Tensor Input Now Supported in MLflow
Traditionally, generic MLflow Python models only supported DataFrame input and output. While DataFrames are a convenient interface when dealing with classical models built over tabular data, it is not convenient when working with deep learning models that expect multi-dimensional input and/or produce multidimensional output. In the newly-released MLflow 1.14, we’ve added support for tensors, which...
Quickly Deploy, Test, and Manage ML Models as REST Endpoints with MLflow Model Serving on Databricks
MLflow Model Registry now provides turnkey model serving for dashboarding and real-time inference, including code snippets for tests, controls, and automation. MLflow Model Serving on Databricks provides a turnkey solution to host machine learning (ML) models as REST endpoints that are updated automatically, enabling data teams to own the end-to-end lifecycle of a real-time machine...
Announcing MLflow Model Serving on Databricks
Databricks MLflow Model Serving provides a turnkey solution to host machine learning (ML) models as REST endpoints that are updated automatically, enabling data science teams to own the end-to-end lifecycle of a real-time machine learning model from training to production. When it comes to deploying ML models, data scientists have to make a choice based...
Introducing Built-in Image Data Source in Apache Spark 2.4
Introduction With recent advances in deep learning frameworks for image classification and object detection, the demand for standard image processing in Apache Spark has never been greater. Image handling and preprocessing have their specific challenges - for example, images come in different formats (eg., jpeg, png, etc.), sizes, and color schemes, and there is no...
