All Videos Tagged Data Preparation”, (Data Science Central) - Data Science Central 2021-09-21T03:03:16Z https://www.datasciencecentral.com/video/video/listTagged?tag=Data+Preparation%E2%80%9D%2C&rss=yes&xn_auth=no 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: Accelerate Analytics Projects with Data Prep on AWS tag:www.datasciencecentral.com,2019-08-15:6448529:Video:869444 2019-08-15T21:54:59.827Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-accelerate-analytics-projects-with-data-prep"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/3428295029?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Leveraging the benefits of effective data preparation to help build a modern ERP system is a vital component in innovating an organization's data workflow systems. Complex pattern matching and parsing of unstructured data requires a great deal of time and effort often utilizing labor-intensive hand coding.<br></br> <br></br> Join us for this… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-accelerate-analytics-projects-with-data-prep"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/3428295029?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Leveraging the benefits of effective data preparation to help build a modern ERP system is a vital component in innovating an organization's data workflow systems. Complex pattern matching and parsing of unstructured data requires a great deal of time and effort often utilizing labor-intensive hand coding.<br /> <br /> Join us for this latest Data Science Central webinar to learn how B/A Products Company has managed to cut 6-12 months process time of reformatting, restructuring and preparing data down to only 2 months through automation and simplification.<br /> <br /> In this webinar you will:<br /> <br /> • Understand technology trends that simplify your analytics modernization journey<br /> • Learn about the challenges and solutions that B/A Products Company used to solve their issues with legacy ERP systems<br /> • Learn how to accelerate time-to-value for analytics projects with data preparation on AWS<br /> • See in action the before / after with the solution live demo<br /> <br /> Speakers:<br /> Jacob S J Joseph, Information Systems Manager - B/A Products Co.<br /> Samantha Winters, Director of Marketing and Business Analytics - B/A Products Co.<br /> Matt Derda, Customer Marketing Manager - Trifacta<br /> <br /> Hosted by:<br /> Stephanie Glen, Editorial Director - Data Science Central DSC Webinar Series: Why Cloud Data Management Requires Modern DataOps Featuring Forrester tag:www.datasciencecentral.com,2019-04-10:6448529:Video:816902 2019-04-10T21:14:25.765Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-why-cloud-data-management-requires-modern"><br /> <img alt="Thumbnail" height="135" src="https://storage.ning.com/topology/rest/1.0/file/get/1869451575?profile=original&amp;width=240&amp;height=135" width="240"></img><br /> </a> <br></br>Over the past few years, the evolution of technology for storing, processing and analyzing data has been absolutely staggering. The advent of cloud platforms from AWS, Microsoft Azure and Google Cloud gives every business the ability to swipe a credit card and have access to virtually any computing service...to handle just about any data… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-why-cloud-data-management-requires-modern"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/1869451575?profile=original&amp;width=240&amp;height=135" width="240" height="135" alt="Thumbnail" /><br /> </a><br />Over the past few years, the evolution of technology for storing, processing and analyzing data has been absolutely staggering. The advent of cloud platforms from AWS, Microsoft Azure and Google Cloud gives every business the ability to swipe a credit card and have access to virtually any computing service...to handle just about any data initiative. Yet, why are so many organizations still struggling to drive meaningful ROI from their data investments? The answer starts with DataOps.<br /> <br /> Join this latest Data Science Central webinar to learn:<br /> • How the emergence of new trends such as cloud, machine learning and AI have impacted the data management landscape<br /> • What is DataOps and key considerations in implementing or transitioning data management to the cloud<br /> • How to maximize cloud investments and adoption with new approaches to data preparation and data quality<br /> <br /> Featured Speakers:<br /> Noel Yuhanna, Principal Analyst - Forrester<br /> Will Davis, Director of Product Marketing - Trifacta<br /> <br /> Hosted by:<br /> Stephanie Glen, Editorial Director - Data Science Central DSC Webinar Series: Clean Data & Accurate ML Models tag:www.datasciencecentral.com,2019-02-18:6448529:Video:803540 