It is intended to be used by researchers in the field of artificial intelligence, but is not limited to those. The empirical analysis of algorithms is often accompanied by the execution of algorithms for different inputs and variants of the algorithms, specified via parameters, and the measurement of non-functional properties. Since the individual evaluations are usually independent, the evaluation can be performed in a distributed manner on an HPC system. However, setting up, documenting, and evaluating the results of such a study is often file-based. Usually, this requires extensive manual work to create configuration files for the inputs or to read and aggregate measured results from a report file.

Sarcasm can be difficult to detect in text, especially for machines. However, with the power of large language models, it's possible to create a tool that can identify sarcastic comments with high accuracy. That's exactly what the ClearML team did with their latest project: a sarcasm detector that combines various ClearML tools to showcase the capabilities of MLOps. In the age of chatGPT and proprietary APIs, this project is meant as an example of how to create tools based on large language models that can run on your own machine, so you have full control over it. And thanks to ClearML being open source, even the whole MLOps stack can run locally.

A first look at ClearML's new report, "MLOps in 2023," also finds that nearly one-third (29%) of respondents say a'lack of talent' is a key challenge in operationalizing ML at scale. Note: TDWI's editors carefully choose press releases related to the data and analytics industry. We have edited and/or condensed this release to highlight key information but make no claims as to its accuracy. ClearML, provider of a unified, end-to-end MLOps platform, announced initial findings from its in-depth research report, MLOps in 2023: What Does the Future Hold? Polling 200 U.S.-based machine learning decision makers, the report examines key trends, opportunities, and challenges in machine learning and MLOps (machine learning operations).

Widely lauded as kingmakers and heroes of the new digital revolution, software developers are in short supply, high demand and are globally recognised as being key to the new fabric of computing we are building across the web and the cloud. But throughout their recent reign in tech, things have broadened; the operations function that works to underpin, manage and facilitate developer needs has been championed and brought more closely into line with software programmers' workflow processes. What has happened here has a special name – and of course we're talking about DevOps. This is the portmanteau pairing of Dev (developers) and Ops (operations) in a new approach to workplace culture designed to enable these traditionally unwelcome bedfellows to live together better. Developers want speed, functionality and choice of software tools.

Artificial intelligence (AI) technologies are quickly transforming almost every sphere of our lives. From how we communicate to the means we use for transportation; we seem to be getting increasingly addicted to them. Because of these rapid advancements, massive amounts of talent and resources are dedicated to accelerating the growth of the technologies. Here are the top 10 open-source AI technologies powering ML projects in 2022. TensorFlow is an open-source machine learning framework that is easy to use and deploy across a variety of platforms.