MLOps.community
471 episodes - English - Latest episode: 2 days ago - ★★★★★ - 17 ratingsWeekly talks and fireside chats about everything that has to do with the new space emerging around DevOps for Machine Learning aka MLOps aka Machine Learning Operations.
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Episodes
MLOps + BI? // Maxime Beauchemin // MLOps Coffee Sessions #104
June 24, 2022 12:11 - 51 minutes - 48 MBMLOps Coffee Sessions #104 with the creator of Apache Airflow and Apache Superset Maxime Beauchemin, Future of BI co-hosted by Vishnu Rachakonda. // Abstract // Bio Maxime Beauchemin is the founder and CEO of Preset. Original creator of Apache Superset. Max has worked at the leading edge of data and analytics his entire career, helping shape the discipline in influential roles at data-dependent companies like Yahoo!, Lyft, Airbnb, Facebook, and Ubisoft. // MLOps Jobs board https://ml...
Making MLFlow // Lead MLFlow Maintainer Corey Zumar // MLOps Coffee Sessions #103
June 17, 2022 12:36 - 1 hour - 59.9 MBMLOps Coffee Sessions #103 with Corey Zumar, MLOps Podcast on Making MLflow co-hosted by Mihail Eric. // Abstract Because MLOps is a broad ecosystem of rapidly evolving tools and techniques, it creates several requirements and challenges for platform developers: - To serve the needs of many practitioners and organizations, it's important for MLOps platforms to support a variety of tools in the ecosystem. This necessitates extra scrutiny when designing APIs, as well as rigorous testing ...
Fixing Your ML Data Blind Spots // Yash Sheth // MLOps Coffee Sessions #102
June 10, 2022 12:25 - 52 minutes - 48.4 MBMLOps Coffee Sessions #102 with Yash Sheth, Fixing Your ML Data Blindspots co-hosted by Adam Sroka. // Abstract Improving your dataset quality is absolutely critical for effective ML. Finding errors in your datasets is generally a slow, iterative, and painstaking process. Data scientists should be proactively fixing their model’s blindspots by improving their training data. In this talk, Yash discusses how Galileo helps data scientists identify, fix, and track data across the entire...
Declarative Machine Learning Systems: Big Tech Level ML Without a Big Tech Team // Piero Molino // MLOps Coffee Sessions #101
June 03, 2022 12:05 - 58 minutes - 54.4 MBMLOps Coffee Sessions #101 with Piero Molino, Declarative Machine Learning Systems: Big Tech Level ML Without a Big Tech Team co-hosted by Vishnu Rachakonda. // Abstract Declarative Machine Learning Systems are the next step in the evolution of Machine Learning infrastructure. With such systems, organizations can marry the flexibility of low-level APIs with the simplicity of AutoML. Companies adopting such systems can increase the speed of machine learning development, reaching the quali...
Scaling Real-time Machine Learning at Chime // Peeyush Agarwal // Lightning Sessions #1
May 27, 2022 12:25 - 24 minutes - 22.5 MBLightning Sessions #1 with Peeyush Agarwal, Scaling Real-time Machine Learning at Chime. // Abstract In this Lighting Talk, Peeyush Agarwal explains 2 key pieces of the ML infrastructure at Chime. Peeyush goes into detail about the current feature store design and feature monitoring process along with the ML monitoring setup. This Lighting Talk is brought to you by arize.com reach out to them for all of your ML monitoring needs. // Bio Peeyush Agarwal is the Lead Software Engineer, ML...
MLOps Critiques // Matthijs Brouns // MLOps Coffee Sessions #100
May 27, 2022 12:17 - 50 minutes - 46.4 MBMLOps Coffee Sessions #100 with Matthijs Brouns, MLOps Critiques co-hosted by David Aponte. // Abstract MLOps is too tool-driven, don't let FOMO drive you to pick the latest feature/model/evaluation/ store but pay closer attention to what you actually need to release more safely and reliably. // Bio Matthijs is a Machine Learning Engineer, active in Amsterdam, The Netherlands. His current work involves training MLEs at Xccelerated.io. This means Matthijs divides his time between building...
