.Net Engineering Team has officially announced the release of ML.NET 0.6. the new framework has been released as the routine monthly release of their cross-platform open source framework.
- Performance improvement in the new release
- Improvement of all Fronts
- Large model export capacity
- Simplifying the use of Tensorflow Model
Performance improvement in the new release
There are various exciting features released by the Team in the new framework. The new features promise on improving the experience by proper usage of the machine learning models, performance improvement and more.
Improvement of all Fronts
The team suggests that the brand-new API is better suited for new tasks and code workflow. This, they claim was not possible with the previous LearningPipeline API.
Phasing out the current Learning Pipeline API is also a part of the plan as the team suggests. The new API according to the developers would expand the list of scenarios its supports.
Large Model export capacity
Further, it is line with the Machine learning principles and naming from various other well-known ML related frameworks such as Apache Spark and Scikit – Learn. In the previous update, ML.NET v0.3, the capacity to exporting the ML.NET models to the ONNX -ML format was added.
It was done to ensure that, the additional execution environments could run swiftly through the model. In the latest version, ML.NET can also utilize the ONNX models to score/predict trained ONNX models running on ONNX standard v1.2. This has been made possible by using a new transformer and runtime for scoring the ONNX models.
Simplifying the use of Tensorflow Model
The new update has also made it simpler to use TensorFlow models in ML.NET. The team has added an API to identify the nodes in the TensorFlow model to identify the input and output of a TensorFlow model.
In ML.NET 0.6, TensorFlow Models which are in the saved formats can also be used. On performance front also, the latest release promises good such as improvement in making single predictions from a trained model.
The Legacy Learning Pipeline API has been moved to the new Estimator API. Again, optimization has been done in the performance of prediction function in the new API.
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