Powering MLOps Initiatives helps Enterprises Gain Actionable Insights from their Data and Turn it into Business Value
NEW YORK, Dec. 11, 2019 /PRNewswire/ -- GigaSpaces, the provider of InsightEdge, the fastest in-memory real-time analytics processing platform, announced today the availability of GigaSpaces Version 15.0, including the InsightEdge Platform and XAP, to operationalize and optimize machine learning with the required speed, scale, accuracy and management tools. GigaSpaces Version 15.0 powers machine learning operations (MLOps) initiatives, helping enterprises maximize the business value derived from big data.
Deploying machine learning models in production remains a major challenge for many enterprises. The Gartner Accelerate Your Machine Learning and Artificial Intelligence Journey Using These DevOps Best Practices says that, "According to the 2019 Gartner CIO Survey, AI and ML continue to be viewed as the No. 1 game-changing technology by CIOs. However, most organizations underestimate how long it will take to move AI and ML projects into production." 
GigaSpaces Version 15.0 simplifies integrating AI workloads with the organization's core infrastructure, accelerating machine learning deployment and enabling enterprises to more readily experience the business benefits of machine learning models.
GigaSpaces Version 15.0 introduces a new enterprise-grade monitoring and administration tool, Ops Manager, that provides visibility into the components of systems running models including logs, inputs, outputs and exceptions, using different performance visualization techniques. The Ops Manager enables continuous monitoring of machine learning pipelines, starting at the cluster level and drilling through to individual services so users can maintain accurate data models and ensure that problems are resolved before they affect overall performance.
The new AnalyticsXtreme Batch Indexing included in GigaSpaces InsightEdge Version 15.0 optimizes and automates data access and storage with the added ability to move data between the more frequent (cold data) access and infrequent (archive data) access tiers on data lakes and data warehouses. The performance of ML models is enhanced since frequently accessed cold data can be retrieved 80X faster directly from data lakes and processing costs are reduced as data access patterns change.
GigaSpaces Version 15.0 also provides a native smart space client in Kubernetes that supports remote CRUD operations, task execution, and event-driven analytics providing high throughput and fast serialization, as well as automatic load balancing. Writing and updating of data without a predefined schema allows easy changes to the data model, while ensuring compatibility with JDBC and BI tools so that code can be integrated more reliably and faster with lower administrative overhead.
"Machine learning is becoming an essential component of mission critical applications to optimize operations and deliver superior real time customer experiences," said Yoav Einav, VP Product at GigaSpaces. "GigaSpaces Version 15.0 provides enterprises with the machine learning model management capabilities, speed and scale that they need to accelerate their machine learning and artificial intelligence journey."
For a more in depth description of GigaSpaces Version 15.0 visit the company's blog.
GigaSpaces provides the fastest in-memory computing platforms for real-time insight to action and extreme transactional processing. With GigaSpaces, enterprises can operationalize machine learning and transactional processing to gain real-time insights on their data and act upon them in the moment. The always-on platforms for mission-critical applications across cloud, on-premise or hybrid, are leveraged by hundreds of Tier-1 and Fortune-listed organizations worldwide across financial services, retail, transportation, telecom, healthcare, and more. GigaSpaces offices are located in the US, Europe and Asia. More at www.gigaspaces.com and www.gigaspaces.com/blog/
1 Gartner "Accelerate Your Machine Learning and Artificial Intelligence Journey Using These DevOps Best Practices," Arun Chandrasekaran and Farhan Choudhary, 12 November 2019