Examine the barriers to
machine learning success
For many organizations, ML has proven difficult to scale, leading to a
lack of progress and frustration with the technology.
With the right services, solutions, tools, and processes, any organization
can achieve success with ML and scale it across their business. But
determining what those solutions are—and how best to implement
them—starts with examining and understanding the barriers that must
be overcome.
In that spirit, let's take a look at the five greatest challenges to driving
widespread adoption and business results with ML.
1 Data processing
Data processing is very time-consuming, typically comprising about 80
percent of an ML project. Further, ML models are built on an enormous
foundation of data from multiple modalities—tabular, text, audio,
video, and others—which need to be managed differently. There are
many disparate tools for processing structured data, and individual
teams will have their own preferred approach. This makes it difficult for
organizations to centralize their efforts into a single method for creating
data pipelines.
Also, unstructured data must be properly cleaned and labeled before
it can be made usable for ML. But setting up data labeling workflows,
validating label quality, and managing labelers can be a time-, cost-, and
resource-intensive process—especially when skilled ML developers and
data scientists are hard to find.
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