Enabling Broader AI & ML Analytics Adoption: Data Platforms, Tools, & Practices

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Presented by

Lalit Ahuja, Chief Customer and Product Officer, GridGain

About this talk

Enabling the broader adoption of AI and ML analytics use cases is one of the top drivers behind the modernization of data infrastructure at companies right now. From gathering, transforming, and processing data to the development, testing, and monitoring of data pipelines and models, deriving greater business value from AI and ML initiatives requires the right mix of platforms, tools, and practices. To be successful, companies need a modular data architecture that can quickly accommodate new use cases. They also need the ability to rapidly integrate data, well-defined data governance and security processes, and standardized processes for building data pipelines. The use of automation, MLOps and AIOps methodologies, and cloud services that offer on-demand scalability are all key pillars for success as well.
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GridGain is a unified real-time data platform that enables a simplified and optimized data architecture for enterprises that require extreme speed, massive scale, and high availability from their data ecosystem. GridGain’s distributed memory-first architecture and colocated compute deliver data processing and analytics at millisecond latencies, with configurable disk-based persistence for added durability. Horizontally scalable clusters can be deployed both on-premises and natively in public or private clouds, empowering companies to handle even the most demanding workloads in multi, hybrid, and inter-cloud environments. GridGain is trusted by companies like Citi, Barclays, American Airlines, AutoZone, and UPS to accelerate their existing applications, speed operational analytics and fraud detection, train machine learning models for AI, and provide fast-access data hubs. To learn more, please visit www.gridgain.com.