Solving the biggest
machine learning
challenges
Most organizations have made some investments in ML and
have made progress on their ML journeys. But many find
themselves hitting stopgaps along the way, worried that
costs and complexities will grow too high as they progress.
Throughout this eBook, we explored the steps to forge
ahead and realize the full power of ML. To recap, let's look
at the biggest challenges we identified along the way, along
with a brief recommendation of how your organization can
solve them.
Challenge Solution
Discouragement
from failures
Developing a fault-tolerant culture
Siloed, unprocessed
data
Creating a modern data strategy that includes
data lakes
Finding the right
business problems
Building blended teams that include both technical
and domain experts
The ML skills gap Adopting new organizational models, processes,
no-code tools, and team management philosophies
Sustainably scaling
beyond pilot
projects
Leveraging end-to-end tools like Amazon SageMaker
to simplify ML development
Measuring
the results
Forgoing traditional ROI metrics in favor of agility,
competitive advantage, and risk tolerance; use the
value tree model
To learn more about how you can overcome
obstacles and accelerate your ML journey,
visit the AWS ML resource hub.
Get started ›
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