Programmers know data types, but what is a feature type to a programmer new to machine learning, given no mainstream programming language has native support for them?
Operational machine learning requires the offline and online testing of both features and models. In this article, we show you how to design, build, and run test for features.
Evolve your models from stateless AI to Total Recall AI with the help of a Feature Store.
Use JOINs for feature reuse to save on infrastructure and the number of feature pipelines needed to maintain models in production.
Hopsworks supports machine learning experiments to track and distribute ML for free and with a built-in TensorBoard.
Integrate with third-party security standards and take advantage from our project-based multi-tenancy model to host data in one single shared cluster.
We have many conversations with companies and organizations who are deciding between building their own feature store and buying one. We thought we would share our experience of building one.
Read how ExtremeEarth brings Large-scale AI to the Earth Observation Community with Hopsworks, the Data-intensive AI Platform.
This blog is a guide to the popular file formats used in open source frameworks for machine learning in Python, including TensorFlow/Keras, PyTorch, Scikit-Learn, and PySpark.
Read how Hopsworks supports easy hyperparameter optimization (both synchronous and asynchronous search), distributed training using PySpark.
If you are employing a team of Data Scientists for Deep Learning, a cluster manager to share GPUs between your team will maximize utilization of your GPUs.
Why HopsFS is a great choice as a distributed file system (DFS) in a time when DFS is becoming increasingly indispensable as a central store for training data, logs, model serving, and checkpoints.
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