CPU vs GPU // Ronen Dar & Gijsbert Janssen van Doorn // MLOps Coffee Sessions #99
May 20, 2022 12:08 - 1 hour - 59.2 MBMLOps Coffee Sessions #99 with Ronen Dar and Gijsbert Janssen van Doorn, Getting the Most Out of your AI Infrastructure co-hosted by Vishnu Rachakonda. // Abstract Run:AI is building a cloud-based platform for building with AI. In this talk, we hear all about why this need exists, how this works, and what value it creates. // Bio Ronen Dar Run:AI Co-founder and CTO Ronen was previously a research scientist at Bell Labs and has worked at Apple and Intel in multiple R&D roles. As CTO, R...
Racing the Playhead: Real-time Model Inference in a Video Streaming Environment // Brannon Dorsey // Coffee Sessions #98
May 12, 2022 09:19 - 58 minutes - 53.7 MBMLOps Coffee Sessions #98 with Brannon Dorsey, Racing the Playhead: Real-time Model Inference in a Video Streaming Environment co-hosted by Vishnu Rachakonda. // Abstract Runway ML is doing an incredibly cool workaround applying machine learning to video editing. Brannon is a software engineer there and he’s here to tell us all about machine learning in video and how Runway maintains their machine learning infrastructure. // Bio Brannon Dorsey is an early employee at Runway, where he lea...
Real-Time Exactly-Once Event Processing with Apache Flink, Kafka, and Pinot //Jacob Tsafatinos // MLOps Coffee Sessions #97
May 05, 2022 09:27 - 54 minutes - 50.2 MBMLOps Coffee Sessions #97 with Jacob Tsafatinos, Real-Time Exactly-Once Event Processing with Apache Flink, Kafka, and Pinot co-hosted by Mihail Eric. // Abstract A few years ago Uber set out to create an ads platform for the Uber Eats app that relied heavily on three pillars; Speed, Reliability, and Accuracy. Some of the technical challenges they were faced with included exactly-once semantics in real-time. To accomplish this goal, they created the architecture diagram above with lots of ...
FastAPI for Machine Learning // Sebastián Ramírez // MLOps Coffee Sessions #96
May 02, 2022 09:46 - 52 minutes - 48.7 MBMLOps Coffee Sessions #96 with Sebastián Ramírez, FastAPI for Machine Learning co-hosted by Adam Sroka. // Abstract Fast API almost never happened. Sebastián Ramírez, the creator of FastAPI, tried as hard as possible not to build something new. After many failed attempts at finding what he was looking for he decided to scratch his own itch and build a new product. The conversation goes over what Fast API is, how Sebastián built it, what the next big problems to tackle in ML are, and ho...
MLOps as Tool to Shape Team and Culture // Ciro Greco // MLOps Coffee Sessions #95
April 25, 2022 14:08 - 43 minutes - 39.9 MBMLOps Coffee Sessions #95 with Ciro Greco, MLOps as Tool to Shape Team and Culture. // Abstract Good MLOps practices are a way to operationalize a more “vertical” practice and blur the boundaries between different stages of “production-ready”. Sometimes you have this idea that production-ready means global availability but with ML products that need to be constantly tested against real-world data, we believe production-ready should be a continuum and that the key person that drives that n...
Traversing the Data Maturity Spectrum: A Startup Perspective // Mark Freeman // Coffee Sessions #94
April 21, 2022 11:20 - 46 minutes - 42.6 MBMLOps Coffee Sessions #94 with Mark Freeman, Traversing the Data Maturity Spectrum: A Startup Perspective. // Abstract A lot of companies talk about having ML and being data-driven, but few are there currently and doing it well. If anything, many companies are on the cusp of implementing ML rather than being ML mature. As a startup, what decisions are we making today to drive data maturity and set us up for success when we further implement ML in the near future. What business cases ar...
Model Monitoring in Practice: Top Trends // Krishnaram Kenthapadi // MLOps Coffee Sessions #93
April 14, 2022 11:26 - 51 minutes - 47.8 MBMLOps Coffee Sessions #93 with Krishnaram Kenthapadi, Model Monitoring in Practice: Top Trends co-hosted by Mihail Eric // Abstract We first motivate the need for ML model monitoring, as part of a broader AI model governance and responsible AI framework, and provide a roadmap for thinking about model monitoring in practice. We then present findings and insights on model monitoring in practice based on interviews with various ML practitioners spanning domains such as financial services, h...
