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• AI Governance
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    • Managed Vocabularies
    • Datasets
    • Benchmarks
    • Batch Norm(alization) & Standardization
    • Data Preprocessing
    • Data Encoding
    • Data Cleaning
    • Feature Exploration/Learning
    • Data Interoperability
    • Data Augmentation, Data Labeling, and Auto-Tagging
    • Imbalanced Data
    • Privacy in Data Science
    • Bias and Variances
    • Excel - Data Analysis
• Hyperparameters
• Visualization
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• How can we improve Azure Data Catalog?
• Automate your data lineage
• Benefiting from AI: A different approach to data management is needed
• Git - GitHub and GitLab
• Global Community for Artificial Intelligence (AI) in Master Data Management (MDM) | Camelot Management Consultants

How is AI changing the game for Master Data Management? Tony Brownlee talks about the ability to inspect and find data quality issues as one of several ways cognitive computing technology is influencing master data management.

Introducing Roxie. Data Management Meets Artificial Intelligence. Introducing Roxie, Rubrik's Intelligent Personal Assistant. A hackathon project by Manjunath Chinni. Created in 10 hours with the power of Rubrik APIs.

DAS Webinar: Master Data Management – Aligning Data, Process, and Governance Getting MDM “right” requires a strategic mix of Data Architecture, business process, and Data Governance.

IBM MDM Feature Spotlight: Machine learning-assisted Data Stewardship This three minute overview shows the benefits of using machine learning models trained by a clients' own data stewards to facilitate faster resolution of pending clerical tasks in IBM Master Data Management Standard Edition.

Better Machine Learning Outcomes rely on Modern Data Management Tarun Batra, CEO, LumenData, talks about how the movement towards artificial intelligence and machine learning relies on a Modern Data Management platform that is able to correlate large amounts of data, and provide a reliable data foundation for machine learning algorithms to deliver better business outcomes. In this video, Tarun discusses: Key industry trends driving Modern Data Management, Data management best practices, Creating joint value for customers "There is a lot of movement towards artificial intelligence and machine learning as being the next big domain that organizations are focusing on. With data volumes continuing to increase, and the velocity of change of data, decisions have to be made in an automated, data-driven fashion for organizations to remain competitive. Machine learning can predict and recommend actions, but a reliable data foundation through MDM that continuously manages and ensures data quality is essential for machine learning algorithms to create accurate, meaningful insight." - Tarun Batra

Top 10 Mistakes in Data Management Come learn about the mistakes we most often see organizations make in managing their data. Also learn more about Intricity's Data Management Health Check which you can download here: http://www.intricity.com/intricity101/ To Talk with a Specialist go to: http://www.intricity.com/intricity101/ www.intricity.com

What is Data Governance? Understand what problems a Data Governance program is intended to solve and why the Business Users must own it. Also learn some sample roles that each group might need to play.

How to manage Artificial Intelligence Data Collection [Enterprise AI Governance Data Management ] Mind Data AI AI researcher Brian Ka Chan's AI ML DL introduction series. Collecting Data is an important step to the success of Artificial intelligence Program in the 4th industrial Revolution. In the current advancement of Artificial Intelligence technologies, machine learning has always been associated with AI, and in many cases, Machine Learning is considered equivalent of Artifical Intelligence. Machine learning is actually a subset of Artificial Intelligence, this discipline of machine learning relies on data to perform AI training, supervised or unsupervised. On average, 80% of the time that my team spent in AI or Data Sciences projects is about preparing data. Preparing data includes, but not limited to: Identify Data required, Identify the availability of data, and location of them, Profiling the data, Source the data, Integrating the data, Cleanse the data, and prepare the data for learning