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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)

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All Episodes

00:18:42

When Machine Learning meets Data Privacy

MLOps.community ·

2020/11/05

This is the first episode of a podcast series on Machine Learning and Data privacy. Machine Learning is the key to the new revolution in many industries. Nevertheless, ML does not exist without data and a lot of it, which in many cases results in the use of sensitive information. With new privacy regulations, access to data is today harder and much more difficult but, does that mean that ML and Data Science has its days counted? Will the Machines beat privacy? Don’t forget to subscribe to the

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01:01:06

Analyzing the Google Paper on Continuous Delivery in...

MLOps.community ·

2020/11/03

MLOps level 2: CI/CD pipeline automation For a rapid and reliable update of the pipelines in production, you need a robust automated CI/CD system. This automated CI/CD system lets your data scientists rapidly explore new ideas around feature engineering, model architecture, and hyperparameters. They can implement these ideas and automatically build, test, and deploy the new pipeline components to the target environment. Figure 4. CI/CD and automated ML pipeline. This MLOps setup includes the

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00:57:45

Hands-on serving models using KFserving // Theofilos...

MLOps.community ·

2020/10/30

MLOps community meetup #40! Last Wednesday, we talked to Theofilos Papapanagiotou, Data Science Architect at Prosus, about Hands-on Serving Models Using KFserving. // Abstract: We looked to some popular model formats like the SavedModel of Tensorflow, the Model Archiver of PyTorch, pickle&ONNX, to understand how the weights of the NN are saved there, the graph, and the signature concepts. We discussed the relevant resources of the deployment stack of Istio (the Ingress gateway, the sidecar and

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00:56:59

Operationalize Open Source Models with SAS Open Model...

MLOps.community ·

2020/10/27

MLOps community meetup #39! Last week we talked to Ivan Nardini, Customer Engineer at SAS, about Operationalize Open Source Models with SAS Open Model Manager. // Abstract: Analytics are Open. According to their nature, Open Source technologies allows an agile development of the models, but it results difficult to put them in production. The goal of SAS is supporting customers in operationalize analytics In this meetup, I present SAS Open Model Manager, a containerized Modelops tool that

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00:57:17

Machine in Production = Data Engineering + ML +...

MLOps.community ·

2020/10/26

//Bio Satish built compilers, profilers, IDEs, and other dev tools for over a decade. At Microsoft Research, he saw his colleagues solving hard program analysis problems using Machine Learning. That is when he got curious and started learning. His approach to ML is influenced by his software engineering background of building things for production. He has a keen interest in doing ML in production, which is a lot more than training and tuning the models. The first step is to understand the product

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01:02:05

MLOps + Machine Learning // James Sutton // MLOps...

MLOps.community ·

2020/10/20

James Sutton is an ML Engineer focused on helping enterprise bridge the gap between what they have now, and where they need to be to enable production scale ML deployments. ----------- Connect With Us ✌️------------- Join our Slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with David on LinkedIn:

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00:57:10

Scalable Python for Everyone, Everywhere // Matthew...

MLOps.community ·

2020/10/19

Parallel Computing with Dask and Coiled Python makes data science and machine learning accessible to millions of people around the world. However, historically Python hasn't handled parallel computing well, which leads to issues as researchers try to tackle problems on increasingly large datasets. Dask is an open source Python library that enables the existing Python data science stack (Numpy, Pandas, Scikit-Learn, Jupyter, ...) with parallel and distributed computing. Today Dask has been broadly

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01:00:54

MLOps Coffee Sessions #13 How to Choose the Right...

MLOps.community ·

2020/10/18

This time we talked about one of the most vibrant questions for any MLOps practitioner: how to choose the right tools for your ML team, given the huge amount of open-source and proprietary MLOps tools available on the market today. We discussed several criteria to rely on when choosing a tool, including: - The requirements of the particular team use-cases - The scaling capacity of the tool - The cost of migration from a chosen tool - The cost of teaching the team to use this tool - The company or

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00:56:42

MLOps Coffee Sessions #14 Conversation with the...

