All Videos Tagged Science (Data Science Central) - Data Science Central 2020-01-18T01:34:19Z https://www.datasciencecentral.com/video/video/listTagged?tag=Science&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: 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: How to Use Time Series Data to Forecast at Scale tag:www.datasciencecentral.com,2019-09-12:6448529:Video:887298 2019-09-12T20:55:33.580Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-how-to-use-time-series-data-to-forecast-at"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/3553482484?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>The growing popularity of sensor networks and telemetry applications has lead to the collection of a vast amount of time series data, which enables forecasting for a multitude of use cases from application performance optimization to workload anomaly detection. The challenge is to automate a historically manual process handcrafted for the… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-how-to-use-time-series-data-to-forecast-at"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/3553482484?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />The growing popularity of sensor networks and telemetry applications has lead to the collection of a vast amount of time series data, which enables forecasting for a multitude of use cases from application performance optimization to workload anomaly detection. The challenge is to automate a historically manual process handcrafted for the analysis of a single data series of just tens of data points to large scale processing of thousands of time series and millions of data points.<br /> <br /> In this latest Data Science Central webinar, we will demonstrate how to leverage InfluxDB to implement some solutions to tackle on the issues of time series forecasting at scale, including continuous accuracy evaluation and algorithm hyperparameters optimization. As a real world use case, we will discuss the storage forecasting implementation in Veritas Predictive Insights which is capable of training, evaluating and forecasting over 70,000 time series daily.<br /> <br /> Speaker:<br /> Marcello Tomasini, Sr. Data Scientist - Veritas Technologies<br /> <br /> Hosted by:<br /> Rafael Knuth, Contributing Editor - Data Science Central DSC Webinar Series: From Pandas to Apache Spark™ tag:www.datasciencecentral.com,2019-07-03:6448529:Video:851584 2019-07-03T19:18:10.864Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-from-pandas-to-apache-spark"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/3189210470?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>***Please be aware there is a slight audio issue from approximately 10:45-13:00 in the recording***<br></br> <br></br> Presenting Koalas, a new open source project unveiled by Databricks, that brings the simplicity of pandas to the scalability powers of Apache Spark™.<br></br> <br></br> Data science with Python has exploded in popularity over the past few years and… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-from-pandas-to-apache-spark"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/3189210470?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />***Please be aware there is a slight audio issue from approximately 10:45-13:00 in the recording***<br /> <br /> Presenting Koalas, a new open source project unveiled by Databricks, that brings the simplicity of pandas to the scalability powers of Apache Spark™.<br /> <br /> Data science with Python has exploded in popularity over the past few years and pandas has emerged as the lynchpin of the ecosystem. When data scientists get their hands on a data set, pandas is often the most common exploration tool. It is the ultimate tool for data wrangling and analysis. In fact, pandas’ read_csv is often the very first command students run in their data science journey.<br /> <br /> The problem? pandas does not scale well to big data. It was designed for small data sets that a single machine could handle. On the other hand, Apache Spark has emerged as the de facto standard for big data workloads. Today many data scientists use pandas for coursework, and small data tasks. When they work with very large data sets, they either have to migrate their code to PySpark's close but distinct API or downsample their data so that it fits for pandas.<br /> <br /> Now with Koalas, data scientists get the best of both worlds and can make the transition from a single machine to a distributed environment without needing to learn a new framework.<br /> <br /> In this latest Data Science Central webinar, the developers of Koalas will show you how:<br /> <br /> Koalas removes the need to decide whether to use pandas or PySpark for a given data set<br /> For work that was initially written in pandas for a single machine, Koalas allows data scientists to scale up their code on Spark by simply switching out pandas for Koalas<br /> Koalas unlocks big data for more data scientists in an organization since they no longer need to learn PySpark to leverage Spark<br /> <br /> Speakers:<br /> Tony Liu, Product Manager, Machine Learning - Databricks<br /> Tim Hunter, Sr. Software Engineer and Technical Lead, Co-Creator of Koalas - Databricks<br /> <br /> Hosted by:<br /> Stephanie Glen, Editorial Director - Data Science Central DSC Webinar Series: Making AI Work in the Real World: How Real Companies Get Real Value with AI tag:www.datasciencecentral.com,2019-07-02:6448529:Video:850987 2019-07-02T16:16:14.793Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-making-ai-work-in-the-real-world-how-real"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/3181882056?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>If you’ve read your fair share of tech press, you’ve certainly been exposed to breathless forecasts about the promise and power of artificial intelligence. The thing is, a lot of those articles are light on detail or focus too heavily on algorithms and not on business value.<br></br> <br></br> In this latest Data Science Central webinar, Alyssa… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-making-ai-work-in-the-real-world-how-real"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/3181882056?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />If you’ve read your fair share of tech press, you’ve certainly been exposed to breathless forecasts about the promise and power of artificial intelligence. The thing is, a lot of those articles are light on detail or focus too heavily on algorithms and not on business value.<br /> <br /> In this latest Data Science Central webinar, Alyssa Simpson Rochwerger of Figure Eight takes an industry-by-industry perspective on true AI adoption.<br /> <br /> We will cover:<br /> <br /> An approach to AI that realizes business value<br /> Real-world examples of businesses using AI to improve their bottom line<br /> Real-world use cases in e-commerce, enterprise software, robotics &amp; IoT, AgTech, and more<br /> <br /> <br /> We will separate the hype from the reality, the theoretical from the practical, and the research labs from ROI.<br /> <br /> <br /> <br /> Speaker:<br /> Alyssa Simpson Rochwerger, VP of Product - Figure Eight<br /> <br /> Hosted by:<br /> Rafael Knuth, Contributing Editor - Data Science Central