Sign up for our newsletter and get the latest big data news and analysis.

VIANAI Systems Introduces First Human-Centered AI Platform for the Enterprise

Vianai Systems (“Vian”), the human-centered AI platform and products company, launched the Vian H+AITM Platform for reliable, optimized enterprise-wide deployment, management, and governance of machine learning (ML) models at scale. The Vian H+AI Platform brings a human-centric approach, combined with advanced AI techniques to address and remove barriers that currently prevent widespread AI adoption into production environments.

XOps: The Rise of Smarter Tech Operations

In this contributed article, Arvind Prabhakar, CTO of StreamSets, discusses how XOps has become a growing data analytics trend in 2021 for good reason. Evolved from the DevOps movement to better support and enable AI and ML automation workflows, XOps enables organizations to operationalize data and analytics to drive greater business value and establish a solid yet flexible foundation for future technology development.

Case Study: Balancing MLOps Innovation with Tough Security Standards

As AI adoption has grown, so too have concerns about data protection and infrastructure security across the MLOps lifecycle. At GTS Data Processing, a rapidly growing German IT company, security is top of mind as they deliver Infrastructure-as-a-Service and Software-as-a-Service platforms to companies across Europe. GTS’ DSready Cloud offering, powered by Domino® and hosted in Germany, brings together the tools, technologies, compute, and collaboration capabilities its clients need to deliver and manage data science capabilities at scale—all within a GDPR-compliant environment that supports Germany’s stringent security standards.

The Missing Role your Organization Needs for the Success of your AI Initiatives

In this contributed article, Alankrita Priya, AI/ML Product Manager at Hypergiant, discusses how MLOps platforms can operationalize new technologies and fully bridge the gap between data scientists and end business users. There is a crucial role that AI PMs will play moving forward in both facilitating this deployment process and ensuring that companies practice responsible AI.

2022 State of Data Engineering: Emerging Challenges with Data Security & Quality

The 2022 Data Engineering Survey, from our friends over at Immuta, examined the changing landscape of data engineering and operations challenges, tools, and opportunities. The modern data engineering technology market is dynamic, driven by the tectonic shift from on-premise databases and BI tools to modern, cloud-based data platforms built on lakehouse architectures.

2022 State of Data Engineering: Emerging Challenges with Data Security & Quality

The 2022 Data Engineering Survey, from our friends over at Immuta, examined the changing landscape of data engineering and operations challenges, tools, and opportunities. The modern data engineering technology market is dynamic, driven by the tectonic shift from on-premise databases and BI tools to modern, cloud-based data platforms built on lakehouse architectures.

Video Highlights: Lessons From the Field in Building Your MLOps Strategy

Our friends over a Comet produced the video presentation below, hosted by Harpreet Sahota, to help you learn when & how to deploy MLOps from experts who have done it! In discussions with leading organizations utilizing ML like The RealReal and Uber, Comet compiled real-world case studies and organizational best practices for MLOps in the enterprise.

Video Highlights: Minimize Risk and Accelerate MLOps With ML Monitoring and Explainability

In the presentation below, Amit Paka, Chief Product Officer and Co-founder, from our friends over at Fiddler AI, spoke at the Machine Learning in Finance Summit discussing the importance of monitoring and explainable AI (XAI).

MLOps: Bringing AI to the Tactical Edge—and Making It Work

In this contributed article, Joel Dillon and Eric Syphard of Booz Allen, feel strongly that in order for machine learning to have a profound impact on data sharing for defense and the intelligence community, it’s imperative that data get communicated to warfighters at the tactical edge, where fast decisions are at a premium and compute power and connectivity are often scarce. It is critical that these edge use cases characterize and shape planning for AI and ML-driven investment as digitization continues to accelerate the pace of war.

2021 MLOps Platforms Vendor Analysis Report

The Neuromation team has just published a new report on the state of Machine Learning Operations Platforms in 2021. MLOps was defined as a separate discipline only recently when the ML practitioners moved from university labs to corporate boardrooms. AI and ML leaders today already have a better understanding of the MLOps lifecycle and the procedures and technology required for deploying new models into production and subsequently scaling them.