Perpetual ML is an AI tool that leverages a unique technology, known as Perpetual Learning, to drastically accelerate model training. This acceleration is chiefly achieved by removing the time-consuming hyperparameter optimization step, thus providing substantial speed-ups.
It offers a range of capabilities including initial fast training via a built-in regularization algorithm, the convenience of continual learning enabling models to be trained incrementally without starting from scratch with each new batch of data, and enhanced decision confidence through built-in Conformal Prediction algorithms.
Additionally, it provides methods for improved learning of geographical decision boundaries and has a feature to monitor models and detect distribution shifts.
The platform is suitable for various machine learning tasks such as tabular classification, regression, time-series, learning to rank tasks and text classification, among others.
It offers portability across various programming languages, including Python, C, C++, R, Java, Scala, Swift, and Julia, owing to its Rust backend. Designed with a focus on computational efficiency, Perpetual ML doesn't require specialized hardware for its operations.
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Pros & Cons
Accelerates model training
Removes hyperparameter optimization
Initial fast training
Offers continual learning
Enhanced decision confidence
Conformal Prediction algorithms
Geographical Decision Boundary Learning
Detects distribution shifts
Supports multiple ML tasks
Supports various programming languages
No specialized hardware required
Compatible with Python
Compatible with C
Compatible with C++
Compatible with R
Compatible with Java
Compatible with Scala
Compatible with Swift
Compatible with Julia
Rust backend
Improves geographic data learning
Built-in regularization algorithm
Enhances tabular classification
Enhances time-series learning
Improves regression tasks
Enhances learning to rank tasks
Improves text classification
Portability
Computational efficiency
Model monitoring feature
No need for another monitoring tool
Aids in distribution shift detection
Doesn't require GPU or TPU
Effortless parallelism
Leverages existing hardware
100x speed up in training
Removes need to start from scratch
Increased decision confidence
Applicable across diverse industries
Resource efficiency
Can be used for limitless applications
Not ecosystem dependent
No hardware specialization
No hyperparameter optimization
Requires continual retraining
Dependent on Rust backend
May oversimplify model complexity
Limited model monitoring
Geographical learning biases
Unspecified regularization methods
Unspecified confidence measurement
Only suitable specific tasks