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While machine learning offers advantages for nearly every industry, very few companies have actually adopted this artificial intelligence (AI) technology, and face several common barriers to entry, according to a new Deloitte report.
Less than 10% of executives said that their companies were investing in machine learning, according to a recent SAP survey, and many cite barriers to adoption including qualified staff, still-evolving tools and frameworks, and a lack of large datasets required to train algorithms. Many people also face the "black box" problem, in that they understand that machine learning models generate valuable information, but are reluctant to deploy them in production, because their inner workings are not immediately clear.
To lower the barriers to entry, Deloitte researchers identified five "vectors of progress" that make it easier, faster, and less expensive to deploy machine learning in the enterprise:
1. Automate data science
Developing machine learning solutions requires data science skills, a field in which practitioners are in large demand and short supply. However, as much as 80% of the work of data scientists can be fully or partially automated, according to Deloitte, including data wrangling, exploratory data analysis, feature engineering and selection, and algorithm selection and evaluation.
"Automating these tasks can make data scientists not only more productive but more effective," the report stated. A growing number of tools from both established companies and startups can help reduce the time required to execute a machine learning proof of concept from months to days, Deloitte noted. This also means augmenting data scientists' productivity, so that even with a talent shortage, enterprises can still expand their machine learning adoption.
2. Reduce the need for training data
Training a machine learning model can require up to millions of data elements, and acquiring and labeling this data can be time consuming and costly for enterprises.
However, we've seen a number of techniques emerging for reducing the amount of training data required for machine learning. Some use synthetic data, generated with algorithms to mimic the characteristics of the real data, and have seen strong results: A Deloitte LLP team tested a tool that allowed it to build an accurate model with only a fifth of the training data previously required by synthesizing the remaining 80%.
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Source: TechRepublic
