Solving the biggest
machine learning
challenges
Most organizations have made some investments in
machine learning and are at some stage of the journey.
But many find themselves hitting stopgaps along the way,
worried that costs and complexities will grow too high as
they progress.
In this eBook, we explored the steps toward forging ahead
and realizing the full power of machine learning. To recap,
let's look at the biggest challenges we identified along
the way—with a brief descriptor of how organizations 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 machine
learning skills gap
Adopting new organizational models, processes, and
team management philosophies
Sustainably scaling
beyond pilot
projects
Leveraging end-to-end tools like Amazon SageMaker
to simplify machine learning development
Measuring the
results
Forgo traditional ROI metrics in favor of agility,
competitive advantage, and risk tolerance; use the
value tree model
To learn more about how
organizations can overcome obstacles
and accelerate their machine learning
journeys, visit the AWS machine
learning resource hub.
Get started ›
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