Feature store applications are fairly new product technology domain that allows for the development, maintaining, and monitoring of data features used by machine learning algorithms in artificial intelligence systems around us. Basically, a feature store is a data management layer used for saving and repurposing data features specifically designed for machine learning use cases. It is a measurable data property of an entity or representation of a object (e.g. The cores system capabilities for a feature store comprises of the abilities to support feature engineering (feature creation), a storage layer for both online and offline feature storage, a serving layer (via API, SDK), with a registry that features can be discovered with historical lineage that is trackable and lastly monitoring (and alerting) of features being used in understanding data drift with anomalies detection. The benefits are ten fold in having a feature store.

An AI-focused neural network software engineer walks into a data shop says hello to the shopkeeper. "I'll have two data preparation functions, one testing and debugging toolset, a couple of application log tracking systems and a bag of potatoes," asks the engineer. Okay it's not a great joke, there's no punchline and the potatoes part is definitely just a ruse, but the way we might build the Artificial Intelligence (AI) functions of tomorrow has a kind of composabe, package-able feel. If it's not quite off-the-shelf AI, then its composable AI that brings together some of the core functions that smart systems use regularly. It's still down to our neural network engineer to know the recipe and peel the spuds, but we can start to shop for many of the individual components needed now.

Iguazio, the data science platform built for production and real-time machine learning applications, announced it has been deployed by mobile software company PadSquad, to improve the relevance and performance of the digital campaigns they run for their customers worldwide. PadSquad is revolutionizing traditional media with interactive features and innovative technologies that transform the audiences' experience and engagement with ad creatives. Iguazio was deployed by PadSquad to use AI to improve ad performance and reduce media costs for their customers. They do this by ingesting and acting upon real-time events – from contextual content on the page, engagement with creative elements like video views, swipeable panels, and hot spots, to the season and time of day – at a rate of over 3,000 events per second. Utilizing online and offline behavioral data from multiple sources, available to them through third-party platforms and their own internal tools, Padsquad can now harness machine learning to optimize ad performance and provide a better and more personalized user experience for their customers' audiences.

In the era of the Internet, the ability to crunch large amounts of data and process it with speed and efficiency has become essential for businesses to survive. But you try sitting down and going through all that data by hand: you'll get done sometime in the year 2050 if you're lucky. That's where machine learning comes to the rescue. But sorting through a sea of providers and companies is an exhausting process. What can you do to narrow it down?

Iguazio, the data science platform for real time machine learning applications, announced that it has raised $24M of funding. The round was led by INCapital Ventures, with participation from existing and new investors, including Pitango, Verizon Ventures, Magma Venture Partners, Samsung SDS, Kensington Capital Partners, Plaza Ventures and Silverton Capital Ventures. The funds will be used by Iguazio to accelerate its growth and expand the reach of its data science platform to new global markets. The demand for AI applications is on the rise. According to Gartner, AI augmentation alone will create $2.9 trillion of business value in 2021.

The MLOps Conference took place earlier this week at Hudson Mercantile in New York City. Experts from the New York Times, Twitter, Netflix and Iguazio, the host company, spoke about best practices and machine learning implementation throughout a variety of different organizations. I learned of the technological void that exists when data scientists want to implement machine learning. With this new context in mind, I can approach conversations with our data team from a new perspective, and take the time to understand how we can implement new models on our team. Machine learning as a technology has been around for more than 50 years, beginning with Arthur Samuel's pioneering work at IBM where his program helped the computer improve with each game of checkers it played in 1952.

When I started my first business in the mid-90's I did what most first-time entrepreneurs do -- I ordered business cards. Actually, I first had to get an address and order a phone. Then it was setting up an accounting system, doing the legal paperwork, building a website, and, of course, writing a really long business plan. I did everything except the things I should have been doing: telling my story and selling my solution. But as is so often the case, I got too caught up in the mechanics and lost sight of my purpose.