ML models can add automation to mundane, repetitive tasks and let humans focus on more mission-critical work. ML also gives businesses the power to extract meaning from the massive amounts of data they are collecting and generating annually.
Machine learning (ML) and artificial intelligence (AI) technologies are increasingly on the investment list for IT leaders. Among the many benefits of these technologies, building and deploying ML models can add automation to mundane, repetitive tasks and let humans focus on more mission-critical work. ML also gives businesses the power to extract meaning from the massive amounts of data they are collecting and generating annually.
The productivity and analytics gains are not lost on business leaders. In IDG’s 2019 Digital Business Study , 78% of IT and business leaders said their organization is considering or has already deployed machine learning technologies as part of their digital business strategy. And as CIO.com observes , machine learning is one of the highest in-demand skills in today’s technology job market.
As ML models gain more traction throughout the enterprise, we asked our IDG Influencer community of experts about the biggest challenges to scaling machine learning across the enterprise, and the best ways to overcome those challenges. Here’s a summary of their insights. Time and complexity
Common roadblocks as organizations look to scale machine learning across the enterprise are, perhaps not surprisingly, time and complexity.
“One challenge we regularly encounter when working with clients is the time it takes to develop and deploy machine learning models and associated machine learning applications,” said Gene De Libero ( @GeneDeLibero ), Chief Strategy Officer and Head of Consulting with GeekHive. “We’ve found that applying continuous delivery principles to scaling machine learning across the enterprise helps us deliver services faster and more reliably. This approach also helps reduce overall risk and shorten the time it takes to attain speed and scale.”
Scott Schober ( @ScottBVS ), President and CEO of Berkeley Varitronics Systems, Inc. noted the larger the organization, the harder it is to extract enterprise-wide value.
“Large companies can be fragmented and tend to build up many siloed teams over time,” he said. “This independence among employees and teams can hinder homogenous adoption of ML systems, which are only effective when implemented throughout the entire organization and its shared data sets. Only when a company carefully structures itself by implementing a clear centralized effort, can It greatly reduce or eliminate the fragmented processes and overcome the challenges of scaling machine learning.”
Data fuels machine learning – and one of the hurdles is ensuring that ML models and underlying systems are set up to deliver accurate results and information, said Jeff Cutler (@JeffCutler), a technology journalist.
“The biggest challenge to machine learning is believing the simulations and being able to make decisions based on the data mined and processed by your systems,” he said. “If you put your trust in bad data –or in theories and projections based on bad data – you’re lost.”
Creating a track record of positive, data-driven decisions that you can share with the organization will help to justify the value of machine learning, Cutler added.
Jo Peterson (@digitalcloudgal), VP of the Cloud Services Practice at Clarify360, also stressed the importance of high-quality data. “AI technology is data driven,” she said. “Clean, quality data that is comprehensively managed and integrates across systems is a good starting point.”
The volume and quality of data are both critical for scaling a machine learning proof of concept (POC) into production, says Sri Elaprolu (@SriElaprolu), Senior Leader, Amazon Machine Learning Solutions Lab. “Make sure the data you’re using in the POC is representative of the real world,” he said. “Not just doing it one time, but rather thinking about what you’ll need as you’re collecting more data.” […]
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