Bottom Line: Verta helps enterprises track the thousands of machine learning models they're creating using an integrated platform that also accelerates deploying models into production, ensuring that models' results are based on the most current data available. The same is true for all data-intensive businesses today. Despite ramping up their data science teams and investing in the latest machine learning tools, many struggle to keep models organized and move them out of development and into production. Verta is a startup dedicated to solving the complex problems of managing machine learning model versions and providing a platform where they can be launched into production. Founded by Dr. Manasi Vartak, Ph.D., a graduate of MIT, who led a team of graduate and undergraduate students at MIT CSAIL to build ModelDB, Verta is based on their work to define the first open-source system for managing machine learning models.

Are you considering using machine learning? First, you have to think about the problem you are trying to solve. Speaking at the recent Code PaLOUsa conference in Louisville, Brian Korzynski, a senior application developer at United Shore, explained why it's essential that businesses understand the pros and cons of machine learning. Brian Korzynski, senior application developer at United Shore, explains why it's essential that businesses understand the pros and cons of machine learning.

In a matter of months, machine learning shifted from cutting-edge science to a tech-industry buzzword. Specifically in advertising and marketing circles, it's gaining a reputation as a magic bullet; however, machine learning is simply a technique to use computational power to solve specified and difficult problems. Across the board, marketers have to focus on identifying their campaign goals and then find the right tool to reach them. Machine learning can be a powerful tool for those capable of implementing it correctly. The reality is, very few marketers actually use machine learning, and for most situations it's like using a bazooka to swat a fly--while it can solve an array of tasks, sometimes it's overkill.

Questions your company should be asking before implementing machine learning. Machine learning (ML) is all the craze right now. You hear about Elon Musk and Mark Zuckerberg debate the future of artificial intelligence and machine learning, but you wonder, how is machine learning going to actually help my business? In this article, we briefly explain what ML is and then dive into the ML-related questions your company should be asking. Machine learning is revolutionary because it gives computers the ability to solve problems without being explicitly programmed.

Artificial intelligence (AI) and its little brother machine learning (ML) are receiving tremendous hype -- and deservedly so. From smart cars to computer-assisted medical diagnoses, machine learning is an incredibly powerful technology that has only scratched the surface of the impact it will eventually have on the world. One obvious use case is for online search, where Google continually uses ML to refine results based on user behavior. For large corporations, cognitive search capabilities can provide employees with valuable insights from massive amounts of structured and unstructured data. Machine learning can play a key role here as well, but it's not appropriate in every situation.

Most businesses recognize that machine learning can generate exceptional value but many still wonder how, in what specific areas, and if the time is right to integrate it within their data strategy. Today, we explore what questions you should be asking to know if machine learning is right for your business. Advancements in computational power: increased performance in microelectronics are enabling businesses to process more data. With more and more of them switching out their CPUs for GPUs, time-consuming processes (like neural net training) have seen a significant increase in speed. Market leaders like Nvidia, Google, and Microsoft are even working on hardware specifically used for machine learning.