MapR is introducing six new data science offerings aimed at customers that are at varying points in their data science journey at the Strata Data Conference happening in NYC today. The six offers are the AI/ML Hack-a-thon, MapR Data Science Refinery, Cybersecurity Advanced Protection, ML Deployment, AI Enablement and ML Rendezvous Orchestration.
According to the company, because AI and ML are complex, organizations cannot always execute AI and ML ideas, and those that do may not be able to easily bring it to production.
The six new offerings are designed to help customers no matter where they are in their AI and ML journey, whether they are just starting or have already invested resources. In addition to increasing in complexity throughout the list, the offerings take more time the more complex they are, with the first one taking one week to prepare and one day to deliver and the final offering taking up to eight weeks.
With the AI/ML Hack-a-thon offering,e the MapR Data Science team will work with an organization to identify a business use case and prototype a solution. According to MapR, this offering is targeted toward those early on in their data science journey and is meant to be a short session that delivers an ML and AI solution that the organization can improve upon over time.
The MapR Data Science Refinery is a one-week session that will guide customers through installation, best practices, and baseline models.
With the Cybersecurity Advanced Protection offering, MapR will help an organization create a real-time pipeline of logs and train models based on the signatures of network sources and traffic. The customer will receive a visual, UI-based assessment that will highlight concerning activity for security experts to review and act on.
ML Deployment maximizes a model’s value by moving the modeling process to the MapR Data Platform, allowing it to leverage all of the organization’s data, use every ML library, and deliver results that will scale as the business does, according to the company.
The AI Enablement offering combines the MapR ML framework and streaming events in order to deploy an AI engine that will search for opportunities to optimize itself through a continuous learning and feedback loop. According to MapR, it “uses machine learning to bring order to the chaotic nature of a system’s behavior.”
Finally, the ML Rendezvous Orchestration is designed for mature ML processes. The offering will enable organizations to monitor ML workflows for events that could impact accuracy, allowing businesses to make informed decisions such as when current models need to be replaced.
In addition, to these offerings, MapR announced the release of a new book, AI and Analytics in Production, written by Ted Dunning, PhD, board member of the Apache Software Foundation, and chief application architect at MapR; and Ellen Friedman, PhD, principal technologist at MapR and Apache committer. According to the company, the book details best practices for maximizing the value of data-driven applications and is intended for a wide audience, including C-level execs, business team leaders, architects, system administrators, data engineers, and data scientists.