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PODCAST

MLOps.community

Relaxed Conversations around getting AI into production, whatever shape that may come in (agentic, traditional ML, LLMs, Vibes, etc)

technology

All Episodes

00:56:36

Product Enrichment and Recommender Systems // Marc...

MLOps.community ·

2022/08/10

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 as

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01:02:01

Building Better Data Teams // Leanne Fitzpatrick //...

MLOps.community ·

2022/08/06

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 realistic take on

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00:50:17

MLX: Opinionated ML Pipelines in MLflow // Xiangrui...

MLOps.community ·

2022/08/03

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 Databricks

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00:48:33

More than a Cache: Turning Redis into a Composable,...

MLOps.community ·

2022/07/30

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,

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00:52:03

Just Fetch the Data and then... // David Bayliss //...

MLOps.community ·

2022/07/29

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);

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01:11:36

Why You Need More Than Airflow // Ketan Umare //...

MLOps.community ·

2022/07/23

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

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01:06:01

ML Flow vs Kubeflow 2022 // Byron Allen // Coffee...

MLOps.community ·

2022/07/19

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 dive into

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00:58:56

Why and When to Use Kubeflow for MLOps // Ryan Russon...

MLOps.community ·

2022/07/11

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 corporations, Ryan

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00:54:06

Building a Culture of Experimentation to Speed Up...

MLOps.community ·

2022/07/05

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

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01:06:10

Cleanlab: Labeled Datasets that Correct Themselves...

MLOps.community ·

2022/07/01

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 for

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00:51:50

MLOps + BI? // Maxime Beauchemin // MLOps Coffee...

MLOps.community ·

2022/06/24

MLOps 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

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01:04:45

Making MLFlow // Lead MLFlow Maintainer Corey Zumar...

MLOps.community ·

2022/06/17

MLOps 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 strategies

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00:52:16

Fixing Your ML Data Blind Spots // Yash Sheth //...

MLOps.community ·

2022/06/10

MLOps 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 ML

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00:58:46

Declarative Machine Learning Systems: Big Tech Level...

MLOps.community ·

2022/06/03

MLOps 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 quality and

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00:24:21

Scaling Real-time Machine Learning at Chime //...

MLOps.community ·

2022/05/27

Lightning 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 Platform

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00:50:05

MLOps Critiques // Matthijs Brouns // MLOps Coffee...

MLOps.community ·

2022/05/27

MLOps 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 new

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01:03:57

CPU vs GPU // Ronen Dar & Gijsbert Janssen van Doorn...

MLOps.community ·

2022/05/20

MLOps 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, Ronen

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00:58:04

Racing the Playhead: Real-time Model Inference in a...

MLOps.community ·

2022/05/12

MLOps 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 leads the

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00:54:12

Real-Time Exactly-Once Event Processing with Apache...

MLOps.community ·

2022/05/05

MLOps 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 love from

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00:52:40

FastAPI for Machine Learning // Sebastián Ramírez //...

MLOps.community ·

2022/05/02

MLOps 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 how to focus

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514 results

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