All Videos Tagged DSC Podcast Series (Data Science Central) - Data Science Central 2021-04-13T12:55:12Z https://www.datasciencecentral.com/video/video/listTagged?tag=DSC+Podcast+Series&rss=yes&xn_auth=no DSC Podcast Series: Looking at Feature Engineering in a Different Way tag:www.datasciencecentral.com,2021-04-08:6448529:Video:1046658 2021-04-08T21:53:52.238Z Sean Welch https://www.datasciencecentral.com/profile/SeanWelch <a href="https://www.datasciencecentral.com/video/dsc-podcast-series-looking-at-feature-engineering-in-a-different"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/8773751685?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Predictive modeling success hinges on selecting the features that are most likely to affect the desired outcome and sub-optimal featuring engineering is one of the culprits behind poorly performing models. In this session, we will discuss how a small number of carefully curated features can successfully serve as a proxy of predictive… <a href="https://www.datasciencecentral.com/video/dsc-podcast-series-looking-at-feature-engineering-in-a-different"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/8773751685?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Predictive modeling success hinges on selecting the features that are most likely to affect the desired outcome and sub-optimal featuring engineering is one of the culprits behind poorly performing models. In this session, we will discuss how a small number of carefully curated features can successfully serve as a proxy of predictive characteristics across a wide range of applications. Learn how data enrichment and the use of pre-defined features can simplify feature engineering, make it more science than art, and eventually improve model quality.<br /> <br /> Speaker: <br /> Anindya Datta, CEO - Mobilewalla<br /> <br /> Hosted by: <br /> Sean Welch, Host and Producer - Data Science Central DSC Podcast Series: The Foundation for Your AI Strategy: MLOps 101 tag:www.datasciencecentral.com,2021-03-09:6448529:Video:1042787 2021-03-09T01:53:39.581Z Sean Welch https://www.datasciencecentral.com/profile/SeanWelch <a href="https://www.datasciencecentral.com/video/dsc-podcast-series-the-foundation-for-your-ai-strategy-mlops-101"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/8644538478?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Organizations of all types and industries are dipping their toes into machine learning and artificial intelligence (AI). However, for most embarking on this transformational journey, the results remain to be seen. And for those who are already underway, scaling their results across their organizations is completely uncharted waters.… <a href="https://www.datasciencecentral.com/video/dsc-podcast-series-the-foundation-for-your-ai-strategy-mlops-101"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/8644538478?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Organizations of all types and industries are dipping their toes into machine learning and artificial intelligence (AI). However, for most embarking on this transformational journey, the results remain to be seen. And for those who are already underway, scaling their results across their organizations is completely uncharted waters.<br /> <br /> Machine Learning Operations (MLOps) allows organizations to alleviate many of the issues on the path to AI with ROI by providing a technological backbone for managing the machine learning lifecycle through automation and scalability.<br /> <br /> In this latest Data Science Central podcast, The Foundation for Your AI Strategy: MLOps 101, you’ll find answers to the following questions:<br /> <br /> <br /> What is MLOps? And why is it needed for organizations in my industry to become AI-driven?<br /> What are the core elements of an MLOps infrastructure?<br /> How can MLOps tools deliver trusted, scalable, and secure infrastructure for machine learning projects and use cases applicable to my industry?<br /> How can MLOps help data science teams, business leaders, and IT professionals build a resilient and scalable foundation for their AI initiatives?<br /> <br /> Speaker: <br /> Ari Kaplan, Director of AI Evangelism and Strategy - DataRobot<br /> <br /> Hosted by:<br /> Sean Welch, Host and Producer - Data Science Central DSC Podcast Series: MLOps Agents: Provide Centralized Monitoring for All Your Production Models tag:www.datasciencecentral.com,2021-03-08:6448529:Video:1042688 2021-03-08T23:58:27.916Z Sean Welch https://www.datasciencecentral.com/profile/SeanWelch <a href="https://www.datasciencecentral.com/video/dsc-podcast-series-mlops-agents-provide-centralized-monitoring"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/8644282693?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Machine Learning Operations (MLOps) allows organizations to deploy, manage, monitor, and govern their machine learning models from a single place, regardless of how they were created or where they are located. This empowers stakeholders to seamlessly collaborate on the common goal of scaling and managing trusted machine learning models in… <a href="https://www.datasciencecentral.com/video/dsc-podcast-series-mlops-agents-provide-centralized-monitoring"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/8644282693?