.. rst-class:: lead Train, tune and deploy state-of-the-art machine learning models for time series in Amazon SageMaker
We provide Amazon SageMaker algorithms for multiple time series tasks, including forecasting, anomaly detection, classification and clustering. Each algorithm implements a state-of-the-art machine learning model designed specifically for time series.
- Automated Data Handling
- The algorithms work directly on raw time series data in CSV format. All the required data preprocessing and scaling is performed internally by the algorithm's code.
- Automatic Model Tuning
- The algorithms support automatic model tuning for optimizing the model hyperparameters in order to achieve the best possible performance on a given dataset.
- Incremental Training
- Most of the algorithms support incremental training to continue training the model on the same dataset or to fine-tune the model on a different dataset.
- Accelerated Training
- The algorithms were built by extending the latest deep learning containers and support both CPU and GPU training. Most of the algorithms also support multi-GPU training.
Each algorithm has a dedicated GitHub repository with detailed documentation and step-by-step tutorials in Jupyter notebook format. Several use cases are also discussed in our blog.
The algorithms are available on the AWS Marketplace on a usage-based pricing plan. Each algorithm offers a 5 days free trial.
For support, contact support@fg-research.com.
.. grid:: 3 .. grid-item:: :columns: 5 .. toctree:: :caption: Algorithms :maxdepth: 1 algorithms/time-series-forecasting/index algorithms/time-series-anomaly-detection/index algorithms/time-series-classification/index algorithms/time-series-clustering/index .. grid-item:: :columns: 3 .. toctree:: :caption: Blog :maxdepth: 1 blog/product/index blog/general/index .. grid-item:: :columns: 4 .. toctree:: :caption: Terms and Conditions :maxdepth: 1 terms/disclaimer/index terms/eula/index