Whitepaper
Managing Machine Learning Workflow
with Amazon SageMaker: New Services,
Use Cases, and Best Practices
Artificial intelligence and machine learning have a major impact across markets. Businesses want to drive change with AI, yet their IT and engineering teams face challenges managing complex, nuanced AI tools and services. They need AI/ML stack to dynamically evolve to meet their needs.
In 2019, AWS released Amazon SageMaker Studio, a web-based IDE that tightly integrates all components of the ML ecosystem within a single interface, in order to address the challenges of Data Scientists and ML Engineers.
In this whitepaper, we will look into Amazon SageMaker Studio:
- Benefits, features, and use cases
- Specific services (in detail)
- Amazon SageMaker Experiments
- Amazon SageMaker Debugger
- Amazon SageMaker Autopilot
- Amazon SageMaker Model Monitor
- Amazon SageMaker Elastic Inference
- How each of these services work, and more
Amazon SageMaker Studio is the ultimate platform for writing code, tracking experiments, visualizing data, and performing debugging and monitoring. Learn how it can help tech professionals do more with AI/ML!
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Artificial intelligence and machine learning have a major impact across markets. Businesses want to drive change with AI, yet their IT and engineering teams face challenges managing complex, nuanced AI tools and services. They need AI/ML stack to dynamically evolve to meet their needs.
In 2019, AWS released Amazon SageMaker Studio, a web-based IDE that tightly integrates all components of the ML ecosystem within a single interface, in order to address the challenges of Data Scientists and ML Engineers.
In this whitepaper, we will look into Amazon SageMaker Studio:
- Benefits, features, and use cases
- Specific services (in detail)
- Amazon SageMaker Experiments
- Amazon SageMaker Debugger
- Amazon SageMaker Autopilot
- Amazon SageMaker Model Monitor
- Amazon SageMaker Elastic Inference
- How each of these services work, and more
Amazon SageMaker Studio is the ultimate platform for writing code, tracking experiments, visualizing data, and performing debugging and monitoring. Learn how it can help tech professionals do more with AI/ML!