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Machine Learning Operations (MLOps) allows organizations to deploy, manage, monitor, and govern their machine learning models from a single place, regardless of how they were created or where they are located. This empowers stakeholders to seamlessly collaborate on the common goal of scaling and managing trusted machine learning models in production.<br /> <br /> In this latest Data Science Central podcast, MLOps Agents: Provide Centralized Monitoring for All Your Production Models, we review how organizations can enable centralized monitoring of machine learning models using MLOps Agents.<br /> <br /> Learn how MLOps Agents:<br /> <br /> <br /> Monitor model behavior, including critical events, performance, and availability.<br /> Capture information then send it to a centralized MLOps server, making it much easier to detect and diagnose issues occurring in any production model.<br /> Track models in your preferred environment, meaning you can centrally monitor models deployed in any location on any infrastructure.<br /> <br /> Listen to this podcast to take your first step to creating a center of excellence for your production AI.<br /> <br /> Speaker: <br /> Seph Mard, Head of Model Risk, Director of Product - DataRobot<br /> <br /> Hosted by:<br /> Sean Welch, Host and Producer - Data Science Central DSC Podcast Series: Wrangle the Data Abyss Part 1: Scoping tag:www.datasciencecentral.com,2021-01-28:6448529:Video:1015321 2021-01-28T21:25:47.261Z Sean Welch https://www.datasciencecentral.com/profile/SeanWelch <a href="https://www.datasciencecentral.com/video/dsc-podcast-series-wrangle-the-data-abyss-part-1-scoping"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/8491453683?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>The hardest part of being an analyst isn’t building the dashboard, it is getting the data in a state where you can gain insights. Data is always messy, in many different places, and often different platforms making the back end work take up the majority of the time to create a dashboard. In part one of the Wrangle the Data Abyss series we dive… <a href="https://www.datasciencecentral.com/video/dsc-podcast-series-wrangle-the-data-abyss-part-1-scoping"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/8491453683?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />The hardest part of being an analyst isn’t building the dashboard, it is getting the data in a state where you can gain insights. Data is always messy, in many different places, and often different platforms making the back end work take up the majority of the time to create a dashboard. In part one of the Wrangle the Data Abyss series we dive deep to cover the most important and often overlooked step of the analytics process: scoping. Listen to this latest Data Science Central podcast to learn how to best handle the initial request to get the direction you need, thoroughly evaluate the available data, and create a plan to set you up for success.<br /> <br /> Speaker: <br /> Lauren Alexander, Senior Marketing Analyst - Tableau<br /> <br /> Hosted by: <br /> Sean Welch, Host and Producer - Data Science Central DSC Podcast Series: 10 Keys to AI Success in 2021 - Part 1: Driving AI with ROI tag:www.datasciencecentral.com,2021-01-22:6448529:Video:1010792 2021-01-22T17:42:22.541Z Sean Welch https://www.datasciencecentral.com/profile/SeanWelch <a href="https://www.datasciencecentral.com/video/dsc-datarobot-december-16-podcast-ari-pt1"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/8467289477?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>According to a recent PwC report, AI could contribute up to $15.7 trillion to the global economy by 2030, with companies around the world adopting AI at a rapid pace.  However, adopting AI is often difficult and achieving AI success can be elusive if not built, deployed and monitored correctly.  To shed more light on how to achieve AI success in the new year, we… <a href="https://www.datasciencecentral.com/video/dsc-datarobot-december-16-podcast-ari-pt1"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/8467289477?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />According to a recent PwC report, AI could contribute up to $15.7 trillion to the global economy by 2030, with companies around the world adopting AI at a rapid pace.  However, adopting AI is often difficult and achieving AI success can be elusive if not built, deployed and monitored correctly.  To shed more light on how to achieve AI success in the new year, we polled global enterprise AI Leaders to collect their feedback on the challenges and successes they’ve had with AI.<br /> <br /> In part 1 of 2 of this latest Data Science Central podcast, ‘10 Keys to AI Success in 2021’, Ari Kaplan, AI Evangelist at DataRobot, shares experience and knowledge from industry leaders on current AI topics including:<br /> <br /> AI with ROI: Delivering Results with value and urgency<br /> Transparent AI storytelling<br /> Building an AI governance framework<br /> Building Trusted AI<br /> Hyperscaling your AI<br /> <br /> Speaker:<br /> Ari Kaplan, AI Evangelist - DataRobot<br /> <br /> Hosted by:<br /> Sean Welch, Host and Producer - Data Science Central