Building the World's First Data Engineering Conference // Pete Soderling // MLOps Coffee Sessions #92
April 11, 2022 11:21 - 41 minutes - 38.9 MBMLOps Coffee Sessions #92 with Pete Soderling, Building the World's First Data Engineering Conference. // Abstract Keep things centered around community building and what he looks for in teams. Folks that are building their community around their tool, what advice do you have for that? What's worth turning into a company? // Bio Pete Soderling is the founder of Data Council and the Data Community Fund. As a former software engineer, repeat founder, and investor in more than 40 data-ori...
The Shipyard: Lessons Learned While Building an ML Platform / Automating Adherence // Joseph Haaga // Coffee Sessions #91
April 07, 2022 09:34 - 39 minutes - 37 MBMLOps Coffee Sessions #91 with Joseph Haaga, The Shipyard: Lessons Learned While Building an ML Platform / Automating Adherence. // Abstract Joseph Haaga and the Interos team walk us through their design decisions in building an internal data platform. Joseph talks about why their use case wasn't a fit for off the self solutions, what their internal tool snitch does, and how they use git as a model registry. Shipyard blogpost series: https://medium.com/interos-engineering. // Bio Jos...
Bringing Audio ML Models into Production // Valerio Velardo // MLOps Coffee Sessions #90
April 04, 2022 09:40 - 50 minutes - 47 MBMLOps Coffee Sessions #90 with Valerio Velardo, Bringing Audio ML Models into Production. // Abstract The majority of audio/music tech companies that employ ML still don’t use MLOps regularly. In these companies, you rarely find audio ML pipelines which take care of the whole ML lifecycle in a reliable and scalable manner. Audio ML probably pays the price of being a small sub-discipline of ML. It’s dwarfed by ML applications in image processing and NLP. In audio ML, novelties tend to tra...
A Journey in Scaling AI // Gabriel Straub // MLOps Coffee Sessions #89
March 31, 2022 09:10 - 52 minutes - 48.8 MBMLOps Coffee Sessions #89 with Gabriel Straub, A Journey in Scaling AI. // Abstract Gabriel talks to us about the difficulties of scaling ML products across an organization. He speaks about differences in profiles of data consumers and data producers, and the challenges of educating engineers so they have greater insights into the effects that their changes to the system may have. // Bio Gabriel joined Ocado Technology in 2020 as Chief Data Officer, bringing over 10 years of experience...
ML Platform Tradeoffs and Wondering Why to Use Them // Javier Mansilla // MLOps Coffee Sessions #88
March 28, 2022 09:37 - 53 minutes - 50 MBMLOps Coffee Sessions #88 with Javier Andres Mansilla, ML Platform Tradeoffs and Wondering Why to Use Them. // Abstract Javier runs ML Platform at Mercado Libre. We’re here with Javier because he’s going to tell us about what the ML platform at Mercado Libre looks like granularly, talk about its purpose, lessons, wins, and future improvements, and share with us some of the most challenging use cases they’ve had to engineer around. // Bio During the last 3 years building the internal ML p...
Don't Listen Unless You Are Going to Do ML in Production // Kyle Morris // MLOps Coffee Sessions #87
March 17, 2022 12:28 - 51 minutes - 47.7 MBMLOps Coffee Sessions #87 with Kyle Morris, Don't Listen Unless You Are Going to Do ML in Production. // Abstract Companies wanting to leverage ML specializes in model quality (architecture, training method, dataset), but face the same set of undifferentiated work they need to productionize the model. They must find machines to deploy their model on, set it up behind an API, make the inferences fast, cheap, reliable by optimizing hardware, load-balancing, autoscaling, clustering launches p...
Building ML/Data Platform on Top of Kubernetes // Julien Bisconti // MLOps Coffee Sessions #86
March 12, 2022 11:53 - 48 minutes - 44.7 MBMLOps Coffee Sessions #86 with Julien Bisconti, Building ML/Data Platform on Top of Kubernetes. // Abstract When building a platform, a good start would be to define the goals and features of that platform, knowing it will evolve. Kubernetes is established as the de facto standard for scalable platforms but it is not a fully-fledged data platform. Do ML engineers have to learn and use Kubernetes directly? They probably shouldn't. So it is up to the data engineering team to provide ...
