Introduction
Amazon Web Services (AWS) offers a wealth of services and tools that help data scientists leverage machine learning to craft better, more intelligent solutions. This article is ideal for data scientists, programmers, and machine-learning enthusiasts who want to learn about the artificial intelligence and machine learning capabilities of
the Amazon Web Services.
What is cloud computing
Cloud computing is the on-demand delivery of IT resources over the Internet with pay-as-you-go pricing.
Cloud Service providers
- Amazon Web Services
- Google Cloud platform
- Microsoft azure
- IBM Cloud
- Digital Ocean
- Terremark
What is AWS
Amazon Web Services(AWS) is a secure cloud services platform, offering compute power, database, storage, content delivery and other functionality to help businesses scale and grow.
Why Amazon Web Services
- Easy to use
- Flexible
- Cost-Effective
- Reliable
- Scalable and high-performance
- Secure.
What is Machine Learning?
Machine Learning is a subset of Artificial Intelligence in the field of computer science that often uses statistical techniques to provide computers with the ability to learn
with data without being programmed.
What is Amazon Machine Learning?
Amazon Machine Learning is a service that allows users to create prediction apps based on their data using algorithms and mathematical models.
What is Amazon SageMaker?
Amazon SageMaker helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning models quickly by bringing together a broad set of capabilities purpose-built for machine learning.
Why Amazon SageMaker?
In the hosted environment, many data scientists design, train, and deploy machine learning models. Unfortunately, they did not have the capability to scale up or down resources as needed. AWS SageMaker addresses this problem by making it easier for developers to construct and train models in order to bring them into production faster and at a reduced cost.
How machine learning works with AWS SageMaker.
Prepare and build the models -Connecting to additional AWS services like S3 and manipulating data in Amazon SageMaker notebooks completes the build stage.
Train and tune -The train step is about using AWS SageMaker's algorithms and frameworks for distributed training, or bringing our own.
Deploy and analyze - Models can be delivered to Amazon SageMaker endpoints for real-time or batch predictions once they've been trained.
Conclusion
This article gives a brief introduction on Machine learning with AWS. Next we will learn hoe to build, train, and deploy Machine Learning Models using AWS SageMaker.
Stay Tuned🎈
Top comments (0)