Machine learning (ML) has emerged as a core technology ingredient for
organizations to drive innovation. Today, more than a hundred thousand
organizations are leveraging artificial intelligence (AI) and ML solutions and
services from Amazon Web Services (AWS) to achieve substantial business
results. These businesses span virtually every industry, including financial
services, healthcare, media, professional sports, retail, and the industrial sector.
The relevance and impact of ML are expected to accelerate. According to IDC,
by 2025, global spending on AI will reach $204 billion.
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Amidst the successes and growth, however, challenges to widespread ML
adoption persist. Many organizations—enticed by the multitude of potential
benefits—have grown frustrated by slow progress and a lack of return on
their ML investments. For these organizations to reach their goals, they must
find ways to put models into production faster and at a lower cost, ultimately
scaling the technology to produce results across the entire business.
In this eBook, we'll explore the major barriers to ML scalability and success.
Then we'll demonstrate how solutions and services from AWS can help
virtually any organization overcome those challenges—and leverage ML to
drive innovation and achieve tangible business results.
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INTRODUCTION
Solve for machine learning scalability
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"Investment in Artificial Intelligence Solutions Will Accelerate as Businesses Seek Insights, Efficiency, and Innovation,
According to a New IDC Spending Guide," IDC, 2021
Top 5 barriers to achieving machine learning results at scale:
Data
processing
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Expensive
infrastructure
4
Lack of development
tools and MLOps
5
Acquiring data
science skills
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Responsible use of
machine learning
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