In this video, listen as IBM data science evangelist James Kobielus talks with Dean Wampler, a fast data product architect with the office of the CTO at Lightbend, about how data scientists can access the open functionality and expertise that are central to their work.
Data scientists and others often encapsulate big data by its dimensions known as the four Vs: volume, variety, velocity and veracity. But when considering big data as a source for insight to enhance decision making, it may be best characterized by its three Cs—confidence, context and choice—with
Spark’s built-in machine-learning library (MLlib) provides a key differentiator from predecessor open source technologies and leverages Spark’s distributed, in-memory execution model. Take a look at some practical applications for specific Spark machine-learning algorithms in three advanced
A world that grows increasingly complex calls for disruptive innovation in an open, collaborative environment. See how open data science provides an ecosystem of expertise, skill sets and advanced open source data science tools that fuels collaborative creativity in the development and deployment
As Spark continues to mature into mainstream adoption in the data science community, the open data analytics stack and open source tools grow more robust, giving data scientists rich core workbenches to develop evermore innovative applications.
With BigInsights having established itself as a leader and with IBM focused on a Cloud First Strategy, we saw the opportunity to help customers reduce these capital and management costs, to enable them to focus on running the analytics for business advantage while providing BigInsights on a dynamic
A growing number of businesses and industries are finding innovative ways to apply graph analytics to a variety of use-case scenarios because it affords a unique perspective on the analysis of networked entities and their relationships. Gain an understanding of how four different types of graph
Open source is a disruptor that never quits, and it is seemingly penetrating and transforming every aspect of established data, analytics and application ecosystems. Give this podcast, recorded at IBM InterConnect 2016, a listen to learn how open source initiatives are transforming machine learning.
As a foundation for data lakes and refineries, NoSQL databases provide access, processing and storage to structured and unstructured data for high-performance statistical modeling and exploration. Take a look at the multitude of advantages of NoSQL databases and opportunities to bridge them to open
Performing programmatic actions on data across services is quite possible in today’s technology ecosystem. And now, the transfer of data across services such as the dashDB data warehouse and deploying it in new environments is also possible. However, the questions often asked by customers center on
Open source is a disruptor that never quits. It seems to be penetrating and transforming every aspect of established data, analytics and application ecosystems. In this podcast, recorded at IBM InterConnect 2016, listen to David Taieb, a cloud data services developer advocate at IBM, share his