Continuous Deployment of Critical ML Applications // Emmanuel Ameisen // MLOps Coffee Sessions #85
March 10, 2022 11:05 - 44 minutes - 41.3 MBMLOps Coffee Sessions #85 with Emmanuel Ameisen, Continuous Deployment of Critical ML Applications. // Abstract Finding an ML model that solves a business problem can feel like winning the lottery, but it can also be a curse. Once a model is embedded at the core of an application and used by real users, the real work begins. That's when you need to make sure that it works for everyone, that it keeps working every day, and that it can improve as time goes on. Just like building a model is a...
Lessons from Studying FAANG ML Systems // Ernest Chan // MLOps Coffee Sessions #84
March 02, 2022 12:54 - 45 minutes - 42.2 MBMLOps Coffee Sessions #84 with Ernest Chan, Lessons from Studying FAANG ML Systems. // Abstract Large tech companies invest in ML platforms to accelerate their ML efforts. Become better prepared to solve your own MLOps problems by learning from their technology and design decisions. Tune in to learn about ML platform components, capabilities, and design considerations. // Bio Ernest is a Data Scientist at Duo Security. As part of the core team that built Duo's first ML-powered product, ...
Better Use cases for Text Embeddings // Vincent Warmerdam // MLOps Coffee Sessions #83
February 28, 2022 12:33 - 48 minutes - 44.8 MBMLOps Coffee Sessions #83 with Vincent Warmerdam, Better Use cases for Text Embeddings. // Abstract Text embeddings are very popular, but there are plenty of reasons to be concerned about their applications. There's algorithmic fairness, compute requirements as well as issues with datasets that they're typically trained on. In this session, Vincent gives an overview of some of these properties while also talking about an underappreciated use-case for the embeddings: labeling! // Bio V...
Feature Stores at Shopify and Skyscanner // Matt Delacour and Mike Moran // Reading Group #4
February 23, 2022 15:01 - 49 minutes - 45.9 MBMLOps Reading Group meeting on February 11, 2022 Reading Group Session about Feature Stores with Matt Delacour and Mike Moran --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Connect with us on LinkedIn: https://www.linkedin.com/company/mlopscommunity/ Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, Feature Store, Machine Learning Monitoring and...
Trustworthy Data for Machine Learning // Chad Sanderson // MLOps Meetup #93
February 21, 2022 10:00 - 51 minutes - 47.2 MBMLOps Community Meetup #93! Two weeks ago, we talked to Chad Sanderson, Trustworthy Data for Machine Learning. //Abstract The most common challenge for ML teams operating at scale is data quality. In this talk, Chad discusses how Convoy invested in a large-scale data quality effort to treat data as an API and provide a data change management surface to enable trustworthy machine learning. // Bio Chad Sanderson is the Product Lead for Convoy's Data Platform team, which includes the dat...
Practitioners Guide to MLOps // Donna Schut and Christos Aniftos // Coffee Sessions #82
February 15, 2022 11:10 - 46 minutes - 43.1 MBMLOps Coffee Sessions #82 with Donna Schut and Christos Aniftos, Practitioners Guide to MLOps. // Abstract The "Practitioners Guide to MLOps" introduced excellent frameworks for how to think about the field. Can we talk about how you've seen the advice in that guide applied to real-world systems? Is there additional advice you'd add to that paper based on what you've seen since its publication and with new tools being introduced? Your article about selecting the right capabilities has a l...
Investing in MLOps // Leigh Marie Braswell and Davis Treybig // MLOps Coffee Sessions #81
February 14, 2022 10:46 - 48 minutes - 45.3 MBMLOps Coffee Sessions #81 with Davis Treybig and Leigh Marie Braswell, Machine Learning from the Viewpoint of Investors. // Abstract Machine learning is a rapidly evolving space that can be hard to keep track of. Every year, thousands of research papers are published in the space, and hundreds of new companies are built both in applied machine learning as well as in machine learning tooling. In this podcast, we interview two investors who focus heavily on machine learning to get their tak...
The Journey from Data Scientist to MLOps Engineer // Ale Solano // MLOps Coffee Sessions #80
February 08, 2022 12:34 - 41 minutes - 38.4 MBMLOps Coffee Sessions #80 with Ale Solano, The Journey from Data Scientist to MLOps Engineer. // Abstract After years of failed POCs then all of a sudden one of our models is accepted and will be used in production. The next morning we are part of the main scrum stand-up meeting and a DevOps guy is assisting us. A strange feeling, unknown to us until then, starts growing on the AI team: we are useful! Deploying models to production is challenging, but MLOps is more than that. MLOps is ab...
