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Ion Stoica

10 Podcast Episodes

Latest 6 Nov 2021 | Updated Daily

Weekly hand curated podcast episodes for learning

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Ray Ecosystem with Ion Stoica

Cloud Engineering – Software Engineering Daily

Ray is a general purpose distributed computing framework. Ray is used for reinforcement learning and other compute intensive tasks. It was developed at the Berkeley RISELab, a research and development lab with an emphasis on practical applications. Ion Stoica is a professor at Berkeley, and he joins the show to talk about the present and future of the Ray framework. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Ray Ecosystem with Ion Stoica appeared first on Software Engineering Daily.

44mins

1 Oct 2020

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Ray Ecosystem with Ion Stoica

Software Daily

Ray is a general purpose distributed computing framework. Ray is used for reinforcement learning and other compute intensive tasks. It was developed at the Berkeley RISELab, a research and development lab with an emphasis on practical applications. Ion Stoica is a professor at Berkeley, and he joins the show to talk about the present and future of the Ray framework.

1 Oct 2020

Similar People

Episode artwork

Ray Ecosystem with Ion Stoica

Podcast – Software Engineering Daily

Ray is a general purpose distributed computing framework. Ray is used for reinforcement learning and other compute intensive tasks. It was developed at the Berkeley RISELab, a research and development lab with an emphasis on practical applications. Ion Stoica is a professor at Berkeley, and he joins the show to talk about the present and future of the Ray framework. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Ray Ecosystem with Ion Stoica appeared first on Software Engineering Daily.

44mins

1 Oct 2020

Episode artwork

Ray Ecosystem with Ion Stoica

Software Engineering Daily

Ray is a general purpose distributed computing framework. Ray is used for reinforcement learning and other compute intensive tasks. It was developed at the Berkeley RISELab, a research and development lab with an emphasis on practical applications. Ion Stoica is a professor at Berkeley, and he joins the show to talk about the present and future of the Ray framework. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Ray Ecosystem with Ion Stoica appeared first on Software Engineering Daily.

44mins

1 Oct 2020

Most Popular

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Anyscale with Ion Stoica

Machine Learning – Software Engineering Daily

Machine learning applications are widely deployed across the software industry.  Most of these applications used supervised learning, a process in which labeled data sets are used to find correlations between the labels and the trends in that underlying data. But supervised learning is only one application of machine learning. Another broad set of machine learning methods is described by the term “reinforcement learning.” Reinforcement learning involves an agent interacting with its environment. As the model interacts with the environment, it learns to make better decisions over time based on a reward function. Newer AI applications will need to operate in increasingly dynamic environments, and react to changes in those environments, which makes reinforcement learning a useful technique. Reinforcement learning has several attributes that make it a distinctly different engineering problem than supervised learning. Reinforcement learning relies on simulation and distributed training to rapidly examine how different model parameters could affect the performance of a model in different scenarios. Ray is an open source project for distributed applications. Although Ray was designed with reinforcement learning in mind, the potential use cases go beyond machine learning, and could be as influential and broadly applicable as distributed systems projects like Apache Spark or Kubernetes. Ray is a project from the Berkeley RISE Lab, the same place that gave rise to Spark, Mesos, and Alluxio. The RISE Lab is led by Ion Stoica, a professor of computer science at Berkeley. He is also the co-founder of Anyscale, a company started to commercialize Ray by offering tools and services for enterprises looking to adopt Ray. Ion Stoica returns to the show to discuss reinforcement learning, distributed computing, and the Ray project. If you enjoy the show, you can find all of our past episodes about machine learning, data, and the RISE Lab by going to SoftwareDaily.com and searching for the technologies or companies you are curious about . And if there is a subject that you want to hear covered, feel free to leave a comment on the episode, or send us a tweet @software_daily. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Anyscale with Ion Stoica appeared first on Software Engineering Daily.

48mins

13 Feb 2020

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Anyscale with Ion Stoica

Software Daily

Machine learning applications are widely deployed across the software industry. Most of these applications used supervised learning, a process in which labeled data sets are used to find correlations between the labels and the trends in that underlying data. But supervised learning is only one application of machine learning. Another broad set of machine learning methods is described by the term “reinforcement learning.”Reinforcement learning involves an agent interacting with its environment. As the model interacts with the environment, it learns to make better decisions over time based on a reward function. Newer AI applications will need to operate in increasingly dynamic environments, and react to changes in those environments, which makes reinforcement learning a useful technique.Reinforcement learning has several attributes that make it a distinctly different engineering problem than supervised learning. Reinforcement learning relies on simulation and distributed training to rapidly examine how different model parameters could affect the performance of a model in different scenarios.Ray is an open source project for distributed applications. Although Ray was designed with reinforcement learning in mind, the potential use cases go beyond machine learning, and could be as influential and broadly applicable as distributed systems projects like Apache Spark or Kubernetes. Ray is a project from the Berkeley RISE Lab, the same place that gave rise to Spark, Mesos, and Alluxio.The RISE Lab is led by Ion Stoica, a professor of computer science at Berkeley. He is also the co-founder of Anyscale, a company started to commercialize Ray by offering tools and services for enterprises looking to adopt Ray. Ion Stoica returns to the show to discuss reinforcement learning, distributed computing, and the Ray project.If you enjoy the show, you can find all of our past episodes about machine learning, data, and the RISE Lab by going to SoftwareDaily.com and searching for the technologies or companies you are curious about . And if there is a subject that you want to hear covered, feel free to leave a comment on the episode, or send us a tweet @software_daily.

