Machine learning can provide companies with a competitive advantage by using the data they're collecting -- for example, purchasing patterns -- to generate predictions that power revenue-generating products (e.g. But it's difficult for any one employee to keep up with -- much less manage -- the massive volumes of data being created. That poses a problem, given AI systems tend to deliver superior predictions when they're provided up-to-the-minute data. Systems that aren't regularly retrained on new data run the risk of becoming "stale" and less accurate over time. Fortunately, an emerging set of practices dubbed "MLOps" promises to simplify the process of feeding data to systems by abstracting away the complexities.

The term "real time" has been infused throughout tech, from real-time stock picks to real-time pizza tracking. As everyday enterprises begin incorporating data tools and tactics used inside the biggest of big tech companies, a sector of data services providers has emerged to help them take advantage of the truly real-time analytics and machine learning approaches only giant companies with far larger database teams and resources could have afforded in the past. Companies like Hazelcast, Rockset, Tecton and others enable split-second analytics and machine learning for things like financial fraud prevention, dynamic pricing or product recommendations that respond to what you just clicked. These companies promise to leave plodding batch-data processing for old-school business intelligence analysis in the dust. But whether every enterprise needs, wants or is ready to operate at a clip as fast paced as a Citibank, Uber or Amazon remains to be seen. Updating data every few days, every night or even every hour or so for business analysis using a typical batch processing approach "is like playing Monday morning quarterback," said Venkat Venkataramani, CEO and co-founder of Rockset, a company that provides a database for building applications for real-time data, analytics and queries.

"Feature stores," with their dreary and opaque moniker, might not sound like the sexiest subject. That's why they're attracting an increasing amount of attention and investment from venture firms, which see the market opportunity growing into the distant future. AI systems are made up of many components, one of which is features. Features are the individual variables that act like inputs in the system. In thinking about features, it can be helpful to visualize a table, where the data used by AI systems is organized into rows of examples (data from which the system learns to make predictions) and columns of attributes (data describing those examples).

Three former Uber engineers, who helped build the company's Michelangelo machine learning platform, left the company last year to form Tecton.ai and build an operational machine learning platform for everyone else. Today the company announced a $20 million Series A from a couple of high-profile investors. Today's investment combined with the seed they used to spend the last year building the product comes to $25 million. But when you have the pedigree of these three founders -- CEO Mike Del Balso, CTO Kevin Stumpf and VP of Engineering Jeremy Hermann all helped build the Uber system -- investors will spend some money, especially when you are trying to solve a difficult problem around machine learning. The Michelangelo system was the machine learning platform at Uber that looked at things like driver safety, estimated arrival time and fraud detection, among other things.