Machine learning, a subset of artificial intelligence, is becoming an important addition for many companies. It is the study of algorithms and statistical models that computers use to perform specific tasks without needing prior instructions, relying on models and inference instead.
This type of technology is commonly being used to help companies work more efficiently and learn more from the data available through their websites and online services. Amazon SageMaker allows developers to build, train, and deploy machine learning fast and with ease. Like many AWS products, this is a fully-managed service. SageMaker facilitates the entire machine learning process, from labeling and preparing data, to training algorithms and even making predictions and recommendations after it is deployed, all at a fraction of the time and cost it would take without it.
Amazon SageMaker Features
Build – Amazon SageMaker gives you the tools to build out and test Machine Learning processes without needing a lot of money and a huge team.
- Build highly accurate training datasets
- Managed Notebooks for Authoring Models with Jupyter
- Built-in, High Performance Algorithms
- Broad Framework Support
- Reinforcement Learning Support with Amazon SageMaker RL
- Test and Prototype Locally
Train – Whether you are starting from scratch or utilizing an existing model, SageMaker makes it easy to train and update with ease.
- One-click Training
- Automatic Model Tuning
- Train Once, Run Anywhere
- Training Job Search
Deploy – Once you have built and trained a model it’s time to deploy it into the real world. AWS has all the resources and tools to make this extremely easy.
- One-click Deployment
- Automatic A/B Testing
- Fully-managed Hosting with Auto Scaling
- Batch Transform
- Inference Pipelines
Finding the best tools and solutions that can support building out a Machine Learning framework is critical to your success since the way you build and train the machine will make a big difference in how it runs. Amazon SageMaker gives you the support you need to build and test a Machine Learning framework efficiently before going live.