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MLOps.community

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

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All Episodes

00:56:50

MLOps Meetup #30 // Path to Production and Monetizing...

MLOps.community ·

2020/08/20

The concept of machine learning products is a new one for the business world. There is a lack of clarity around key elements: Product Roadmaps and Planning, the Machine Learning Lifecycle, Project and Product Management, Release Management, and Maintenance. In this talk, we covered a framework specific to Machine Learning products. We discussed the improvements businesses can expect to see from a repeatable process. We also covered the concept of monetization and integrating machine learning into

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01:02:54

MLOps Meetup #29 // Scaling Machine Learning...

MLOps.community ·

2020/08/10

Machine learning has become an increasingly important means for organizations to extract value from their data. Many companies start off with successful proofs of value but face problems when scaling their capabilities afterward. By generalizing engineering problems and solving them centrally, scaling becomes much more feasible. Model serving platforms generalize the problem of turning a machine learning model in a value-generating application. Combining a serving platform with cultural shifts such

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01:02:01

MLOps Coffee Sessions #6 // Continuous Integration...

MLOps.community ·

2020/08/08

David & Elle talk about how one of the staples of DevOps, the practice of continuous integration, can work for machine learning. Continuous integration is a tried-and-true method for speeding up development cycles and rapidly releasing software- an area where data science and ML could use some help. Making continuous integration work for ML has been challenging in the past, and we chat about new open-source tools and approaches in the Git ecosystem for leveling up development processes with big

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00:53:50

MLOps Coffee Sessions #5 // Airflow in MLOps //...

MLOps.community ·

2020/08/04

Airflow is a renowned tool for data engineering. It helps with orchestrating ETL workloads and it's well regarded amongst machine learning engineers as well. So, how does Airflow work and how is it applied to MLOps? In this episode, Demetrios and David are joined by Simon Darr, a Managing Consultant at Servian, with many years of experience using Airflow, along with a Byron Allen, a Senior Consultant at Servian, specializing in ML. The group discusses how Airflow works, its pros, and cons for MLOps

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01:00:36

MLOps #28 Continuous Evaluation & Model...

MLOps.community ·

2020/07/26

Most MLOps discussion traditionally focuses on model deployment, containerization, model serving - but where do the inputs come from and where do the outputs get used? In this session we demystify parts of the data science process used to create the all-important target variable and design machine learning experiments. We discuss some probability and statistical concepts which are useful for MLOps professionals. Knowledge of these concepts may assist practitioners working closely with data

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01:03:17

MLOps Coffee Sessions #4: A Conversation Around...

MLOps.community ·

2020/07/25

We asked what you wanted to hear next on our Coffee sessions and the vote was in favor of feature stores! Today the usual suspects Demetrios Brinkmann and David Aponte sat down to talk with Jim Dowling CEO of Logical Clocks and Venkata Pingali CEO of scribble data to talk about feature stores, what they are, why we need them, some business implications and everything in between! As always if you enjoyed the session let us know or reach out to us in slack! Check out what Jim is doing around

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00:55:21

MLOps #27 ML Observability // Aparna Dhinakaran -...

MLOps.community ·

2020/07/24

As more and more machine learning models are deployed into production, it is imperative we have better observability tools to monitor, troubleshoot, and explain their decisions. In this talk, Aparna Dhinakaran, Co-Founder, CPO of Arize AI (Berkeley-based startup focused on ML Observability), will discuss the state of the commonly seen ML Production Workflow and its challenges. She will focus on the lack of model observability, its impacts, and how Arize AI can help. This talk highlights common

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00:56:06

MLOps Meetup #26 // How to Leverage ML Tooling...

MLOps.community ·

2020/07/20

In this talk, I demonstrate an example of an ML project development and production workflows which we build on top of our proprietary core - Neu.ro - using a number of open-source and proprietary tools: Jupyter Notebooks, Tensorboard, FileBrowser, PyCharm Professional, Cookiecutter, Git, DVC, Airflow, Seldon, and Grafana. I describe how we integrate each of these tools with Neu.ro, and how we can improve these integrations. Mariya came to MLOps from a software development background. She started her

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01:06:39

MLOps Coffee Sessions #3 MLOps: Isn't That Just...

MLOps.community ·

2020/07/16

It can be tricky to explain MLOps to colleagues and managers who are used to traditional software engineering and DevOps, let alone your gran. We have to answer the 'Isn't that just DevOps?' question clearly, otherwise the challenges of MLOps will continue to be underestimated (potentially by us as well as others). In this session we dive into what is new about MLOps and why current mainstream DevOps alone does not solve the problems. Ryan Dawson is an Engineer at Seldon and author of the article

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01:29:06

MLOps Meetup #25 // Python and Dask: Scaling the...

MLOps.community ·

2020/07/12

Python's most popular data science libraries—pandas, numpy, and scikit-learn—were designed to run on a single computer, and in some cases, using a single processor. Whether this computer is a laptop or a server with 96 cores, your compute and memory are constrained by the size of the biggest computer you have access to. In this course, you'll learn how to use Dask, a Python library for parallel and distributed computing, to bypass this constraint by scaling our compute and memory across multiple

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00:55:53

MLOps Meetup #23 // Monitoring the ML stack // Lina...