Platform Thinking: A Lemonade Case Study // Orr Shilon // MLOps Coffee Sessions #79
February 04, 2022 11:44 - 51 minutes - 47.9 MBMLOps Coffee Sessions #79 with Orr Shilon, Platform Thinking: A Lemonade Case Study. // Abstract This episode is the epitome of why people listen to our podcast. It’s a complete discussion of the technical, organizational, and cultural challenges of building a high-velocity, machine learning platform that impacts core business outcomes. Orr tells us about the focus on automation and platform thinking that’s uniquely allowed Lemonade’s engineers to make long-term investments that hav...
Calibration for ML at Etsy - apply() special // Erica Greene and Seoyoon Park // MLOps Coffee Sessions #78
January 31, 2022 12:30 - 49 minutes - 45.9 MBMLOps Coffee Sessions #78 with Erica Greene and Seoyoon Park, Calibration for ML at Etsy - apply() special. // Abstract This is a special conversation about Machine Learning calibration at Etsy. Demetrios sat down with Erica Greene and Seoyoon Park to hear about how they implemented Calibration into the Etsy Machine Learning workflow. The conversation is a pre-chat with these two before their presentation at the apply() conference on February 10th. Register here: applyconf.com // Bio ...
Data Mesh - The Data Quality Control Mechanism for MLOps? // Scott Hirleman // MLOps Coffee Sessions #77
January 28, 2022 10:56 - 57 minutes - 52.8 MBMLOps Coffee Sessions #77 with Scott Hirleman, Data Mesh - The Data Quality Control Mechanism for MLOps? // Abstract Scott covers what is a data mesh at a high level for those not familiar. Data mesh is potentially a great win for ML/MLOps as there is very clear guidance on creating useful, clean, well-documented/described and interoperable data for "unexpected use". So instead of data spelunking being a harrowing task, it can be a very fruitful one. And that one data set that was so awes...
Build a Culture of ML Testing and Model Quality // Mohamed Elgendy // MLOps Coffee Sessions #76
January 25, 2022 12:18 - 51 minutes - 47.4 MBMLOps Coffee Sessions #76 with Mohamed Elgendy, Build a Culture of ML Testing and Model Quality. // Abstract Machine learning engineers and data scientists spend most of their time testing and validating their models’ performance. But as machine learning products become more integral to our daily lives, the importance of rigorously testing model behavior will only increase. Current ML evaluation techniques are falling short in their attempts to describe the full picture of model performa...
Towards Observability for ML Pipelines // Shreya Shankar // MLOps Coffee Sessions #75
January 21, 2022 15:00 - 57 minutes - 52.9 MBMLOps Coffee Sessions #75 with Shreya Shankar, Towards Observability for ML Pipelines. // Abstract Achieving observability in ML pipelines is a mess right now. We are tracking thousands of means, percentiles, and KL divergences of features and outputs in a haphazard attempt to figure out when and how to retrain models. In this session, we break down current unsuccessful approaches and discuss the path towards effectively maintaining ML models in production. Along the way, we introduce mlt...
Scaling Biotech // Jesse Johnson // MLOps Coffee Sessions #74
January 19, 2022 09:59 - 51 minutes - 70.4 MBMLOps Coffee Sessions #74 with Jesse Johnson, Scaling Biotech. // Abstract Scaling a biotech research platform requires managing organization complexity - teams, functions, projects - rather than just the traditional volume, velocity, and variety. By examining the processes and experiments that drive the platform, you can focus your work where it matters the most by finding the ideal balance for each type of experiment along with a number of common trade-offs. // Bio Jesse Johnson is hea...
On Structuring an ML Platform 1 Pizza Team //Breno Costa & Matheus Frata //MLOps Coffee Sessions #73
January 07, 2022 17:34 - 52 minutes - 72.6 MBMLOps Coffee Sessions #73 with Breno Costa and Matheus Frata, On Structuring an ML Platform 1 Pizza Team. // Abstract Breno and Matheus were part of an organizational change at Neoway in recent years. With the creation of cross-functional and platform teams in order to improve the value stream generated by these. They share their experience in creating a machine learning platform team. The challenges they faced along the way, how they approached using product thinking and the results achie...
