO of Union.ai, Why You Need More Than Airflow co-hosted by George Pearse. // Abstract Airflow is a beloved tool by data engineers and Machine Learning Engineers alike. But when doing ML what are the shortcomings and why is an orchestration tool like that not always the best developer experience? In this episode, we break down what some key drivers are for using an ML-specific orchestration tool. // Bio Ketan Umare is the CEO and co-fo...
Jul 23, 2022•1 hr 12 min•Season 1Ep. 109
MLOps Coffee Sessions #108 with Byron Allen, AI & ML Practice Lead at Contino, ML Flow vs Kubeflow 2022 co-hosted by George Pearse. // Abstract The amazing Byron Allen talks to us about why MLflow and Kubeflow are not playing the same game! ML flow vs Kubeflow is more like comparing apples to oranges or as he likes to make the analogy they are both cheese but one is an all-rounder and the other a high-class delicacy. This can be quite deceiving when analyzing the two. We do a deep div...
Jul 19, 2022•1 hr 6 min•Season 1Ep. 108
MLOps Coffee Sessions #107 with Ryan Russon, Manager, MLOps and Data Science of Maven Wave Partners, Why and When to Use Kubeflow for MLOps co-hosted by Mihail Eric. // Abstract Kubeflow is an excellent platform if your team is already leveraging Kubernetes and allows for a truly collaborative experience. Let’s take a deep dive into the pros and cons of using Kubeflow in your MLOps. // Bio From serving as an officer in the US Navy to Consulting for some of America's largest corpora...
Jul 11, 2022•59 min•Season 1Ep. 107
MLOps Coffee Sessions #106 with Delina Ivanova, Associate Director, Data of HelloFresh, Building a Culture of Experimentation to Speed Up Data-Driven Value co-hosted by Vishnu Rachakonda. // Abstract Supply chain/manufacturing are prime areas where the use of data science/analytics/ ML is underdeveloped, and experimentation is required to collect data and enable data-driven solutions. This talk encourages companies to conduct experiments and collect data over time in order to build accurate/scal...
Jul 05, 2022•54 min•Season 1Ep. 106
MLOps Coffee Sessions #106 with Curtis Northcutt, CEO & Co-Founder of Cleanlab, Cleanlab: Labeled Datasets that Correct Themselves Automatically co-hosted by Vishnu Rachakonda. // Abstract Pioneered at MIT by 3 Ph.D. Co-Founders, Cleanlab is an open-source/SaaS company building the premier data-centric AI tools workflows for (1) automatically correcting messy data and labels, (2) auto-tracking of dataset quality over time, (3) automatically finding classes to merge and delete, (4) auto ml fo...
Jul 01, 2022•1 hr 6 min•Season 1Ep. 105
