Round 3: Analyzing the Google paper "Continuous Delivery and Automation Pipelines in ML" Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Show Notes Data Science Steps for ML Data extraction: You select and integrate the relevant data from various data sources for the ML task. Data analysis: You perform e...
Oct 04, 2020•1 hr 6 min•Season 1Ep. 11
MLOps community meetup #36! This week, we talk to David Hershey, Solutions Engineer at Determined AI, about Moving Deep Learning from Research to Production with Determined and Kubeflow. // Key takeaways: What components are needed to do inference in ML How to structure models for ML inference How a model registry helps organize your models for easy consumption How you can set up reusable and easy-to-upgrade inference pipelines // Abstract: Translating the research that goes into creating a grea...
Oct 04, 2020•56 min•Season 1Ep. 36
Second installation, David and Demetrios are reviewing the Google paper about Continuous training and automated pipelines. They dive deep into machine learning monitoring and also what exactly continuous training actually entails. Some key highlights are: Automatically retraining and serving the models: When to do it? Outlier detection Drift detection Outlier detection: What is it? How you deal with it Drift detection Individual features may start to drift. This could be a bug, or it could be pe...
Sep 22, 2020•1 hr 8 min•Season 1Ep. 10
MLOps Meetup #34! This week, we talk to Kai Waehner about the beast that is Apache Kafka and how many different ways you can use it! Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // Key takeaways: -Kafka is much more than just messaging -Kafka is the de facto standard for processing huge volumes of data at s...
Sep 17, 2020•53 min•Season 1Ep. 35
Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter While machine learning is spreading like wildfire, very little attention has been paid to the ways that it can go wrong when moving from development to production. Even when models work perfectly, they can be attacked and/or degrade quickly if the data changes. Havin...
Sep 14, 2020•56 min•Season 1Ep. 34
Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter In this last episode, we covered how Google is thinking about MLOps and how automation plays a key part in their view of MLOps. We started to talk about CI, CD, and the role they play in a pipeline setup for CT. In the next episode, we'll pick up where we left off, star...
Sep 14, 2020•59 min•Season 1Ep. 9
Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter Yoav is the builder behind Say Less, an AI-powered email summarization tool that was recently featured on the front page of Hacker News and Product Hunt. In this talk, Yoav will walk us through the end-to-end process of building the tool, from the prototype phase to deploy...
Sep 08, 2020•53 min•Season 1Ep. 33
Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter || Links Referenced in the Show || General Info: https://medium.com/@paktek123 Load Balancer Series: https://medium.com/load-balancer-series Upcoming Open Source: https://medium.com/upcoming-open-source Some Libraries Neeran maintains: https://github.com/paktek123/elasticsear...
Sep 08, 2020•58 min•Season 1Ep. 8
Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter We trained a Transformer neural net on ambient music to see if a machine can compose with the great masters. Ambient is a soft, flowing, ethereal genre of music that I’ve loved for decades. There are all kinds of ambient, from white noise to tracks that mimic the murmur of soft ...
Sep 05, 2020•56 min•Season 1Ep. 32
Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter MLOps and DevOps have a large number of parallels. Many of the techniques, practices, and processes used for traditional software projects can be followed almost exactly in ML projects. However, the day-to-day of an ML project is usually significantly different from a traditional s...
Aug 31, 2020•56 min•Season 1Ep. 7
Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter 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 ...
Aug 20, 2020•57 min•Season 1Ep. 31
Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter 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 cent...
Aug 10, 2020•1 hr 3 min•Season 1Ep. 30
Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter 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 u...
Aug 08, 2020•1 hr 2 min•Season 1Ep. 6
Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter 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 Co...
Aug 04, 2020•54 min•Season 1Ep. 5
Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter Most MLOps discussion traditionally focuses on model deployment, containerization, and 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 ...
Jul 26, 2020•1 hr 1 min•Season 1Ep. 29
Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter We asked what you wanted to hear next in 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 store...
Jul 25, 2020•1 hr 3 min•Season 1Ep. 4
Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter As more and more machine learning models are deployed into production, it is imperative that 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 ...
Jul 24, 2020•55 min•Season 1Ep. 28
Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter 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, Sel...
Jul 20, 2020•56 min•Season 1Ep. 27
Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter 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 o...
Jul 16, 2020•1 hr 7 min•Ep. 3
Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter 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 hav...
Jul 12, 2020•1 hr 29 min•Season 1Ep. 26
Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter 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 focuses on the “four golden signals”: latency, errors, traffic, and saturation. Machine learning services can suffer from special types of problems t...
Jul 11, 2020•56 min•Season 1Ep. 24
Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter How to become a better data scientist: the definitive guide with Alexey Grigorev We all know what we need to do to be good data scientists: know machine learning, be able to program, and 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 bes...
Jul 10, 2020•1 hr 1 min•Season 1Ep. 25
Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter 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-rea...
Jul 04, 2020•1 hr 6 min•Season 1Ep. 23
David Aponte and Misha sat down and talked in depth about what the ML tool Paperspace can do. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter 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 A...
Jun 28, 2020•1 hr 7 min•Season 1Ep. 22
Running a Fintech on Machine Learning Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter 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 beginn...
Jun 21, 2020•54 min•Season 1Ep. 18
DataOps and Data Version Control MLOps.community meetup #19 with the Founder and creator of DVC.org Dmitry Petrov. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter 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? What are the benefits of using Git and data codi...
Jun 19, 2020•1 hr 2 min•Season 1Ep. 19
Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter 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 are, what serverless means, API endpoints, streaming and batch data, and a bit of coffee vs tea banter. ||Show Notes...
Jun 13, 2020•50 min•Season 1Ep. 1
MLOps.community meetup #17: a deep dive into the open source ML framework Hermoine built on top of MLflow with Neylson Crepalde Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter 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 experien...
Jun 11, 2020•1 hr 1 min•Season 1Ep. 17
Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter 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 of experience as an entrepreneur and business advisor/consultant...
Jun 06, 2020•57 min•Season 1Ep. 16
Human In The Loop Machine Learning and how to scale it with Robert Munro. Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter 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 becaus...
Jun 04, 2020•55 min•Season 1Ep. 15
