All Videos Tagged Machine (Data Science Central) - Data Science Central 2019-11-13T15:07:12Z https://www.datasciencecentral.com/video/video/listTagged?tag=Machine&rss=yes&xn_auth=no 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: Migrating R Applications to the Cloud using Databricks tag:www.datasciencecentral.com,2019-09-26:6448529:Video:891573 2019-09-26T22:43:53.494Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-migrating-r-applications-to-the-cloud-using"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/3630312078?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>R, along with Python, is the most popular language among enterprise data scientists. The R ecosystem includes thousands of packages for statistical analysis and machine learning as well as advanced graphical capabilities. R users across enterprises are expressing strong interest in leveraging cloud for R workloads. Cloud offers several… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-migrating-r-applications-to-the-cloud-using"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/3630312078?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />R, along with Python, is the most popular language among enterprise data scientists. The R ecosystem includes thousands of packages for statistical analysis and machine learning as well as advanced graphical capabilities. R users across enterprises are expressing strong interest in leveraging cloud for R workloads. Cloud offers several unique advantages, such as accessing ever-growing datasets, easily scaling up compute resources for processing large data, managing resources more cost efficiently.<br /> <br /> In this latest Data Science Central webinar, we will demonstrate how Databricks helps R users migrating their applications from legacy on-prem environments to public clouds such as AWS and Azure. We will cover:<br /> <br /> Seamless migration of models developed in desktop RStudio to RStudio in Databricks<br /> Leveraging Databricks Notebooks with MLflow to enhance their work<br /> Recording and notebooks will be provided after the webinar so that you can practice at your own pace.<br /> <br /> Speaker:<br /> Hossein Falaki, Tech Lead - Databricks<br /> <br /> Hosted by:<br /> Stephanie Glen, Editorial Director - Data Science Central DSC Webinar Series: Mathematical Optimization + ML: Featuring Forrester Survey Insights tag:www.datasciencecentral.com,2019-09-17:6448529:Video:888956 2019-09-17T23:25:28.960Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-mathematical-optimization-ml-featuring"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/3561625819?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Mathematical optimization (AKA Mixed Integer Programming) and Machine Learning (ML) are different but complementary technologies. Simply put – Mixed Integer Programming (MIP) answers questions that ML cannot. Machine learning makes predictions while MIP makes decisions. For Data Scientists to be effective, an understanding of MIP and when to use… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-mathematical-optimization-ml-featuring"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/3561625819?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Mathematical optimization (AKA Mixed Integer Programming) and Machine Learning (ML) are different but complementary technologies. Simply put – Mixed Integer Programming (MIP) answers questions that ML cannot. Machine learning makes predictions while MIP makes decisions. For Data Scientists to be effective, an understanding of MIP and when to use it is critical, as ML does not solve all problems effectively.<br /> <br /> In this latest Data Science Central webinar, you will hear the results of the 2019 Mathematical Optimization Survey commissioned by Gurobi and conducted by Forrester and insights on how Data Scientists can use tools such as MIP to make complex decisions.<br /> <br /> You’ll learn:<br /> - The latest trends in ML and Artificial Intelligence<br /> - Key findings from the Mathematical Optimization Survey<br /> - How you can use MIP in concert with ML techniques<br /> - How industries are using MIP today to efficiently use resources, often resulting in time savings and millions of dollars in cost savings<br /> <br /> All registrants will receive a copy of the 2019 Mathematical Optimization Study available in October 2019.<br /> <br /> Speakers:<br /> Mike Gualtieri, VP Principal Analyst, Application Development and Delivery Professionals - Forrester Research<br /> Edward Rothberg, CEO and Co-Founder - Gurobi Optimization<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 DSC Webinar Series: Scale AI/ML with Data Wrangling Featuring Forrester tag:www.datasciencecentral.com,2019-06-05:6448529:Video:834537 2019-06-05T22:24:26.704Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-scale-ai-ml-with-data-wrangling-featuring"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/2783842558?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>The Machine Learning Race is upon us. Every organization is seeking to outpace their competition by leveraging AI/ML to drive differentiation for their business. To win this race, companies are building up data science teams, investing in faster/more scalable cloud data platforms and utilizing the growing variety of publicly available… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-scale-ai-ml-with-data-wrangling-featuring"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/2783842558?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />The Machine Learning Race is upon us. Every organization is seeking to outpace their competition by leveraging AI/ML to drive differentiation for their business. To win this race, companies are building up data science teams, investing in faster/more scalable cloud data platforms and utilizing the growing variety of publicly available algorithms and ML toolkits. Yet, organizations ramping up these initiatives soon find that their ML processes are only useful if the data that is feeding them is clean and structured for the task at hand. They quickly learn that scaling machine learning is entirely dependent upon scaling data wrangling processes.<br /> <br /> Join Forrester VP &amp; Principal Analyst, Mike Gualtieri and Trifacta Head of Platform Product Management Mahesh Gandhe for a live webinar covering organizational best practices for scaling data preparation in order to scale ML and AI initiatives.<br /> <br /> In this latest Data Science Central webinar you can expect to learn:<br /> <br /> Common data prep bottlenecks in machine learning such as data quality, feature engineering &amp; data blending<br /> How data preparation platforms improve scale, collaboration and automation of wrangling data for AI<br /> Organizational best practices and deployment scenarios for data preparation &amp; machine learning in cloud, on-premises and hybrid/multi-cloud environments<br /> <br /> Featured Speakers:<br /> Mike Gualtieri, VP &amp; Principal Analyst - Forrester<br /> Mahesh Gandhe, Head of Platform Product Management - Trifacta<br /> <br /> Hosted by:<br /> Stephanie Glen, Editorial Director - Data Science Central