Machine Learning has energised software applications with highly accurate predictions thereby upsurging the product demand of tech driven companies. However, while developing such smart applications, numerous machine learning challenges and software development issues are been faced by data scientist and machine learning professionals.
Today, Databricks open sources their newly developed framework MLflow, with an aim to simplify their complex machine learning experiments with smart automation and numerous accessibility in deploying your machine learning models across any platform.
With MLflow, Machine Learning users can simply standardize their complex processes while building and deploying their machine learning and predictive models. With this framework, data scientists are fueled with lots of automation accessibility through which they can track experiments, package their machine learning codes and manage their models on any of the popular machine learning frameworks.
The current platform offers following three components:
- MLflow Tracking: This component allows you to log codes, data files, config and results. It also allows to query your experiments through which you visualize and compare your experiments and parameters swiftly without much hassle.
- MLflow Projects: It provides structured format for packaging machine learning codes along with useful API and CLI tools.This allows data scientists to reuse and reproduce their codes and easily chain their projects and workflows together.
- MLflow Models: It is a standard format for packaging and distributing machine learning models across different downstream tools. Azure ML compatible models, Deploying with Amazon Sagemaker or deploying on a local REST API are some of the examples of distributing models.
The current version is just an Alpha release and more features would be added to its full release. To get more details on its core offerings, APIs and command-line interfaces, read the official documentation at mlflow.org.
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