MLOps.community ·

2020/07/11

How To Monitor Machine Learning Stacks - Why Current Monitoring is Unable to Detect Serious Issues and What to Do About It with Lina Weichbrodt. Monitoring usually focusses on the “four golden signals”: latency, errors, traffic, and saturation. Machine learning services can suffer from special types of problems that are hard to detect with these signals. The talk will introduce these problems with practical examples and suggests additional metrics that can be used to detect them. A case study

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01:00:45

MLOps Meetup #24 // How to Become a Better Data...

MLOps.community ·

2020/07/10

How to become a better data scientist: the definite guide with Alexey Grigorev We all know what we need to do to be good data scientists: know machine learning, be able to program, be fluent in SQL and Python. That’s enough to do our job quite well. But what does it take to be a better data scientist? The best way to grow as a data scientist is to step out of direct responsibilities and try on the hats of a product manager as well as a DevOps engineer. In particular, we should: - be pragmatic and

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01:05:54

MLOps #22 Feature Stores: An Essential Part of the ML...

MLOps.community ·

2020/07/04

Companies are increasingly investing in Machine Learning (ML) to deliver new customer experiences and re-invent business processes. Unfortunately, the majority of operational ML projects never make it to production. The most significant blocker is the lack of infrastructure and tooling required to build production-ready data for ML. Kevin Stumpf has a long history of building data infrastructure for ML, first for Uber Michelangelo, and most recently as co-founder of Tecton. Kevin will share his

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01:07:14

MLOps Meetup #21 Deep Dive on Paperspace Tooling //...

MLOps.community ·

2020/06/28

David Aponte and Misha sat down and talked in depth about what the ML tool paperspace can do. Misha Kutsovsky is a Senior Machine Learning Architect at Paperspace working on the Gradient team. He has expertise in machine learning, deep learning, distributed training, and MLOps. Previously he was on Microsoft's Windows Active Defense team building fileless malware detection software and tooling machine learning systems for Microsoft DevOps & Data Scientist teams. He holds B.S. and M.S. degrees in

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00:53:38

MLOps Meetup #18 // Nubank - Running a Fintech on ML...

MLOps.community ·

2020/06/21

Running a Fintech on Machine Learning For this meetup we sat down with Caique Lima and Cristiano Breuel Machine Learning Engineers at the Brasilian Fintech Nubank. Nubank is a Fintech providing credit and banking services to more than 20 million customers. Data science has been one of the company's pillars since the beginning, and many of its critical decisions in production are made with ML, in areas such as Credit, Fraud and Customer Service. We discussed how they develop, deploy and monitor ML

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01:02:10

MLOps Meetup #19 // DataOps and Data Versioning in ML...

MLOps.community ·

2020/06/19

DataOps and Data version Control MLOps.community meetup #19 with the Founder and creator of DVC.org Dmitry Petrov. Data versioning and data management are core components of MLOps and any end-to-end AI platform. What challenges are related to data versioning and how to overcome these? What are the benefits of using Git and data codification as a foundation of data versioning? And how open data versioning tools can enable an open MLOps ecosystem instead of closed end-to-end ML platforms. DVC and

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00:49:56

MLOps Coffee Sessions #1: Serving Models with Kubeflow

MLOps.community ·

2020/06/13

MLOps coffee sessions coming at you with our primer episode talking bout kfserving! David Aponte and Demetrios Brinkmann dive deep into what model serving is in machine learning, what different types of serving there is, what serverless means, API endpoints, streaming and batch data and a bit of coffee vs tea banter. ||Show Notes|| ML in Production is Hard Blog article by Nikki: http://veekaybee.github.io/2020/06/09/ml-in-prod/?utm_campaign=Data_Elixir&utm_source=Data_Elixir_289 Interactive

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01:01:29

MLOps Meetup #17 // The Challenges of ML Operations &...

MLOps.community ·

2020/06/11

MLOps.community meetup #17 a deep dive into the open source ML framework Hermoine built on top of MLflow with Neylson Crepalde Key takeaways for attendees: MLOps problems are dealt with tools but also with processes Open-source framework Hermione can help in a lot of parts of the operations process Abstract: In Neylson's experience with Machine Learning projects, he has encountered a series of challenges regarding agile processes to build and deploy ML models in a professional cooperative

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00:57:29

MLOps Meetup #16 // Venture Capital and Machine...

MLOps.community ·

2020/06/06

Venture Capital in Machine Learning Startups With John Spindler CEO of Capital Enterprise. John Spindler CEO of Capital Enterprise. We talked about what trends he has been seeing within MLOps, ML companies and also how he evaluates a deal. John Spindler has over 15 years experience as an entrepreneur and business advisor/consultant and as well as being responsible for the day to day management of Capital Enterprise he is also a general partner at AI Seed, an early-stage fund that invests in highly

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00:55:03

MLOps Meetup #15 Scaling Human-in-the-Loop Machine...

MLOps.community ·

2020/06/04

Human In The Loop Machine Learning and how to scale it with Robert Munro. This conversation centered around the components of Human-in-the-Loop Machine Learning systems and the challenges when scaling them. Most machine learning applications learn from human examples. For example, autonomous vehicles know what a pedestrian looks like because people have spent 1000s of hours labeling “pedestrians” in videos; your smart device understands you because people have spent 1000s of hours labeling the

en-us

514 results

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