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Saturday, December 16, 2017

Overcoming The Challenges Of Machine Learning Model Deployment | BCW - Business

Yvonne Cook, General Manager at DataRobot UK summarizes, "Our societies and economies are in transition to a future shaped by artificial intelligence (AI)." 

Photo: BCW

To thrive in this upcoming era, companies are transforming themselves by using machine learning, a type of AI that that allows software applications to make accurate predictions and recommend actions without being explicitly programmed.  

There are three ways that companies successfully transform themselves into AI-driven enterprises, differentiating them from the companies that mismanage their use of AI:
  • They treat machine learning as a business initiative, not a technical speciality.
  • They have higher numbers of machine learning models in production.
  • They have mastered simple, robust, fast, and repeatable ways to move models from their development environment into systems that form the operations of their business.
Commercial payback from AI comes when companies deploy highly-accurate machine learning models that operate robustly within the systems that support business operations. 

Why Companies Struggle With Model Deployment
While hard data is scarce, anecdotal evidence suggests that it is not uncommon for companies to train more machine learning models than they actually put into production. Challenges to organisation and technology are in play here, and success requires that both are addressed. From an organisational perspective, many companies see AI enablement as a technical speciality. This is a mistake.

AI is a business initiative. Becoming AI-driven requires that the people currently successful in operating and understanding the business can also create tomorrow’s revenue and be responsible for both building and maintaining the machine learning models that grow revenues. To succeed, these business drivers will need collaboration and support from specialists, including data scientists and the IT team.

Machine learning models must be trained on historic data, which demands the creation of a prediction data pipeline. This is an activity that requires multiple tasks including data processing, feature engineering, and tuning. Each task, down to versions of libraries and handling missing values, must be exactly duplicated from the development to the production environments, a task with which the IT team is intimately familiar...

Summary
AI and machine learning offer companies an opportunity to transform their operations. IT professionals play a critical role in ensuring that the models developed by their business peers and data scientists are suitably deployed to succeed in serving predictions that optimise business processes. Automated machine learning platforms allow business people to develop the models they need to transform operations while collaborating with specialists, including data scientists and IT professionals.

Choosing an enterprise-grade automated machine learning platform will certainly make IT’s life easier. By providing guidance on organising for successful model deployment and the choice of appropriate technology, IT executives ensure their teams are recognised for their effective contribution to the company’s success as it transforms into an AI-driven enterprise.
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Source: BCW


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