How ModelOps Helps You Execute Your AI Strategy
Artificial Intelligence is a hotter topic today than ever. From self-driving cars to personal assistants, AI is slowly making its way into our daily lives. Artificial Intelligence (AI) is an area of computer science that studies the possibility of thinking computers and machines.
What’s more, it’s no longer a theory but a reality in many ways. There are already many applications in place that have been developed with the help of AI, including business applications.
The past decade has seen an explosion of applications for artificial intelligence, machine learning, and deep learning. This has led to advances in a wide range of application domains, including document classification and processing, natural language understanding, and bioinformatics. A growing number of highly effective methods for speech processing and image classification are being applied successfully to robotics and computer vision. We are witnessing exponential growth in the use of deep learning in web search algorithms, translators, speech recognition, image and photo classifiers. Furthermore, artificial intelligence has allowed us to automate repetitive tasks with little or no human intervention.
There has been a lot of talks lately about the promise of value linked to the potential of artificial intelligence. However, the return on investment in AI is proving difficult for many organizations, and worse, they see themselves falling behind others who have overcome the challenge. Often, the discussion revolved around technologies that were fundamental and enabling, but not enough to generate business results. For most organizations, interest in AI has initially been focused on the delivery of analytical and machine learning models as artifacts in this first phase of its exploitation. On the other side, the aspect of their integration with IT and business delivery processes has been overlooked, that is, the aspect of their “operationalization”.
The latter aspect is fundamental to reduce the “time-to-value”, considering that it takes months to integrate an AI model within a business workflow and to achieve the first benefits.
There are many reasons for this, but a key one is that AI requires different skills than traditional software development. Most companies have data scientists who can create models and algorithms that power their artificial intelligence systems, but few have technical staff who can create and maintain the software infrastructure that enables these models to run and large-scale algorithms. At a high level, the software infrastructure required for AI may seem similar to that required for traditional software development. But at a lower level, it requires different skills and thinking. The people best suited to build this infrastructure are those with experience in both technology, process management and a strategic vision of business and innovation.
The reasons behind the complexity are different. The lack of security and privacy, inadequate volume and quality of data, accessibility challenges, limited understanding of the use cases and related benefits, insufficient internal skills, to name just a few. The real risk of such a situation is to create skepticism on the part of important stakeholders about the benefits and transformative impact of AI on their organization. The enthusiasm for AI may fade and AI will be perceived as just another technology, rather than something that can fundamentally change the way organizations operate.
What can we do to handle this complexity? Data is of course the common element underlying all of the interdependencies. Although it is necessary to work on technologies, it is also necessary to consider the aspect of work practices and operating models.
