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.
Data labeling is the act of adding additional context or insight to an image or file. For problems that require a specialized type of insight, this labeling is often done by a human with the goal of building a model that can be used to replace human labeling over time.
A Convolutional Neural Network is a special class of neural networks that are built with the ability to extract unique features from image data. For instance, they are used in face detection and recognition because they can identify complex features in image data.
Do you remember the first time you started to build some SQL queries to analyse your data? I’m sure most of the time you just wanted to see the “Top selling products” or “Count of product visits by weekly”.
Deploying machine learning models is a small part of the entire machine learning life cycle. Once a model is in production, you have to monitor it to ensure it’s working as expected.