Perpetual ML is an AI tool distinguished by its innovative Perpetual Learning technology, which significantly accelerates model training. This acceleration is primarily achieved by bypassing the time-consuming hyperparameter optimization step, resulting in notable speed improvements.
Its features include swift initial training facilitated by a built-in regularization algorithm, the flexibility of continual learning allowing for incremental model training without starting anew with each data batch, and augmented decision confidence courtesy of integrated Conformal Prediction algorithms.
Furthermore, Perpetual ML offers functionalities for refining geographical decision boundaries and includes a feature for monitoring models and identifying distribution shifts.
The platform caters to diverse machine learning tasks such as tabular classification, regression, time-series analysis, learning to rank tasks, and text classification, among others.
Its versatility extends to various programming languages including Python, C, C++, R, Java, Scala, Swift, and Julia, thanks to its Rust backend. Engineered with computational efficiency in mind, Perpetual ML operates without the need for specialized hardware.
More details about Perpetual ML
Why is Perpetual ML considered to be 100 times faster?
Perpetual ML’s exceptional speed, touted as 100 times faster, stems from its innovative Perpetual Learning technology. By integrating a built-in regularization algorithm, it eliminates the need for time-consuming hyperparameter optimization. This approach drastically accelerates initial model training, making Perpetual ML exceptionally fast and efficient in machine learning tasks.
What does Perpetual Learning entail in Perpetual ML?
Perpetual Learning within Perpetual ML is a unique technology designed to expedite model training. It enables models to undergo incremental training without the need to restart with each new batch of data. This continuous learning capability significantly enhances computational efficiency and facilitates ongoing model refinement.
What features are incorporated into Perpetual ML?
Perpetual ML incorporates several built-in features, including rapid initial training via a built-in regularization algorithm, continuous learning capabilities, enhanced decision confidence through Conformal Prediction algorithms, methods for learning geographical decision boundaries, and a feature for detecting distribution shifts. Together, these features optimize machine learning model development, monitoring, and usage.
Can I use my existing hardware and software with Perpetual ML?
Yes, you can utilize your current hardware and software setups with Perpetual ML. The platform is designed to operate without the need for specialized hardware like GPUs or TPUs. This compatibility allows for cost savings, simplified setup processes, and reduced complexity when integrating Perpetual ML into your existing infrastructure.
