All Videos Tagged Optimization” (Data Science Central) - Data Science Central 2021-02-25T21:51:50Z https://www.datasciencecentral.com/video/video/listTagged?tag=Optimization%E2%80%9D&rss=yes&xn_auth=no DSC Webinar Series: How to Drive Python Model ROI & User Engagement tag:www.datasciencecentral.com,2020-11-12:6448529:Video:1001430 2020-11-12T23:52:56.528Z Sean Welch https://www.datasciencecentral.com/profile/SeanWelch <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-how-to-drive-python-model-roi-user-engagement"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/8155694864?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Python is one of the most popular modeling languages in the world, and yet more than half of the time, developers fail to deliver their advanced analytics to the business end-user. The main barrier is the lack of a deployment platform that converts models into user-friendly applications. Instead of having to continue running these models… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-how-to-drive-python-model-roi-user-engagement"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/8155694864?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Python is one of the most popular modeling languages in the world, and yet more than half of the time, developers fail to deliver their advanced analytics to the business end-user. The main barrier is the lack of a deployment platform that converts models into user-friendly applications. Instead of having to continue running these models yourself or relying on a patchwork of open-source platforms, we’d like to (re-)introduce you to an enterprise-ready solution that fully integrates with Python to convert your analytics into a useable, interactable form: FICO Xpress Insight.<br /> <br /> If you work with Python, don’t miss this chance to learn how to overcome all of the "last-mile” obstacles and actually generate applications that allow for what-if analyses, reporting, user management, load balancing, drag-and-drop UI creation capabilities, and more.<br /> <br /> In today’s Data Science Central webinar, we’ll show you:<br /> <br /> How to deploy a Python model in FICO Xpress Insight in 5 minutes<br /> Customization options for Python models<br /> Practical demonstrations of several common use cases<br /> <br /> Finally, data scientists and operations researchers can stop wasting time, money, and effort on advanced analytics that never get put to use—be sure to join us!<br /> <br /> Speakers:<br /> Dr. Oliver Bastert, Vice President of Product Management - FICO<br /> Dr. Johannes Mueller, Senior Optimization Modeler - FICO<br /> Dr. Vladimir Roitch, Senior Scientist - FICO<br /> <br /> Hosted by: <br /> Bill Vorhies, Editorial Contributor - Data Science Central DSC Webinar Series: Optimization and The NFL’s Toughest Scheduling Problem tag:www.datasciencecentral.com,2020-06-23:6448529:Video:959590 2020-06-23T22:13:10.013Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-optimization-and-the-nfl-s-toughest-scheduling"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/6247863064?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Learn how the National Football League (NFL) uses mathematical optimization to solve one of the hardest scheduling problems in existence.<br></br> <br></br> At first glance, the NFL’s scheduling problem seems simple: 5 people have 12 weeks to schedule 256 games over the course of a 17-week season. The scenarios are potentially well into the… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-optimization-and-the-nfl-s-toughest-scheduling"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/6247863064?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Learn how the National Football League (NFL) uses mathematical optimization to solve one of the hardest scheduling problems in existence.<br /> <br /> At first glance, the NFL’s scheduling problem seems simple: 5 people have 12 weeks to schedule 256 games over the course of a 17-week season. The scenarios are potentially well into the quadrillions. Making the problem particularly hard is the necessary inclusion of thousands of constraints addressing stadium availability, travel considerations, competitive equity, and television viewership.<br /> <br /> In this latest Data Science Central webinar, you will learn how the NFL began using Gurobi’s mathematical optimization solver to tackle this complex scheduling problem. With mathematical optimization, NFL planners can generate and analyze more than 50,000 feasible schedules despite adding more constraints to the process every year.  Now rather than spending months manually constructing one schedule, the NFL planners can focus on evaluating and comparing thousands of completed schedules to determine which should be selected as the final schedule.   <br /> <br /> In this webinar, you will learn:<br /> <br /> How the NFL uses mathematical optimization to solve one of the most challenging scheduling problems in existence.<br /> How the NFL switched from a linear to a parallel approach to optimization.<br /> <br /> Speaker: <br /> Mike North, Vice President of NFL Broadcast Planning &amp; Scheduling - NFL<br /> <br /> <br /> Hosted by:<br /> Sean Welch, Host and Producer - Data Science Central DSC Webinar Series: Learn How to Design and Deploy Optimization Applications tag:www.datasciencecentral.com,2020-05-22:6448529:Video:953426 2020-05-22T00:30:12.690Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-learn-how-to-design-and-deploy-optimization"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/5234201073?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Interested in learning how to build and deploy modern optimization applications that deliver tremendous business value?