All Videos Tagged Chambers” (Data Science Central) - Data Science Central 2020-04-04T03:55:54Z https://www.datasciencecentral.com/video/video/listTagged?tag=Chambers%E2%80%9D&rss=yes&xn_auth=no DSC Webinar Series: Patterns for Successful Data Science Projects tag:www.datasciencecentral.com,2019-03-14:6448529:Video:809851 2019-03-14T21:54:32.450Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-patterns-for-successful-data-science-projects"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/1424893480?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Running data science workloads is a challenge regardless of whether you are running them on your laptop, on an on-premises cluster, or in the cloud. While buying 100% managed service is an option, these tools can be expensive and lack extensibility. Therefore, many companies opt for open source data science tools like scikit-learn and… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-patterns-for-successful-data-science-projects"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/1424893480?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Running data science workloads is a challenge regardless of whether you are running them on your laptop, on an on-premises cluster, or in the cloud. While buying 100% managed service is an option, these tools can be expensive and lack extensibility. Therefore, many companies opt for open source data science tools like scikit-learn and Apache Spark’s MLlib in order to balance both functionality and cost.<br /> <br /> However, even if a project succeeds at a point in time with any set of tools, these projects become harder and harder to maintain as data volumes increase and a desire for real-time pushes technology to its limit. New projects also struggle as new challenges of scale invalidate previous assumptions.<br /> <br /> In this latest Data Science Central Webinar, we will discuss some patterns we see that companies leverage to succeed with their data science projects.<br /> <br /> Key takeaways will be:<br /> <br /> Strategies for removing cognitive load for you and your team<br /> How to execute a program that is simple and effective<br /> How to best use the ecosystem of tools to be successful<br /> <br /> Speaker:<br /> Bill Chambers, Data Scientist - Databricks<br /> <br /> Hosted by:<br /> Rafael Knuth, Contributing Editor - Data Science Central DSC Webinar Series: An Expert’s Guide to Apache Spark™ tag:www.datasciencecentral.com,2018-05-23:6448529:Video:723863 2018-05-23T22:52:20.713Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-an-expert-s-guide-to-apache-spark"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/2781548053?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Apache Spark™ has become the de-facto data processing and AI engine in enterprises today due to its speed, ease of use, and sophisticated analytics. As the first Unified Analytics engine to unify data with AI, Spark allows data engineering and data science teams to simplify data preparation and model training — enabling innovative AI use cases that… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-an-expert-s-guide-to-apache-spark"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/2781548053?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Apache Spark™ has become the de-facto data processing and AI engine in enterprises today due to its speed, ease of use, and sophisticated analytics. As the first Unified Analytics engine to unify data with AI, Spark allows data engineering and data science teams to simplify data preparation and model training — enabling innovative AI use cases that leverage advanced analytics like machine learning, graph analytics, and deep learning.<br /> <br /> Join Bill Chambers, author of the book “Spark: The Definitive Guide,” and Matei Zaharia, Chief Technologist and Co-founder of Databricks and the orginal creator of Apache Spark™, in this Data Science Central webinar as they break down the basic operations and common functions of Spark and walk through sample use cases where Spark has helped accelerate AI innovation.<br /> <br /> In this webinar, we will cover:<br /> <br /> A gentle overview of big data and Spark<br /> Expert guidance on how to use, deploy and maintain Spark <br /> The fundamentals of monitoring, tuning, and debugging Spark<br /> An exploration into machine learning techniques and scenarios for employing MLlib, Spark’s scalable machine-learning library<br /> <br /> Speakers:<br /> Bill Chambers, Product Manager -- Databricks<br /> Matei Zaharia, Co-founder and Chief Technologist -- Databricks<br /> <br /> Hosted by:<br /> Bill Vorhies, Editorial Director -- Data Science Central