Microsoft recently released the newest version of its machine learning framework ML.NET, ML.NET 3.0. With the ML.NET new edition 3.0 from Microsoft, programmers can fully take advantage of resource acceleration calculations during training because of several body acceleration improvements from previously released ML.NET 2.0.
ML.NET 3.0 offers algorithmic building blocks for the entire machine learning process and enhanced workload efficiency when installed alongside Intel’s oneDAL (oneAPI Data Analytics Library) beta kit. Installing them together accelerates the process by using 64-bit architectures standard to AMD and Intel CPUs for better performance.
With ML.NET 3.0, one can import more machine learning capabilities to existing .NET applications and directly create predictions using application data. This feature cuts the requirement of undertaking comprehensive programming steps to generate prediction models.
Additionally, developers can import previously trained TensorFlow and ONNX models or train a new custom model by inputting algorithms.
TensorFlow, ONNX, and OneDAL integration into ML.NET in the newest ML.NET 30 version further the model in analyzing massive datasets and generating more accurate predictions. This development of more advanced ML.NET integrations is a beginning point for machine learning, and developers can expect more such updates.
Refer to the ML.NET 3.0 (prerelease) documentation for more information.