IBM reveals that nearly half of the challenges related to AI adoption focus on data complexity (24%) and difficulty integrating and scaling projects (24%). While it may be expedient for marketers to "slap a GPT suffix on it and call it AI," businesses striving to truly implement and incorporate AI and ML face a two-headed challenge: first, it's difficult and expensive, and second, because it's difficult and expensive, it's hard to come by the "sandboxes" that are necessary to enable experimentation and prove "green shoots" of value that would warrant further investment. In short, AI and ML are inaccessible. History shows that most business shifts at first seem difficult and expensive. However, spending time and resources on these efforts has paid off for the innovators.

Over the last 50 years, machine learning (ML) has evolved through a series of hype cycles -- periods of public fervor as well as funding droughts known as "AI winters" -- to reach mainstream applicability and acceptance. With recent computing advances, we now see machine learning being widely used for things like search and feed ranking, spam filtering, and warnings about suspicious credit card activity. A specific form of ML called Deep Learning has fueled the recent growth in Natural Language Processing (NLP), autonomous driving, image and object recognition, and virtual personal assistants. Now, machine learning has evolved to the point where it won't just be integrated into new products but will also transform how products are built. Already today, ML offers enough benefits for product development that most companies should consider incorporating it into their processes. But when does it make sense to invest in machine learning capabilities and how do you actually build a machine learning team?

This growth is driven not only by "middle adopters" recognizing the vast potential of machine learning after watching early adopters benefit from its use but by steady improvements in machine-learning technology itself. It may be too early to say with certainty that machine learning develops according to a predictable framework like Moore's Law, the famous precept about computing power that has borne out for nearly 50 years and only recently began to show signs of strain. But the industry is clearly on a fast track. Since machine-learning algorithms grow wealthier and more businesses come about the notion of incorporating this powerful technology in their processes, it is time for your business idea of placing machine learning how to operate, also. To begin with, think about the advantages and costs.

Demand for machine learning is skyrocketing. This growth is driven not only by "middle adopters" recognizing the vast potential of machine learning after watching early adopters benefit from its use, but by steady improvements in machine-learning technology itself. It may be too early to say with certainty that machine learning develops according to a predictable framework like Moore's Law, the famous precept about computing power that has borne out for nearly 50 years and only recently began to show signs of strain. But the industry is clearly on a fast track. As machine-learning algorithms grow smarter and more organizations come around to the idea of integrating this powerful technology into their processes, it's high time your enterprise thought about putting machine learning to work, too.

Machine learning may sound like an overwhelmingly complicated concept rather than a data-driven method to extract insights that drive future business decisions. To fully utilize machine learning, we first need to understand the benefits to our organization, and the techniques to create models based on questions we need to answer. In this webinar series, we will show you how to easily and automatically apply complex algorithms to data in real world applications.