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.
Balancing MLOps Innovation with Tough Security Standards
January 13, 2022 by Leave a Comment
The Missing Role your Organization Needs for the Success of your AI Initiatives
January 5, 2022 by 1 Comment
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
November 18, 2021 by Leave a Comment
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
November 15, 2021 by Leave a Comment
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
October 31, 2021 by Leave a Comment
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
August 8, 2021 by Leave a Comment
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
July 27, 2021 by Leave a Comment
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.
Video Highlights: Challenges of Operationalizing ML
May 31, 2021 by Leave a Comment
In the panel discussion below, the focus is on the main challenges of building and deploying ML applications. The discussion includes common pitfalls, development best practices, and the latest trends in tooling to effectively operationalize. The presentation comes from apply(): The ML Data Engineering Conference sponsored by Tecton.
Domino Data Lab Debuts New Solutions with NVIDIA to Enhance the Productivity of Data Scientists
April 14, 2021 by Leave a Comment
Domino Data Lab, provider of a leading Enterprise MLOps platform trusted by over 20% of the Fortune 100, announced a series of new integrated solutions and product enhancements with NVIDIA, some of which are available now and others will be made available in the coming months. These new enhancements allow data scientists and data engineers the ability to deploy the industry’s most powerful and innovative solution to enhance productivity and positively impact business outcomes.
The Future Starts Now – Achieving Successful Operation of ML & AI-Driven Applications
June 18, 2020 by Leave a Comment
Operationalizing AI and ML has become an unavoidable need in business, as various industries heavily rely on large volumes of real-time data as input to automated decision-making processes to yield the best results. Use cases in the data science field have shown that ML models and AI have few tangible business benefits until they are operationalized. In this e-book, our friends over at MemSQL show us how to successfully deploy model-driven applications into production.
