Over the last ten years, SaaS has changed the way we work -- for the better. SaaS is helping tech companies move so much faster, but it's also introducing a new surface area of risk they've never seen before. As a result, companies are faced with answering the difficult question of how to manage compounding security risks as they grow without introducing more red tape that slows their team members down. At Metomic, we help tech companies save time & reduce costs by minimising their sensitive data footprint in cloud applications like Slack, Google Drive and Zendesk. Our technology includes native integrations with dozens of cloud apps, AI to detect thousands of sensitive data types, and advanced functionality to manipulate data at scale (redaction, retention, access controls, etc).

We would all like to think we're impeccable drivers, but the reality is most of us are far from perfect on the road. Even if self-driving car tech improves and your vehicle is equipped with the best version, chances are you'll eventually end up in an accident. You may even be at fault. Though accidents can't be completely prevented, the enormous property damage bills that come along with car accidents can be partially minimized with the right insurance. Auto insurance is required in most states, yet this incredibly important protection is often overly complicated to understand and annoying to secure.

Machine learning models typically come in two flavors: those used for batch predictions and those used to make real-time predictions in a production application. These are known as offline and online models, respectively. Offline models, which require little engineering overhead, are helpful in visualizing, planning, and forecasting toward business decisions. On the other hand, online models require substantial engineering effort and are used to personalize a customer's experience via recommendations. Understanding which model to use based on project needs is critical because it not only dictates the deployment process, but also influences how the model is trained.

While Elon Musk-esque theories of an impending AI apocalypse tend to dominate popular conversations around machine learning, the reality is that the machine learning revolution has already happened, cyborg-free. To see it, you just have to look at data-heavy, sometimes "boring" industries like insurance. That's where the unparalleled processing ability of machine learning can have an outsized impact. On October 16, Quantemplate--a London-based startup that uses machine learning to help insurers process data--raised a $12 million Series B. This round comes on the heels of several others at insurance-focused machine learning startups: Ethos, a data-driven life insurance issuer, raised a $60 million Series C in late August. Clearcover, a platform that uses AI to sell auto insurance, raised $43 million in January.

Despite the promise artificial intelligence holds to transform many facets of American life, the majority of artificial intelligence companies are still concentrated in traditional tech hubs. According to a Glassdoor analysis from November, 30 percent of open jobs at the time that included the words "artificial intelligence, "AI" or "deep learning" in the job title were located in San Jose, with another 18 percent in San Francisco. That's why VentureBeat decided to take a look at what kinds of AI startups are forming in an area that venture capitalists have traditionally overlooked -- the Midwest -- and what problems they're trying to tackle. Using data from research firms CB Insights and Crunchbase, VentureBeat looked for Midwestern startups with unique machine learning or artificial intelligence platforms that have raised significant amounts of venture capital. We also spoke with two Midwestern venture capitalists about which startups are worth watching.