13 Feb 2020

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How to Change the World (Ion Stoica, Executive Chairman & Former CEO of Databricks; & Co-Founder of Conviva)

B2B a CEO (with Ashu Garg)

Two-time founder/CEO, and Berkeley computer science professor, Ion Stoica chats with Ashu about what it means to have an impact and how to go from academic to entrepreneur.

18mins

21 Nov 2019

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Serverless Research with Ion Stoica

Business and Philosophy

The Berkeley AMPLab was a research lab where Apache Spark and Apache Mesos were both created. In the last five years, the Mesos and Spark projects have changed the way infrastructure is managed and improved the tools for data science. Because of its proximity to Silicon Valley, Berkeley has become a university where fundamental research is blended with a sense of industry applications. Students and professors move between business and academia, finding problems in industry and bringing them into the lab where they can be studied without the day-to-day pressures of a corporation. This makes Berkeley the perfect place for research around “serverless”. Serverless computing abstracts away the notion of a server, allowing developers to work at a higher level and be less concerned about the problems inherent in servers–such as failing instances and unpredictable network connections. With serverless functions-as-a-service, the cloud provider makes guarantees around the execution of serverless code–such as with AWS Lambda. With serverless backend services, the cloud provider makes guarantees around the reliability of a database or queueing system. The cloud provider is operating servers to power this functionality. But the user is not exposed to those servers. Today’s show centers around the serverless functions-as-a-service. This is a new paradigm of computing, and there are many open questions. How can the servers for our functions be quickly provisioned? How can we parallelize batch jobs into functions as a service? How can large numbers of serverless functions communicate with each other reliably to coordinate? In production applications, functions-as-a-service are mostly used for “event-driven” applications. But the potential for functions-as-a-service is much larger. Ion Stoica is a professor of computer science at Berkeley, where he leads the RISELab. He is the co-founder of Conviva Networks and Databricks. Databricks is the company that was born as a result of the research on Apache Spark. Ion now serves as executive chairman of Databricks. Ion joins the show to describe why serverless computing is exciting, the open research problems, and the solutions that researchers at the RISELab are exploring. Show Notes Occupy the Cloud: Distributed Computing for the 99% RISELab at UC Berkeley – REAL-TIME INTELLIGENT SECURE EXECUTION pywren — run your python code on thousands of cores – pywren IEEE Cloud Serverless Workshop July 2018 Jonas GitHub – Vaishaal/numpywren: Serverless Scientific Computing Serverless for Data Scientists| Mike Lee Williams @ PyBay2018 – YouTube Serverless for data scientists Serverless Big Data Analytics at Traveloka (Cloud Next ’18) – YouTube With PyWren, AWS Lambda Finds an Unexpected Market in Scientific Computing – The New Stack The post Serverless Research with Ion Stoica appeared first on Software Engineering Daily.

1hr 6mins

10 Dec 2018

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Ray: A Distributed Computing Platform for Reinforcement Learning with Ion Stoica - TWiML Talk #55

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

The show you’re about to hear is part of a series of shows recorded in San Francisco at the Artificial Intelligence Conference. In this episode, I talk with Ion Stoica, professor of computer science & director of the RISE Lab at UC Berkeley. Ion joined us after he gave his talk “Building reinforcement learning applications with Ray.” We dive into Ray, a new distributed computing platform for RL, as well as RL generally, along with some of the other interesting projects RISE Lab is working on, like Clipper & Tegra. This was a pretty interesting talk. Enjoy! The notes for this show can be found at twimlai.com/talk/55

28mins

5 Oct 2017

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Ep. 30: Ion Stoica on how RISELab is pushing the envelope on real-time data

THE ARCHITECHT SHOW

In this episode of the ARCHITECHT Show, Ion Stoica talks about the promise of real-time data and machine learning he's pursuing with the new RISELab project he directs at UC-Berkeley, along with some other big names in big data. Stoica previously was director of the university's AMPLab, which created and helped to mature technologies such as Apache Spark, Apache Mesos and Alluxio. Stoica is also co-founder and executive chairman of Apache Spark startup Databricks, and he shares some insights into that company's business and the evolution of the big data ecosystem. In the news segment, co-hosts Derrick Harris (ARCHITECHT) and Barb Darrow (Fortune) discuss Microsoft (and possibly AWS) doubling down on Kubernetes, Google's cloudy cloud revenue, GoDaddy getting out of the cloud business, and the possibility of Meg Whitman as Uber CEO.

1hr 6mins

27 Jul 2017