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Model Risk Management in the Age of AI

In this contributed article, Stu Bailey, Co-Founder and Chief AI Architect of ModelOp, discusses how financial services companies can easily validate multiple AI/ML models and reduce ML project costs by 30% through automation. ModelOps refers to the process of enabling data scientists, data engineers, and IT operations teams to collaborate and scale models across an organization. This drives business value by getting models into production faster and with greater visibility, accountability and control.

Transform Raw Data to Real Time Actionable Intelligence Using High Performance Computing at the Edge

In this special guest feature, Tim Miller from One Stop Systems discusses the importance of transforming raw data to real time actionable intelligence using HPC at the edge. The imperative now is to move processing closer to where the data is being sourced, and apply high performance computing edge technologies so real time insights can drive business actions.

AIOps – 6 Things to Avoid When Selecting a Solution

In this special guest feature, Paul Scully, a Vice President at Grok, believes that sometimes it’s easier to look at what NOT to do in order to find an AIOps solution that will work for your company. Read on to learn more about what to avoid when it comes to finding an AIOps platform that will benefit your company.

Expansion of the Edge: The Preeminent Importance of Edge Computing Today

In this contributed article, editorial consultant Jelani Harper discusses how the reliance on edge components and edge processing is becoming more critical to the decentralized big data landscape, especially with the ongoing need to communicate remotely. It’s imperative to ensure edge networks can be remotely provisioned, secured, and available for fringe processing where applicable to continue to support what’s become a burgeoning need for the IoT in general.

Rubber Meets the Road: Reality of AI in Infrastructure Monitoring

In this special guest feature, Farhan Abrol, Head of Machine Learning Products at Pure Storage, examines the disparity between the hype and what’s been delivered, and where we’ll see the most impactful advancements in efficiency and capacity in the coming year. The hype around artificial intelligence and machine learning’s potential to improve IT infrastructure continues to grow, as does enterprise investment in intelligent infrastructure management, however the anticipated value has yet to be realized.

Reality Bites: 3 Misconceptions that Can Lead to Microservice Mayhem

In this contributed article, Eric D. Schabell, Global Technology Evangelist and Portfolio Architect Director at Red Hat, discusses how microservices are core to organizations’ flexibility and agility in the digital world. But that doesn’t mean that microservices are right for every use case or even for every organization—at least, not right now.

Businesses Building AI Applications Are Shifting to Open Infrastructure

In this special guest feature, Ami Badani, CMO of Cumulus Networks, suggests that as AI requires a lot of data to train algorithms in addition to immense compute power and storage to process larger workloads when running these applications, IT leaders are fed up with forced, expensive and inefficient infrastructure, and as a result they are turning to open infrastructure to enable this adoption, ultimately transforming their data centers.

Dotscience Enables Simplest Method for Building, Deploying and Monitoring ML Models in Production on Kubernetes Clusters to Accelerate the Delivery of Business Value from AI

Dotscience, a pioneer in DevOps for Machine Learning (MLOps), announced new platform advancements that offer the easiest way to deploy and monitor ML models on Kubernetes clusters, making Kubernetes simple and accessible to data scientists.

Interview: Haoyuan Li, Founder and CTO of Alluxio

I recently caught up with Haoyuan Li, Founder and CTO of Alluxio to examine the next critical piece in the data evolution: Data Orchestration – the missing piece in building hybrid and multi-cloud analytical architectures. Haoyuan Li received his Ph.D. from UC Berkeley AMPLab, in Computer Science. At the AMPLab, he created Alluxio (formerly Tachyon) Open Source Data Orchestration System, co-created Apache Spark Streaming, and became an Apache Spark founding committer.

Benefits to Consider from a Multi-Cloud Approach

In this contributed article, Bill Fenick, Vice President, Enterprise for Interxion, believes that the cloud is undoubtedly the future within business – and we’re already seeing it evolve. Businesses that adapt to the evolving cloud and adopt a multi-cloud strategy will gain an edge over their competition, while those that don’t will find themselves left behind.