If you've ever wanted to discover more about Kaggle, the online community for machine learning students and data scientist practitioners then look no further than our expert-led guide to get an overview on all the basics you need to know about this amazing opportunity provider and competition organiser.

Real-time feature engineering is valuable for a variety of use cases, from service personalization to trade optimization to operational efficiency. It can also be helpful for risk mitigation through fraud prediction, by enabling data scientists and ML engineers to harness real-time data, perform complex calculations in real time and make fast decisions based on fresh data, for example to predict credit card fraud before it occurs.

With the explosion of the machine learning tooling space, the barrier to entry has never been lower for companies looking to invest in AI initiatives. But enterprise AI in production is still immature. How are companies getting to production and scaling up with machine learning in 2021? Implementing data science at scale used to be an endeavor reserved for the tech giants with their armies of developers and deep pockets.

CPG executives invest billions of dollars in trade and consumer promotion investments every year, spending as much as 15-20% of their total annual revenues on these initiatives. However, studies show that less than 72% of these promotions don’t break even and 59% of them fail. Despite these troubling statistics, most CPG organizations continue to design and execute essentially the same promotions year after year with negligible hope of obtaining sustained ROI.

We are proud to announce that Iguazio has been named a sample vendor in five 2021 Gartner Hype Cycles, including the Hype Cycle for Data Science and Machine Learning, the Hype Cycle for Artificial intelligence, Analytics and Business Intelligence, Infrastructure Strategies and Hybrid Infrastructure Services, alongside industry leaders such as Google, IBM and Microsoft (who are also close partners of ours).