2021 MLOps Year in Review // Vishnu Rachakonda and Demetrios Brinkmann // MLOps Coffee Sessions #72
January 03, 2022 14:02 - 51 minutes - 70.8 MBMLOps Coffee Sessions #72 with Vishnu Rachakonda and Demetrios Brinkmann, 2021 MLOps Year in Review. // Abstract Vishnu and Demetrios sit down to reflect on some of the biggest news and learnings from 2021 from the biggest funding rounds to best insights. The two finish out the chat by talking about what to expect in 2022. // Bio Demetrios Brinkmann At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.communit...
2021 MLOps Year in Review // Vishnu Rachakonda and Demetrios Brinkmann // MLOps Coffee Sessions #71
January 03, 2022 14:02 - 51 minutes - 70.8 MBMLOps Coffee Sessions #71 with Vishnu Rachakonda and Demetrios Brinkmann, 2021 MLOps Year in Review. // Abstract Vishnu and Demetrios sit down to reflect on some of the biggest news and learnings from 2021 from the biggest funding rounds to best insights. The two finish out the chat by talking about what to expect in 2022. // Bio Demetrios Brinkmann At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.commu...
Setting up an ML Platform on GCP: Lessons Learned // Mefta Sadat // MLOps Coffee Sessions #70
December 28, 2021 11:44 - 40 minutes - 55 MBLoblaws is one of Canada’s largest grocery store chains, Mefta's team at Loblaw Digital runs several ML systems such as search, recommendations, inventory, and labor prediction on production. In this conversation, he shares his experience setting up their ML platform on GCP using Vertex AI and open-source tools. The goal of this platform is to help all the data science teams within their organization to take ML projects from EDA to production rapidly while ensuring end-to-end tracking of t...
Setting up an ML Platform on GCP: Lessons Learned // Mefta Sadat // MLOps Coffee Sessions #71
December 28, 2021 11:44 - 40 minutes - 55 MBLoblaws is one of Canada’s largest grocery store chains, Mefta's team at Loblaw Digital runs several ML systems such as search, recommendations, inventory, and labor prediction on production. In this conversation, he shares his experience setting up their ML platform on GCP using Vertex AI and open-source tools. The goal of this platform is to help all the data science teams within their organization to take ML projects from EDA to production rapidly while ensuring end-to-end tracking of t...
2022 Predictions for MLOps and the Industry // Reah Miyara // MLOps Coffee Sessions #70
December 23, 2021 13:02 - 36 minutes - 50 MBMLOps Coffee Sessions #70 with Reah Miyara, 2022 Predictions for MLOps and the Industry. // Abstract MLOps has moved fast in the last year. What will 2022 be like in the MLOps ecosystem? Raeh from Arize AI comes on to talk to us about what he expects for the new year. Arize is kindly offering 20 free subscriptions to their tool. No marketing BS these are design partners. First come first serve https://arize.com/mlops-signup/! // Bio Reah Miyara is a Senior Product Manager at Arize AI, ...
2022 Predictions for MLOps and the Industry // Reah Miyara // MLOps Coffee Sessions # 70
December 23, 2021 13:02 - 36 minutes - 50 MBMLOps Coffee Sessions #70 with Reah Miyara, 2022 Predictions for MLOps and the Industry. // Abstract MLOps has moved fast in the last year. What will 2022 be like in the MLOps ecosystem? Raeh from Arize AI comes on to talk to us about what he expects for the new year. Arize is kindly offering 20 free subscriptions to their tool. No marketing BS these are design partners. First come first serve https://arize.com/mlops-signup/! // Bio Reah Miyara is head of product at Arize AI, a leadin...
Building for Small Data Science Teams // James Lamb // MLOps Coffee Sessions #69
December 20, 2021 12:13 - 52 minutes - 72.3 MBMLOps Coffee Sessions #69 with James Lamb, Building for Small Data Science Teams co-hosted by Adam Sroka. // Abstract In this conversation, James shares some hard-won lessons on how to effectively use technology to create applications powered by machine learning models. James also talks about how making the "right" architecture decisions is as much about org structure and hiring plans as it is about technological features. // Bio James Lamb is a machine learning engineer at SpotHero, a ...
