AI/ML

Presented at World AI & RPA Show, MalaysiaTo support machine learning, many organizations leverage data lakes, as they can collect large volumes of data from multiple sources, including structured and unstructured sources, and store the data in its original format. However, storing data in different formats does not necessarily facilitate discovery, as data in different formats must first be integrated before it can be leveraged for machine learning. Due to the increasingly distributed nature of data infrastructure in today’s enterprises, data integration gets more complex.Data...

Demo by Stanley Zhang, Sr. Sales Engineer at Denodo.

Presentation by Stan Wu, Vice President of Sales, Greater China at Denodo.

Demo by Edwin Robbins, Sales Engineer at Denodo.

Presented at World AI Show 2021, PhilippinesIn AI/ML systems, applications or machines are set up to learn from new information, and gradually, over time, they acquire the intelligence to support day-to-day business processes and decision making. The more data they receive, the more they learn, and the more accurate their predictions are. However, extracting data from multiple sources and then replicating it to a central repository is the old and inefficient way. This approach results in 80% of the project time being spent on data acquisition and preparation tasks.Data virtualization is a...

Presented at Data Architecture Online A/NZThe current data landscape is fragmented, not just in location but also in terms of processing paradigms: data lakes, IoT architectures, NoSQL and graph data stores, SaaS applications, etc. are found coexisting with relational databases to fuel the needs of modern analytics, ML and AI. The physical consolidation of enterprise data into a central repository, although possible, is both expensive and time consuming.A logical data warehouse is a modern data architecture that allows organisations to leverage all of their data irrespective of where the data...

Advanced data science techniques, like machine learning, have proven to be extremely useful to derive valuable insights from your data. Data Science platforms have become more approachable and user friendly. With all the advancements in the technology space, the Data Scientist is still spending most of the time massaging and manipulating the data into a usable data asset. How can we empower the data scientist? How can we make data more accessible, and foster a data sharing culture?Join us, and we will show you how Data Virtualization can do just that, with an agile and AI/ML laced data...