<br></br> <br></br> In this latest Data Science Central webinar, you will have the opportunity to see several live optimization application demos. These demos will showcase the power of mathematical optimization applications and… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-learn-how-to-design-and-deploy-optimization"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/5234201073?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Interested in learning how to build and deploy modern optimization applications that deliver tremendous business value?<br /> <br /> In this latest Data Science Central webinar, you will have the opportunity to see several live optimization application demos. These demos will showcase the power of mathematical optimization applications and demonstrate how you can deploy these applications on modern IT architectures like Amazon Web Services and Docker.<br /> <br /> We will cover well-known optimization problems, such as the facility location problem and workforce scheduling – we will give you an in-depth look at the user interface, the architecture, and the deployment of optimization applications. During this webinar, we will:<br /> <br /> <br /> Illustrate the business value of optimization.<br /> Demonstrate how to interact with an optimization application.<br /> Show how this application can be implemented within a modern IT architecture.<br /> Go over best practices in deploying your own optimization applications.<br /> <br /> Speaker: <br /> Richard Oberdieck, PhD, Technical Account Manager - Gurobi Optimization<br /> <br /> <br /> Hosted by:<br /> Sean Welch, Host and Producer - Data Science Central DSC Webinar Series: How to Create Mathematical Optimization Models with Python tag:www.datasciencecentral.com,2020-04-29:6448529:Video:948616 2020-04-29T23:08:58.350Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-how-to-create-mathematical-optimization-models"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/4566482083?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>With mathematical optimization, companies can capture the key features of their business problems in an optimization model and can generate optimal solutions (which are used as the basis to make optimal decisions). Data scientists with some basic mathematical programming skills can easily learn how to build, implement, and maintain… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-how-to-create-mathematical-optimization-models"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/4566482083?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />With mathematical optimization, companies can capture the key features of their business problems in an optimization model and can generate optimal solutions (which are used as the basis to make optimal decisions). Data scientists with some basic mathematical programming skills can easily learn how to build, implement, and maintain mathematical optimization applications.<br /> <br /> The Gurobi Python API borrows ideas from modeling languages, enabling users to deploy and solve mathematical optimization models with scripts that are easy to write, read, and maintain. Such modules can even be embedded in decision support systems for production-ready applications.<br /> <br /> In this latest Data Science Central webinar, we will:<br /> <br /> Discuss the motivation for using Python in mathematical optimization applications<br /> <br /> Help you understand the importance of parameterizing a mathematical optimization model<br /> Review some of the best practices for deploying mathematical optimization models in Python<br /> <br /> Speaker: <br /> Juan Orozco Guzman, Optimization Support Engineer- Gurobi Optimization<br /> <br /> Hosted by:<br /> Sean Welch, Host and Producer - Data Science Central DSC Webinar Series: Mathematical Optimization Modeling: Learn the Basics tag:www.datasciencecentral.com,2020-03-10:6448529:Video:936967 2020-03-10T23:19:58.139Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-mathematical-optimization-modeling-learn-the"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/4068705138?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Mathematical optimization (MO) technologies are being utilized today by leading global companies across industries – including aviation, energy, finance, logistics, telecommunications, manufacturing, media, and many more – to solve a wide range of complex, real-world problems, make optimal, data-driven decisions, and achieve greater… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-mathematical-optimization-modeling-learn-the"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/4068705138?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Mathematical optimization (MO) technologies are being utilized today by leading global companies across industries – including aviation, energy, finance, logistics, telecommunications, manufacturing, media, and many more – to solve a wide range of complex, real-world problems, make optimal, data-driven decisions, and achieve greater operational efficiency. An increasing number of data scientists are adding MO into their analytics toolbox and developing applications that combine MO and machine learning (ML) technologies. In this series of webinars, we will show you how – with MO techniques – you can build interpretable models to tackle your prediction and classification problems.<br /> <br /> In this latest Data Science Central webinar, you will learn:<br /> <br /> The main components of an MO problem.<br /> How to formulate an MO model.<br /> How to build an MO model using the Gurobi Python API.<br /> How to modify the original model formulation to accommodate changing conditions.<br /> How to implement changes in the model using the Gurobi Python API.<br /> <br /> Speaker:<br /> Pano Santos, Senior PhD, Senior Technical Manager - Gurobi Optimization<br /> <br /> Hosted by:<br /> Sean Welch, Host and Producer - Data Science Central