2019-02-18T23:02:30.627Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-clean-data-accurate-ml-models"><br /> <img alt="Thumbnail" height="0" src="https://storage.ning.com/topology/rest/1.0/file/get/1136933577?profile=original&amp;width=0&amp;height=0" width="0"></img><br /> </a> <br></br>Predictive analytics can provide your organization with data insights and differentiation to rise above the competition. However, Machine learning (ML) outcomes are only as good as the data they are built upon. Getting the data ready for accurate modeling is time consuming, cumbersome, and a waste of data professionals’ skills to be polishing the… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-clean-data-accurate-ml-models"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/1136933577?profile=original&amp;width=0&amp;height=0" width="0" height="0" alt="Thumbnail" /><br /> </a><br />Predictive analytics can provide your organization with data insights and differentiation to rise above the competition. However, Machine learning (ML) outcomes are only as good as the data they are built upon. Getting the data ready for accurate modeling is time consuming, cumbersome, and a waste of data professionals’ skills to be polishing the materials they rely on while they should focus on the work that matters—creating accurate predictions that improve products, services, and organizational efficiency.<br /> <br /> In this latest Data Science Central webinar, we will see how the data preparation process can be streamlined to produce an accurate model for Amazon SageMaker. Guest speaker Kris Skrinak, Machine Learning Segment Lead from Amazon Web Services Partner Network will provide deep insights.<br /> <br /> Join this webinar and you will learn:<br /> <br /> The modern scalable and agile cloud data pipeline for analytics and ML applications<br /> What it takes to deliver accurate models leveraging Amazon SageMaker<br /> Typical data flaws and how to remediate them with Trifacta data preparation solutions<br /> End to end demo from data acquisition, cleansing to feature engineering and modeling with Trifacta &amp; Amazon SageMaker<br /> <br /> Speakers:<br /> Vijay Balasubramaniam, Sr. Partner Solutions Architect - Trifacta<br /> <br /> Kris Skrinak, Machine Learning Segment Lead – Amazon Web Services<br /> <br /> Hosted by:<br /> Bill Vorhies, Editorial Director - Data Science Central DSC Webinar Series: Data Prep & Automated ML: Better Predictions for Consensus tag:www.datasciencecentral.com,2019-02-18:6448529:Video:803474 2019-02-18T22:58:14.477Z Tim Matteson https://www.datasciencecentral.com/profile/2edcolrgc4o4b <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-data-prep-automated-ml-better-predictions-for"><br /> <img alt="Thumbnail" height="0" src="https://storage.ning.com/topology/rest/1.0/file/get/1136869624?profile=original&amp;width=0&amp;height=0" width="0"></img><br /> </a> <br></br>Financed smartphones are a magnet for identity theft, leaving retailers in the digital and telecommunication industry vulnerable to fraud. Consensus, a Target-owned subsidiary, has developed a highly accurate solution to identify fraud at the point-of-sale before it happens.<br></br> <br></br> In this latest Data Science Central webinar, you… <a href="https://www.datasciencecentral.com/video/dsc-webinar-series-data-prep-automated-ml-better-predictions-for"><br /> <img src="https://storage.ning.com/topology/rest/1.0/file/get/1136869624?profile=original&amp;width=0&amp;height=0" width="0" height="0" alt="Thumbnail" /><br /> </a><br />Financed smartphones are a magnet for identity theft, leaving retailers in the digital and telecommunication industry vulnerable to fraud. Consensus, a Target-owned subsidiary, has developed a highly accurate solution to identify fraud at the point-of-sale before it happens.<br /> <br /> In this latest Data Science Central webinar, you will learn how Consensus put together agile processes on a cloud analytic solution leveraging Trifacta data preparation and DataRobot automated machine learning to prevent fraud.<br /> <br /> Attendees will learn:<br /> <br /> • How Consensus developed an AWS cloud-based solution<br /> • The role of data preparation in supplying accurate data for machine learning models<br /> • How automated machine learning can drive more accurate predictions<br /> • Consensus’s time-saving ROI from building models, deploying them on AWS, and the improvement in accuracy and recall<br /> <br /> Speakers:<br /> David McNamara, Lead Product Specialist - Trifacta<br /> Harrison Lynch, Sr. Director of PM - Consensus Corporation<br /> Rajiv Shah, Data Scientist - DataRobot<br /> <br /> Hosted by:<br /> Bill Vorhies, Editorial Director - Data Science Central