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The open source ML tooling ecosystem has become vast in the last few years, with many tools both overlapping in their capabilities as well as complimenting each other nicely. In part because AI/ML is a still-immature practice, the messaging around what all these tools can accomplish can be quite vague. In this article, we’ll dive into three tools to better understand their capabilities, and how they fit into the ML lifecycle.

2023 is looking likely to be a breakout year for artificial intelligence (AI) and machine learning (ML). Some industry-watchers predict that recent breakthroughs in AI might lead to a new revolution in society akin to the industrial revolution, the invention of the internet, or the advent of the smartphone. Yet, 2023 doesn’t mark the invention of AI—just the year it went viral thanks to OpenAI’s ChatGPT technology.

10 years ago, it would be ridiculous for people to believe that someday they would be able to use their faces to unlock their phones. That’s because it had been extremely difficult to create cartoon characters without profound drawing skills – but now we can easily turn photos into cartoon characters. Struggling with parallel parking? No worries, because self-parking systems are becoming standard equipment in vehicles.

Seagate is the world’s leading data storage solution. Together with Iguazio, Seagate is able to manage data engineering at scale while harnessing petabytes of data, efficiently utilize resources, bridge the gap between data engineering and data science and create one production-ready environment with enterprise capabilities. In this new webinar, Vamsi Paladugu, Sr.

Deep learning has become a buzzword in the field of artificial intelligence (AI) in recent years. It has achieved impressive results in a variety of tasks, including image and speech recognition, natural language processing, and even playing games.