Data scientists and developers know the power of Python and Python's wide-spread adoption is a testament to its success. Now, Python users can extend this power when analyzing data in Oracle Autonomous Database. Oracle Machine Learning for Python (OML4Py) makes the open source Python scripting language and environment ready for the enterprise and big data.
Designed for problems involving both large and small data volumes, Oracle Machine Learning for Python integrates Python with Oracle Autonomous Database, allowing users to run Python commands and scripts for data exploration, statistical analysis, and machine learning on database tables and views using Python syntax. Familiar Python functions are overloaded to translate Python functionality into SQL for in-database processing - achieving performance and scalability - transparently.
Python users can take advantage of parallelized in-database algorithms to enable scalable model building and data scoring - eliminating costly data movement. Further, Python users can develop and deploy user-defined Python functions that leverage the parallelism and scalability of Autonomous Database, and deploy those same user-defined Python functions using environment-managed Python engines through a REST API.
Oracle Machine Learning for Python also introduces automated machine learning (AutoML), which consists of: automated algorithm selection to select the algorithm most appropriate for the provided data, automated feature selection to enhance model accuracy and performance, and automated model tuning to improve model quality. AutoML enhances data scientist productivity by automating repetitive and time-consuming tasks, while also enabling non-experts to produce models without needing detailed algorithm-specific knowledge.
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