All Videos Tagged Machine (Data Science Central) - Data Science Central 2020-05-25T22:06:15Z https://www.datasciencecentral.com/video/video/listTagged?tag=Machine&rss=yes&xn_auth=no DSC Webinar Series: A Collaborative Approach to Machine Learning tag:www.datasciencecentral.com,2020-05-06:6448529:Video:950224 2020-05-06T22:52:03.941Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-a-collaborative-approach-to-machine-learning"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/4798479864?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>It's generally accepted that you need a team with a wide variety of skills to build modern machine learning (ML) pipelines and make them operational. But what does that team look like, and how do they work together? These questions are especially important when the skills required are particularly specialized. When you’re developing… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-a-collaborative-approach-to-machine-learning"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/4798479864?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />It's generally accepted that you need a team with a wide variety of skills to build modern machine learning (ML) pipelines and make them operational. But what does that team look like, and how do they work together? These questions are especially important when the skills required are particularly specialized. When you’re developing interactive clinical and healthcare applications (backed by specialized statistical methods and made to scale on terabytes of highly complex data), you want the right people by your side. <br /> <br /> In this latest Data Science Central webinar, we'll describe 3 healthcare and life sciences projects in which business analysts, data scientists, and ML engineers collaborated. These applications and use cases are not only focused on healthcare but across a number of industries.<br /> <br /> Tune in to learn about the latest advances in big data analytics and artificial intelligence (AI) from PerkinElmer. And see real-life examples of how TIBCO data science and analytics solutions combined with the PerkinElmer AI platform can be used to create:<br /> <br /> <br /> An imaging-based phenotypic screening of cell-based disease models using high-content screening (HCS)<br /> Clinical Translational systems designed to detect and score significant biomarkers in clinical prognostication <br /> <br /> Also, find out how, in just three days, a diverse team from TIBCO responded to a critical healthcare challenge, analyzing health outcomes using socioeconomic data from countries around the world.<br /> <br /> Speakers:<br /> <br /> Alberto Pascual, Sr. Manager of AI and Analytics Innovation - PerkinElmer, Inc.<br /> Steven Hillion, Sr. Director of Data Science - TIBCO Software, Inc. <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: Edge Computing with Real-time Analytics at Scale tag:www.datasciencecentral.com,2019-12-12:6448529:Video:914389 2019-12-12T23:46:52.594Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-edge-computing-with-real-time-analytics-at"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/3767434029?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Performing analytics at the edge is needed in today’s distributed landscape. Edge Computing allows the flexibility of virtualized computation, network and storage resources to the edge, as an integrated solution combined with ML and AI libraries. At the heart of the solution is the open-source time series database, InfluxDB, and the data… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-edge-computing-with-real-time-analytics-at"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/3767434029?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Performing analytics at the edge is needed in today’s distributed landscape. Edge Computing allows the flexibility of virtualized computation, network and storage resources to the edge, as an integrated solution combined with ML and AI libraries. At the heart of the solution is the open-source time series database, InfluxDB, and the data processing framework Kapacitor.<br /> <br /> In this latest Data Science Central webinar, we will share how to build this point-and-click solution to help customers unlock the power of high-frequency data in real-time to become a data-driven organization.<br /> <br /> Speakers:<br /> Anil Joshi, CEO - AnalyticsPlus, Inc.<br /> Pankaj Bhagra, Co-Founder and Software Architect - Nebbiolo Technologies<br /> <br /> Hosted by:<br /> Rafael Knuth, Contributing Editor - Data Science Central DSC Webinar Series: Train & Tune Your Computer Vision Models at Scale tag:www.datasciencecentral.com,2019-12-06:6448529:Video:913214 2019-12-06T05:21:19.642Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-train-tune-your-computer-vision-models-at"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/3757286762?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Whether you are training a self-driving car, detecting animals with drones, or identifying car damage for insurance claims, the steps needed to effectively train a computer vision model at scale remain the same.<br></br> <br></br> In this latest Data Science Central webinar, we’ll walk through best practices for managing a computer vision project… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-train-tune-your-computer-vision-models-at"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/3757286762?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Whether you are training a self-driving car, detecting animals with drones, or identifying car damage for insurance claims, the steps needed to effectively train a computer vision model at scale remain the same.<br /> <br /> In this latest Data Science Central webinar, we’ll walk through best practices for managing a computer vision project including staffing, budgeting, and roles and responsibilities. Learn how to collect and label the data that will train and tune your machine learning algorithm, and which types of data labeling best fit your project along with the tools that will get the job done.<br /> <br /> In this webinar, you’ll learn how to:<br /> <br /> Identify key success factors when scoping a computer vision project<br /> Determine what kind of source data you need to make it successful<br /> Select tools that best fit your project<br /> Label your dataset so your algorithms can learn and perform as designed<br /> <br /> Speaker:<br /> Meeta Dash, Director of Product - Figure Eight<br /> <br /> Hosted by:<br /> Stephanie Glen, Editorial Director - Data Science Central DSC Webinar Series: Enterprise-ready Data Science and ML with Python tag:www.datasciencecentral.com,2019-11-19:6448529:Video:909443 2019-11-19T23:49:48.055Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-enterprise-ready-data-science-and-ml-with"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/3720869848?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Many Data Scientists spend much of their time on laptops, working with familiar tools like Jupyter and Conda, on data that fits on their machine.<br></br> <br></br> In this latest Data Science Central webinar we will discuss a laptop-like experience for Data Science and Machine Learning, supporting the same tools and workflows you have become… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-enterprise-ready-data-science-and-ml-with"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/3720869848?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Many Data Scientists spend much of their time on laptops, working with familiar tools like Jupyter and Conda, on data that fits on their machine.<br /> <br /> In this latest Data Science Central webinar we will discuss a laptop-like experience for Data Science and Machine Learning, supporting the same tools and workflows you have become accustomed to. We will highlight how Databricks augments that experience with collaborative features like co-editing and commenting, as well as enterprise-level security, scalability, and reliability.<br /> <br /> Speaker:<br /> Clemens Mewald, Director of Product Management, Machine Learning and Data Science - Databricks<br /> <br /> Hosted by:<br /> Rafael Knuth, Contributing Editor - Data Science Central