XGBoost is a machine learning library based on the gradient boosting framework. It can run in distributed environments and can handle billions of data samples. Unlike LightGBM, it uses depth-wise tree growth. XGBoost also generates feature importance to help you identify the most critical features. This article will look at
Building machine learning models is an experimental process that requires several iterations. You change different model parameters or data preprocessing steps at each iteration to obtain an optimal model. It is vital to keep track of the processing steps and the parameters at each iteration. Failure to do this leads
LightGBM is a popular gradient boosting framework that places continuous values in discrete bins leading to faster training and efficient memory utilization. It uses leaf-wise tree growth, unlike other algorithms that use depth-wise growth. The algorithm can handle missing values and categorical features by default. You can use LightGBM for
With this integration, you can now instantiate SAHI models from Layer. For example, you can pass the path of a YOLOv5 model trained on Layer to instantiate a DetectionModel. Here we fetch the yolo5vs pretrained model from this Layer project.
A metadata store is a central repository for storing all data generated in the process of building machine learning models. This data includes dataset versions, model versions, model parameters, model evaluation metrics, CPU and GPU utilization, just to mention a few.
We have added support for summary stats for your dataset columns on the Layer web interface. We have added percentiles on the data summary statistics to make it easier for you to see a quick description of your datasets. It's now much easier to get a picture of your training data profile.
Today, Layer goes open-source to make machine learning more accessible and contribute to ML's growth and evolution. Machine Learning is becoming the default way to build technology. It's how you make your apps smarter, your systems more reliable and your businesses smarter.
Layer helps you build, train and track all your machine learning project metadata including ML models and datasets with semantic versioning, extensive artifact logging, and dynamic reporting with local↔cloud training.
Layer is the collaboration-first metadata store for production ML that enables build, train and track all of your machine learning project metadata including ML models and datasets with semantic versioning, extensive artifact logging and dynamic reporting with local↔cloud training.
One of the biggest problems in the machine learning (ML) space is models eventually regress — it is challenging to maintain high-performing AI solutions in the long winding journey from development to production, especially at scale.