MLOps.community ·

2020/10/12

Dask What is it? Parallelism for analytics What is parallelism? Doing a lot at once by splitting tasks into smaller subtasks which can be processed in parallel (at the same time) Distributed work across multiple machines and then combining the results Helpful for CPU bound - doing a bunch of calculations on the CPU. The rate at which process progresses is limited by the speed of the CPU Concurrency? Similar but a but things don’t have to happen at the same time, they can happen asynchronously. They

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01:05:05

MLOps Coffee Sessions #12: Journey of Flyte at Lyft...

MLOps.community ·

2020/10/10

Why was Flyte built at Lyft? What sorts of requirements does a ML infrastructure team have at lyft? What problems does it solve / use cases? Where does it fit in in the ML and Data ecosystem? What is the vision? Who should consider using it? Learnings as the engineering team tried to bootstrap an open-source community. Ketan Umare is a senior staff software engineer at Lyft responsible for technical direction of the Machine Learning Platform and is a founder of the Flyte project. Before Flyte he

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01:06:27

MLOps Coffee Sessions #11: Analyzing “Continuous...

MLOps.community ·

2020/10/04

Round 3 analyzing the Google paper "Continuous Delivery and Automation Pipelines in ML" // 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 exploratory data analysis (EDA) to understand the available data for building the ML model. This process leads to the following: Understanding the data schema and characteristics that are expected by the model. Identifying the data preparation

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00:56:05

MLOps Meetup #36: Moving Deep Learning from Research...

MLOps.community ·

2020/10/04

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 great

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01:07:59

MLOps Coffee Sessions #10 Analyzing the Article...

MLOps.community ·

2020/09/22

Second installation David and Demetrios 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 perfectly

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00:52:48

MLOps Meetup #34: Streaming Machine Learning with...

MLOps.community ·

2020/09/17

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! // Key takeaways: -Kafka is much more than just messaging -Kafka is the de facto standard for processing huge volumes of data at scale in real-time -Kafka and Machine Learning are complementary for various use cases (including data integration, data processing, model training, model scoring, and monitoring) // Abstract: The combination of Apache Kafka, tiered storage,

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00:56:24

MLOps Meetup #33 Owned By Statistics: How Kubeflow &...

MLOps.community ·

2020/09/14

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. Having a well understood MLOps process is necessary for ML security! Using Kubeflow, we demonstrated how to the common ways machine learning workflows go wrong, and how to mitigate them using MLOps pipelines to provide reproducibility,

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00:59:20

MLOps Coffee Sessions #9 Analyzing the Article...

MLOps.community ·

2020/09/14

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, starting our discussion of CT and some of the reasons you’d want to set up a pipeline with continuous training in the first place. Join our slack community:

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00:53:17

MLOps Meetup #32 Building Say Less: An AI-Powered...

MLOps.community ·

2020/09/08

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 deploying the model as a realtime HTTP endpoint. Yoav Zimmerman is the engineer / founder behind Model Zoo, a machine learning deployment platform focused on ease-of-use. He has previously worked at Determined AI on large-scale deep

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00:57:45

MLOps Coffee Sessions #8 // MLOps from the...

MLOps.community ·

2020/09/08

|| Links Referenced in the Show || General Info: https://medium.com/@paktek123 Load Balancer Series: https://medium.com/load-balancer-series Upcoming Open Src: https://medium.com/upcoming-open-source Some Libraries Neeran maintains: https://github.com/paktek123/elasticsearch-crystal Some libraries Neeran used to maintain: https://github.com/microsoft/pgtester (and https://medium.com/yammer-engineering/testing-postgresql-scripts-with-rspec-and-pg-tester-c3c6c1679aec) Some interesting projects Neeran

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00:56:00

MLOps Meetup #31 // Creating Beautiful Ambient Music...

MLOps.community ·

2020/09/05

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 summer rain in a sprawling forest, but Dan favors ambient that weaves together environmental sounds and dreamy, wavelike melodies into a single, lush tapestry. Can machine learning ever hope to craft something so seemingly

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00:55:31

MLOps Coffee Sessions #7 // MLOps and DevOps -...

MLOps.community ·

2020/08/31

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 software project. So while the ideas and principles can still apply, it’s important to be aware of the core aims of DevOps when applying them. Damian is a Cloud Advocate specializing in DevOps and MLOps. After spending a

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514 results

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