All Videos Tagged DSC Podcast Series (Data Science Central) - Data Science Central 2020-09-21T16:31:14Z https://www.datasciencecentral.com/video/video/listTagged?tag=DSC+Podcast+Series&rss=yes&xn_auth=no DSC Podcast Series: ML Decisioning, Deployment and Governance at Scale tag:www.datasciencecentral.com,2020-08-14:6448529:Video:977120 2020-08-14T11:59:04.224Z Andrei Macsin https://www.datasciencecentral.com/profile/AndreiMacsin <a href="https://www.datasciencecentral.com/video/dsc-podcast-series-ml-decisioning-deployment-and-governance-at"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/7459077467?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>In today's Data Science Central podcast, we will show you how SAS Viya, a cloud-enabled and API-driven analytics engine, can easily automate decisioning at a production level. We will demonstrate how models can be managed and integrated into a decisioning process, as well as deployed and exposed externally using a custom Web application.… <a href="https://www.datasciencecentral.com/video/dsc-podcast-series-ml-decisioning-deployment-and-governance-at"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/7459077467?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />In today's Data Science Central podcast, we will show you how SAS Viya, a cloud-enabled and API-driven analytics engine, can easily automate decisioning at a production level. We will demonstrate how models can be managed and integrated into a decisioning process, as well as deployed and exposed externally using a custom Web application.<br /> <br /> Speaker:<br /> Paata Ugrekhelidze, Data Scientist - SAS AI<br /> <br /> <br /> Hosted by:<br /> Rafael Knuth, Contributing Editor - Data Science Central DSC Podcast Series: Do We Need Data Scientists in Today’s World of Automation? tag:www.datasciencecentral.com,2020-08-14:6448529:Video:977078 2020-08-14T11:58:30.017Z Andrei Macsin https://www.datasciencecentral.com/profile/AndreiMacsin <a href="https://www.datasciencecentral.com/video/dsc-podcast-series-do-we-need-data-scientists-in-today-s-world-of"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/7459072681?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Machine learning is said to be an important driver of the future of intelligent systems, automatically analyzing data and distilling new knowledge, actionable insights and compelling decisions. But why show market trends that investments in data scientists – those golden people that train machine learning models – have never been higher… <a href="https://www.datasciencecentral.com/video/dsc-podcast-series-do-we-need-data-scientists-in-today-s-world-of"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/7459072681?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Machine learning is said to be an important driver of the future of intelligent systems, automatically analyzing data and distilling new knowledge, actionable insights and compelling decisions. But why show market trends that investments in data scientists – those golden people that train machine learning models – have never been higher if machine learning can be fully automated? Why do we need data scientists if machine learning is designed to do it all? In this latest Data Science Central podcast, Véronique Van Vlasselaer, Data &amp; Decision Scientist at SAS, will discuss what machine learning automation entails, and how valuable human input in the machine learning process is.<br /> <br /> Speaker:<br /> Véronique Van Vlasselaer, Data &amp; Decision Scientist - SAS<br /> <br /> <br /> Hosted by:<br /> Rafael Knuth, Contributing Editor - Data Science Central DSC Podcast Series: Data Science Fails: The Transparency Sweet Spot tag:www.datasciencecentral.com,2020-08-14:6448529:Video:977075 2020-08-14T11:57:38.135Z Andrei Macsin https://www.datasciencecentral.com/profile/AndreiMacsin <a href="https://www.datasciencecentral.com/video/dsc-podcast-series-data-science-fails-the-transparency-sweet-spot"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/7459065689?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>2020 is the year that AI has moved from a science experiment to business as usual. And with the arrival of AI comes the need for AI governance. How do you know that your AI shares your values, behaves intuitively, and works as planned? There has been much debate about whether AIs should be trusted based upon their results, or whether they… <a href="https://www.datasciencecentral.com/video/dsc-podcast-series-data-science-fails-the-transparency-sweet-spot"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/7459065689?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />2020 is the year that AI has moved from a science experiment to business as usual. And with the arrival of AI comes the need for AI governance. How do you know that your AI shares your values, behaves intuitively, and works as planned? There has been much debate about whether AIs should be trusted based upon their results, or whether they should become fully transparent and understandable to humans. In this latest Data Science Central podcast, we go beyond the opinions and learn what academic research and experiments can tell us about optimal transparency.<br /> <br /> Speaker: <br /> Colin Priest, VP of AI Strategy - DataRobot<br /> <br /> Hosted by:<br /> Sean Welch, Host and Producer - Data Science Central DSC Podcast Series: Production Model Governance: MLOps tag:www.datasciencecentral.com,2020-08-14:6448529:Video:977114 2020-08-14T11:56:14.081Z Andrei Macsin https://www.datasciencecentral.com/profile/AndreiMacsin <a href="https://www.datasciencecentral.com/video/dsc-podcast-series-production-model-governance-mlops"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/7459054090?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Many models created by data science teams never generate value because they never reach production. In the end, data scientists have to help deploy and maintain their models, which is costly and takes away from doing new data science.<br></br> <br></br> What if there was a better way? Machine learning operations (MLOps) practices and technology help bridge… <a href="https://www.datasciencecentral.com/video/dsc-podcast-series-production-model-governance-mlops"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/7459054090?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Many models created by data science teams never generate value because they never reach production. In the end, data scientists have to help deploy and maintain their models, which is costly and takes away from doing new data science.<br /> <br /> What if there was a better way? Machine learning operations (MLOps) practices and technology help bridge the gap between data science and IT so that IT operations can deploy and manage models in production. In this latest Data Science Central podcast, we explore the best practices in four areas of MLOps: Production Model Deployment, Production Model Monitoring, Production Lifecycle Management, and Production Model Governance.<br /> <br /> Speaker:<br /> Dan Darnell, Senior Director on the MLOps Product Team – DataRobot<br /> <br /> Hosted by:<br /> Sean Welch, Host and Producer - Data Science Central DSC Podcast Series: Tackling the Challenges of Production Model Deployment with MLOps tag:www.datasciencecentral.com,2020-06-09:6448529:Video:956771 2020-06-09T22:48:34.413Z Andrei Macsin https://www.datasciencecentral.com/profile/AndreiMacsin <a href="https://www.datasciencecentral.com/video/dsc-podcast-series-tackling-the-challenges-of-production-model-de"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/5778153687?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Many models created by data science teams never generate value because they never reach production. In the end, data scientists have to help deploy and maintain their models, which is costly and takes away from doing new data science.<br></br> <br></br> What if there was a better way? Machine learning operations (MLOps) practices and technology… <a href="https://www.datasciencecentral.com/video/dsc-podcast-series-tackling-the-challenges-of-production-model-de"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/5778153687?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Many models created by data science teams never generate value because they never reach production. In the end, data scientists have to help deploy and maintain their models, which is costly and takes away from doing new data science.<br /> <br /> What if there was a better way? Machine learning operations (MLOps) practices and technology help bridge the gap between data science and IT so that IT operations can deploy and manage models in production. In this latest Data Science Central podcast, we explore the best practices in four areas of MLOps: Production Model Deployment, Production Model Monitoring, Production Lifecycle Management, and Production Model Governance.<br /> <br /> Speaker:<br /> Dan Darnell, Senior Director on the MLOps Product Team – DataRobot<br /> <br /> Hosted by:<br /> Sean Welch, Host and Producer - Data Science Central