Machine Learning - eBook (EN)

ML Six steps to machine learning success

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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 › 2023, Amazon Web Services, Inc. or its affiliates. All rights reserved.

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