MapR introduces 6 new data science offerings to help organizations with their AI journey
MapR announced six new data science service offerings to help customers gain immediate value from Machine Learning (ML) and Artificial Intelligence (AI) and expand their competitive edge over competitors, no matter where the customer is in their data science journey.
Because AI and ML can be complex, organizations don’t always have the capacity to execute on AI and ML ideas.Those that do, may not be able to bring those ideas to production.
According to McKinsey Global Institute, early adopters of ML have a 3 to 15% profit advantage across sectors. Many say they achieve revenue increases by using AI in core processes.
Some organizations may lack the internal knowledge and expertise, but this should not hold them back entirely.
“The MapR Data Science team has helped dozens of organizations get to the next stage in their AI journey,” said Joe Blue, director data science, MapR Technologies.
“We’ve learned that there is no one-size fits all approach that will work for every organization. When we engage with organizations, we learn where they are in their AI maturity level and what their objectives are to come up with a customized plan to get them to the next level.”
In this offering, the MapR Data Science team works with the organization to identify a business use case and prototype a solution. This offering is targeted toward AI and ML contemplators, and is meant to be a short, hands-on session that delivers a real ML and AI solution that the organization will continue to improve and maintain over time.
Data Science Refinery Accelerator
MapR’s Data Science Refinery unlocks container and ML technologies and, with this engagement, an expert will guide customers through installation, practices, and baseline models to ensure maximum production success. For a limited time, a one-week engagement is included when purchasing the MapR Data Platform, a $15k value.
Cybersecurity Advanced Protection
Network security vendors are effective at spotting known patterns, but there are often “unknown” gaps that could allow evolving hackers to gain entry or inflict damage. The MapR cybersecurity data science offering orchestrates a real-time pipeline of logs (e.g. application logs, transaction logs, etc.) and trains models based on the signature of network sources and traffic. Ultimately, the organization receives a visual, UI-based assessment showing suspicious activity, allowing internal security experts to review and escalate threats in real-time.
Building ML solutions to solve business problems doesn’t actually address that problem until the model can make real decisions. The issue is that many environments have limitations in using data and scaling decisions. Intended for organizations that are further along in their ML / AI journey, the model deployment offering maximizes a model’s value by uploading the modeling process to the MapR Data Platform. The solution is then poised to take advantage of all the organization’s data, utilize every ML library, and deliver results that will scale and improve with the business.
With this offering, the data science team combines the MapR ML framework with Streaming events to deploy an AI engine that will begin to find new opportunities for optimization through a continuous learning and feedback loop. The team uses Machine Learning to bring order to the chaotic nature of a system’s behavior (e.g., a person, a car, a pipeline, etc.), then apply reinforcement learning to teach the system to adapt to identify and assess unusual cases to achieve generalization.
ML Model Maintenance
ML models degrade over time. In many cases, the arrival of performance results lags behind the next model deployment. Designed for mature ML processes, this offering enables organizations to monitor their ML workflows for events that might impact their accuracy in lieu of performance data. Having detected those impacts, the business will be able to make more informed decisions, such as when the current model should be replaced.
“Production success with AI and ML depends on deploying and then evaluating models over time,” explained Ted Dunning, chief applications architect, MapR Technologies.
“The ML Model Maintenance offering streams diagnostics and makes it easier to offers better model evaluation, and improves the ability to respond, increasing the agility and effectiveness of an organization’s ML and AI deployment.”