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How to Bring Breakthrough Performance and Productivity To AI/ML Projects

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.

Your Parents Still Don't Know What a Hashtag Is. Let's Teach Them the Basics of Machine Learning and Streaming Data

Quite often, the digital natives of the family — you — have to explain to the analog fans of the family what PDFs are, how to use a hashtag, a phone camera, or a remote. Imagine if you had to explain what machine learning is and how to use it. There’s no need to panic. Cloudera produced a series of ebooks — Production Machine Learning For Dummies, Apache NiFi For Dummies, and Apache Flink For Dummies (coming soon) — to help simplify even the most complex tech topics.

Building Machine Learning Pipelines with Real-Time Feature Engineering

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.

Implementing Automation and an MLOps Framework for Enterprise-scale ML

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.

Using Automated Model Management for CPG Trade Success

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.

All That Hype: Iguazio Listed in 5 Gartner Hype Cycles for 2021

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).

Operationalizing Machine Learning for the Automotive Future

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.

What's new with BigQuery ML: Unsupervised anomaly detection for time series and non-time series data

When it comes to anomaly detection, one of the key challenges that many organizations face is that it can be difficult to know how to define what an anomaly is. How do you define and anticipate unusual network intrusions, manufacturing defects, or insurance fraud? If you have labeled data with known anomalies, then you can choose from a variety of supervised machine learning model types that are already supported in BigQuery ML.