Machine learning has become an essential tool for developers, enabling the creation of applications that can perform a wide range of prediction-based tasks. Traditionally, building such applications required complex rules engines and statistical models, often relying on numeric techniques. Today, machine learning platforms simplify the process, allowing developers to build, train, and test models more efficiently. These platforms make it possible to apply machine learning to real-world problems, from recognizing patterns in images to understanding language, by leveraging pre-built models or training new ones.
Machine learning outputs, often referred to as predictions, can vary depending on the application. In computer vision, predictions could be the identification of objects within an image, while in natural language processing, predictions might include intent recognition or translations. Regardless of the type of model, machine learning predictions are statistical in nature, offering a response with an associated confidence level. This confidence level helps determine the reliability of the output and allows for informed decision-making based on the model’s predictions.
When working with machine learning, there are two key stages: running prebuilt models and training new models. Running prebuilt models is relatively straightforward and can be done through cloud platforms like Azure ML, which offer REST APIs for accessing predictions. Alternatively, models can be exported in the ONNX format and run on a local PC using tools like WinML. The more challenging aspect is training and testing new models. This process demands a large amount of labeled data, computational resources, and often a high-performance GPU or CPU for training, which can be a significant barrier for smaller teams or individual developers.
For many developers, particularly those just starting with machine learning or working on small-scale prototypes, training models on a personal PC can be an attractive option. While cloud-based platforms like Azure’s Machine Learning studio provide powerful resources, they come at a cost, especially when large virtual machines and extensive data storage are needed. For those experimenting with smaller datasets or learning the fundamentals, training models locally on a PC can be both cost-effective and practical. DirectML, a hardware-accelerated API for machine learning on Windows, enables this by providing GPU-accelerated training capabilities, making it easier to leverage the power of machine learning on a personal PC without the need for expensive cloud infrastructure.