Feature store repositories emerge as an MLOps linchpin for advancing AI

A battle for control over machine learning operations (MLOps) is beginning in earnest as organizations embrace feature store repositories to build AI models more efficiently. A feature store is at its core a data warehouse through which developers of AI models can share and reuse the artifacts that make up an AI model as well as an entire AI model that might need to be modified or further extended. In concept, feature store repositories play a similar role as a Git repository does in enabling developers to build applications more efficiently by sharing and reusing code.

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Amazon: We don’t need another AI tool or APl, we need an open AI platform for cloud and edge

After Amazon’s three-week re:Invent conference, companies building AI applications may have the impression that AWS is the only game in town. Amazon announced improvements to SageMaker, its machine learning (ML) workflow service, and to Edge Manager — improving AWS’ ML capabilities on the edge at a time when serving the edge is considered increasingly critical for enterprises.

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Machine Learning: Automated DevOps and threat identification

Machine Learning (ML) and Artificial Intelligence (AI) is a technology that is still finding its footing in the commercial sector. Although few systems are touted as a complete solution, there are many new AI/ML based companies that are capitalising on the benefits, and traditional business will need to follow suit. ML Ops, the ML equivalent of DevOps will become increasingly important.

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Deloitte: MLOps is about to take off in the enterprise

Deloitte Consulting published a report today that suggests a golden age of AI is in the offing, assuming organizations can implement and maintain a consistent approach to machine learning operations (MLOps). Citing market research conducted by AI-focused Cognilytica, the MLOps: Industrialized AI report from Deloitte notes that the market for MLOps platforms is forecast to generate annual revenues in excess of $4 billion by 2025.

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Algorithmia: AI budgets are increasing but deployment challenges remain

A new report from Algorithmia has found that enterprise budgets for AI are rapidly increasing but significant deployment challenges remain. Algorithmia’s 2021 Enterprise Trends in Machine Learning report features the views of 403 business leaders involved with machine learning initiatives. Diego Oppenheimer, CEO of Algorithmia, says: “COVID-19 has caused rapid change which has challenged our…

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How to easily deploy ML Models to production

One of the known truths of the Machine Learning(ML) world is that it takes a lot longer to deploy ML models to production than to develop it.¹ The problem of deploying ML models to production is well known. Let’s discuss some different options you have when it comes to deploying ML models. Variants are provided in order from the most general to ML-specific.

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Technology in the Oil and Gas industry: An MLOps Perspective

The Oil and gas industry generates an annual revenue that was approximately $3.3 trillion in 2019 and is one of the largest enterprises in the world. Oil and natural gas upstream, midstream and downstream processes constantly generate large amounts of data and is immensely dependent on sophisticated technologies to reveal new insights in the business i.e prevent equipment malfunctioning and improve operational efficiency…

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WhyLabs raises $4 million to grow AI and data monitoring platform

WhyLabs is launching out of stealth today with $4 million to grow its platform for data scientists who need help monitoring and troubleshooting problems they encounter with datasets or AI models. The goal is to help teams managing machine learning models save time and catch problems before they make trouble for businesses or customers.

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Key aspects of Machine Learning operations, explained

Machine Learning Operations

Until 2015, even professional programmers didn’t consider machine learning has real potential and benefits. However, with innovation the development of AI and computing capabilities build-up, autonomous MLOps platforms began to develop rapidly and became an integral part of computer systems development.

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