Wikimedia MLOps // Chris Albon // Coffee Sessions #68
December 13, 2021 12:00 - 1 hour - 90.4 MBMLOps Coffee Sessions #68 with Chris Albon, Wikimedia MLOps co-hosted by Neal Lathia. // Abstract // Bio Chris spent over a decade applying statistical learning, artificial intelligence, and software engineering to political, social, and humanitarian efforts. He is the Director of Machine Learning at the Wikimedia Foundation. Previously, Chris was the Director of Data Science at Devoted Health, Director of Data Science at the Kenyan startup BRCK, cofounded the AI startup Yonder, created t...
ML Stepping Stones: Challenges & Opportunities for Companies // John Crousse // Coffee Sessions #67
December 09, 2021 07:00 - 47 minutes - 65.9 MBMLOps Coffee Sessions #67 with John Crousse, ML Stepping Stones: Challenges & Opportunities for Companies co-hosted by Adam Sroka. // Abstract In this coffee session, John shares his observations after working with multiple companies which were in the process of scaling up their ML capabilities. John's observations are mostly around changes in practices, successes, failures, and bottlenecks identified when building ML products and teams from scratch. John shares a few thoughts on building...
Machine Learning at Reasonable Scale // Jacopo Tagliabue // MLOps Coffee Sessions #66
December 08, 2021 13:00 - 1 hour - 88.9 MBMLOps Coffee Sessions #66 with Jacopo Tagliabue, Machine Learning at Reasonable Scale. // Abstract We believe that immature data pipelines are preventing a large portion of industry practitioners from leveraging the latest research on ML: truth is, outside of Big Tech and advanced startups, ML systems are still far from producing the promised ROI. The good news is that times are changing: thanks to a growing ecosystem of tools and shared best practices, even small teams can be incredibly ...
The Future of Data Science Platforms is Accessibility // Skylar Payne // Coffee Session #65
November 30, 2021 13:00 - 52 minutes - 72 MBMLOps Coffee Sessions #65 with Skylar Payne, The Future of Data Science Platforms is Accessibility. // Abstract The machine learning and data science space is blowing up -- new tools are popping up every day. While we seem to have every type of "Flow" and "Store" you could imagine, few people really understand how to glue this stuff together. Despite all the tools we have available, we still see companies failing to leverage data science effectively to drive business results. Instead of ...
The Future of Data Science Platforms is Accessibility // Skylar Payne // Video Only coffee #65
November 30, 2021 13:00 - 52 minutes - 72 MBMLOps Coffee Sessions #65 with Skylar Payne, The Future of Data Science Platforms is Accessibility. // Abstract The machine learning and data science space is blowing up -- new tools are popping up every day. While we seem to have every type of "Flow" and "Store" you could imagine, few people really understand how to glue this stuff together. Despite all the tools we have available, we still see companies failing to leverage data science effectively to drive business results. Instead of ...
Impact of SWE in ML Projects // Laszlo Sragner and Tim Blazina // MLOps Reading Group
November 29, 2021 09:39 - 55 minutes - 76.5 MBMLOps Reading Group meeting on November 20, 2021 --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Connect with us on LinkedIn: https://www.linkedin.com/company/mlopscommunity/ Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, Feature Store, Machine Learning Monitoring and Blogs: https://mlops.community/
The Future of AI and ML in Process Automation // Slater Victoroff // MLOps Coffee Sessions #64
November 23, 2021 09:16 - 58 minutes - 79.7 MBMLOps Coffee Sessions #64 with Slater Victoroff, The Future of AI and ML in Process Automation. // Abstract The Unstructured Imperative Recent advances in AI have dramatically advanced the state of the art around unstructured data, especially in the spaces of NLP and computer vision. Despite this, the adoption of unstructured technologies has remained low. Why do you think that is? How have the dynamics changed in the last five years? Multimodal AI Historic AI approaches have genera...
PyTorch: Bridging AI Research and Production // Dmytro Dzhulgakov // Coffee Sessions #63
November 16, 2021 09:13 - 53 minutes - 73 MBDmytro Dzhulgakov, PyTorch: Bridging AI Research and Production. Talking PyTorch is always interesting, as the Facebook ML OSS project is one of the most important parts of the machine learning tooling ecosystem. This week, we talked to Dmytro Dzhulgakov, a tech lead for PyTorch. We started off talking about Dmytro's journey to being an engineer and tech lead at Facebook, and what his role entails. Dmytro has been at Facebook for 10+ years, so he gave some very interesting advice on how ...