All Videos Tagged ML (Data Science Central) - Data Science Central 2020-01-27T01:02:05Z https://www.datasciencecentral.com/video/video/listTagged?tag=ML&rss=yes&xn_auth=no 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 DSC Webinar Series: Data Mastering at Scale tag:www.datasciencecentral.com,2019-10-30:6448529:Video:903806 2019-10-30T00:48:10.189Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-data-mastering-at-scale"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/3686726325?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Master data management (MDM) software turned 15 years old this year.<br></br> <br></br> Originally launched in 2004 by SAP, master data management systems aimed to help resolve the data unification problem by creating a central source of standardized references to customers, products, employees, suppliers, physical assets and other data across their many IT… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-data-mastering-at-scale"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/3686726325?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Master data management (MDM) software turned 15 years old this year.<br /> <br /> Originally launched in 2004 by SAP, master data management systems aimed to help resolve the data unification problem by creating a central source of standardized references to customers, products, employees, suppliers, physical assets and other data across their many IT systems.<br /> <br /> MDM is valuable, but it’s also slow, labor-intensive, and costly. As the scale of MDM projects increases to millions of entities and hundreds or thousands of data sources, the traditional methods often fail.<br /> <br /> Mike Stonebraker will share his view on how MDM technology and MDM organizations must change to fulfill the promise of MDM at scale. In this latest Data Science Central webinar, we will review:<br /> <br /> Why large enterprises need data management solutions that solve data mastering challenges at scale<br /> Why traditional, rule-based, data mastering options are struggling to keep up<br /> How Machine Learning can be used to address large-scale data mastering challenges<br /> <br /> Speaker:<br /> Mike Stonebraker, CTO &amp; Co-Founder - Tamr, Inc.<br /> <br /> Hosted by:<br /> Stephanie Glen, Editorial Director - Data Science Central DSC Webinar Series: Forecasting Using TensorFlow and FB's Prophet tag:www.datasciencecentral.com,2019-10-18:6448529:Video:899832 2019-10-18T01:02:41.579Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-forecasting-using-tensorflow-and-fb-s-prophet"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/3667408762?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>We live in a time where we are able to monitor everything--servers, containers, fitness levels, power consumption, etc. Making predictions on time series data is often just as important as monitoring is. In this latest Data Science Central webinar, we will learn about how InfluxDB can be used with TensorFlow and FB's Prophet to make… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-forecasting-using-tensorflow-and-fb-s-prophet"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/3667408762?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />We live in a time where we are able to monitor everything--servers, containers, fitness levels, power consumption, etc. Making predictions on time series data is often just as important as monitoring is. In this latest Data Science Central webinar, we will learn about how InfluxDB can be used with TensorFlow and FB's Prophet to make predictions and solve data engineering problems.<br /> <br /> Speaker:<br /> Anais Dotis-Georgiou, Developer Advocate - InfluxData<br /> <br /> Hosted by:<br /> Rafael Knuth, Contributing Editor - Data Science Central