- Google services are designed to work together. It works with Cloud Dataflow for feature processing, Cloud Storage for data storage and Cloud Datalab for model creation.
- Discover and Share Samples
- Discover and share our Machine Learning Samples tailored to your industry use case.
- Build better performing models faster by automatically tuning your hyperparameters with HyperTune, instead of spending many hours to manually discover values that work for your model.
- Managed Service
- Focus on model development and prediction without worrying about the infrastructure. Managed service automates all resource provisioning and monitoring.
- Scalable Service
- Build models of any data size or type using managed distributed training infrastructure that supports CPUs and GPUs. Accelerate model development, by training across many number of nodes, or running multiple experiments in parallel.
- Notebook Developer Experience
- Create and analyze models using the familiar Jupyter notebook development experience, with integration to Cloud Datalab.
- Portable Models
- Use the open source TensorFlow SDK to train models locally on sample data sets and use the Google Cloud Platform for training at scale. Models trained using Cloud Machine Learning Engine can be downloaded for local execution or mobile integration.
“ Google Cloud Machine Learning Engine enabled us to improve the accuracy and speed at which we correct visual anomalies in the images captured from our satellites. It solved a problem that has existed for decades. It will allow Airbus Defence and Space to continue to provide unrivaled access to the most comprehensive range of commercial Earth observation data available today ”— Mathias Ortner Data Analysis & Image Processing Lead, Airbus Defense & Space
|Custom Cluster Configuration||$0.49/hour per ML training unit||$0.54/hour per ML training unit|
|Basic GPU Tier||$1.47/hour||$1.62/hour|
|Up to 100M per Month||
$0.10 / 1K
$0.11 / 1K
|Requests over 100M per month||
$0.05 / 1K
$0.05 / 1K