The number of test projects for Machine Learning and AI that are being deployed in Enterprise are less than 10% as of now according to a recent report from International Institute for Analytics. You would be surprised to see that such small amount of project getting initiated instead of the hype in market but it can be said that these are due to lack of easy-to-use tools for overwhelming amount of data analysis. It’s a problem that calls for operationalizing AI and machine learning, making it accessible and repeatable consistently.
“Ultimately, if you want to get business value from those models and all of the hard work that you’ve done, it has to be injected into the business process,” said Anant Chintamaneni, vice president and general manager of BlueData at Hewlett Packard Enterprise Co. “Operationalization of machine learning is ultimately the key, and that’s the progression that enterprises have to make.”
The issue with operationalizing Machine Learning is that it takes an effort from lot of people like data scientists, data engineers and machine learning architects to select right algorithm, clean inputted data to build predictors and programs. HPE launched ML Ops, to help team of these people which is a container-centric software solution based on the technology of BlueData which they acquired last year. The software platform of BlueData containers, virtualization and big-data.
Organizations will have to take an architect approach to build data science into a business process. In the past deploying complex analytic tools into complex enterprise environments has hindered ability of consistent output. With this it becomes important that tools being developed are easy to use, easily accessible while maintaining secure operationalized environment. Security is the most important aspect today- no matter where the model gets built or where users access it.
Spending in Machine Learning is going to touch new heights in next four years compared to what it is now for enterprises. Many of them are looking forward to making programs that will learn and grow by themselves and can be applied to human interactions in different environments. It will not be easy task at hand to work on the amount of data that is being collected but whatever they are working on will benefit in the long run to grow businesses and achieve strategic goals. A lot of talent and tools have gone into making these models but the main aspects of this innovation will be to operationalize it so that it can be used to get desired results.
Adapting to change is the major problem area for many enterprises for which they need easy to use tools for employees so that tougher models don’t give a setback to their big-data analytics. Automating monotonous tasks will be the one area that enterprises are looking to sort but tasks like customer chat cannot be fully automated as a customer wants that human touch to remain on when they contact customer care.