You got through all the hurdles getting the data you need; you worked hard training that model, and you are confident it will work. You just need to run it with a more extensive data set, more memory and maybe GPUs. And then...well. Running your code at scale and in an environment other than yours can be a nightmare. You have probably experienced this or read about it in the ML community. How frustrating is that? All your hard work and nothing to show for it.

By Jean-Baptiste Thomas, Pure Storage & Yaron Haviv, Co-Founder & CTO of Iguazio You trained and built models using interactive tools over data samples, and are now working on building an application around them to bring tangible value to the business. However, a year later, you find that you have spent an endless amount time and resources, but your application is still not fully operational, or isn’t performing as well as it did in the lab. Don’t worry, you are not alone.

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).

It’s no secret that global mobility ecosystems are changing rapidly. Like so many other industries, automakers are experiencing massive technology-driven shifts. The automobile itself drove radical societal changes in the 20th century, and current technological shifts are again quickly restructuring the way we think about transportation. The rapid progress in AI/ML has propelled the emergence of new mobility application scenarios that were unthinkable just a few years ago.

Across organizations large and small, ML teams are still faced with data silos that slow down or halt innovation. Read on to learn about how enterprises are tackling these challenges, by integrating with any data types to create a single end-to-end pipeline and rapidly run AI/ML with Azure Synapse Analytics with Iguazio.

At Iguazio, we’ve spoken and written at length about the challenges of bringing data science to production. The complexity of operationalizing ML can generate huge costs in terms of work hours and compute resources, especially as successful projects get scaled up and expanded. We’re proud to share that the Iguazio Data Science Platform has been named a fast moving leader in the GigaOm Radar for MLOps report.

We’re delighted to announce the release of the Iguazio Data Science Platform version 3.0. Data Engineers and Data Scientists can now deploy their data pipelines and models to production faster than ever with features that break down silos between Data Scientists, Data Engineers and ML Engineers and give you more deployment options . The development experience has been improved, offering better visibility of the artifacts and greater freedom of choice to develop with your IDE of choice.