Noah Gift is lecturer and consultant at UC Davis Graduate School of Management in the MSBA program. Professionally, Noah has approximately 20 years' experience programming in Python and is a member of the Python Software Foundation. He has worked for a variety of companies in roles ranging from CTO, general manager, consulting CTO, and cloud architect. Currently, he is consulting start-ups and other companies on machine learning and cloud architecture, and is doing CTO-level consulting via Noah Gift Consulting. He has published close to 100 technical publications including two books on subjects ranging from cloud machine learning to DevOps.

At a recent panel on using marketing data to grow your business, Visier CMO Christy Marble noted that every piece of technology we use today should incorporate some degree of machine learning (ML). In enterprise cybersecurity, given the well-documented concerns around skills shortages and tool sprawl, it certainly seems wise to take advantage of any technology that can increase efficiency, make individual employees more effective, and ultimately scale the business. The cloud may be the factor that finally takes ML from being an overhyped pipe dream of a technology to an integral part of every successful security practice. Not all ML is created equal, however, and now that this tech has entered the realm of table stakes, it's time to raise our collective standards and understand exactly what differentiates genuinely powerful, effective ML from systems that are capitalizing on the hype to sell subpar products to under-informed customers. Machine learning systems need a whole lot of data in order to actually work.

We are big fan of cloud-based machine learning and deep learning, and AI in general. After all, you can't be a geek without imagining having a conversation with an artificially intelligent being that can answer questions and carry out your bidding! That's said, we are also seeing cloud-based machine learning and deep learning misapplied over and over again. All have easy fixes for the most part, and certainly cloud-based machine learning is here to stay. But use it wisely and appropriately.

After all, you can't be a geek without imagining having a conversation with an artificially intelligent being that can answer questions and carry out your bidding! That's said, I'm also seeing cloud-based machine learning and deep learning misapplied over and over again. All have easy fixes for the most part, and certainly cloud-based machine learning is here to stay. But use it wisely and appropriately. Here are the top three recurring mistakes that I'm seeing.

Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results--even if you don't have a strong background in math or data science. Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you'll gain a more intuitive understanding of what you can achieve with them and how to maximize their value.

Business data is a capital asset. That's because, in a classical economic framework, data is a factor of production, is not depleted in the process of production, and gains value from human inputs, contributions, and sweat equity. As a capital asset, data's value goes far beyond the sunk cost of its physical instantiation in a database or even the cost of restoring if it were to be lost, stolen, or corrupted. Data's full value resides in the full range of potential business decisions, processes, engagements, and outcomes that it might support. And its value in those derives in great part on the data-driven insights that can be unlocked through analytic tools.

Experienced machine learning developer, Hugh Perkins, author of the popular open source OpenCL libraries DeepCL and cltorch, is an avid user of the Nimbix cloud. Mr. Perkins chose to work with Nimbix in addressing machine learning due to the powerful platform API, industry-leading selection of GPUs, superior-performance and economics. "Nimbix is a breath of fresh air," said Mr. Perkins. "The per-second billing, spin up times of seconds, and the availability of high end GPUs, make Nimbix an awesome choice for machine learning developers." The Nimbix cloud platform is democratized and developer-friendly, allowing users to monetize their trained neural networks in the application marketplace.