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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.

Video Highlights: Challenges of Operationalizing ML

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

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

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.

The Future Starts Now – Achieving Successful Operation of ML & AI-Driven Applications

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.