Businesses of the 21st century are now harnessing the tools and techniques of data scientists at a rapid pace to accomplish their goals. Advancements in the tools and techniques used by practitioners are heavily impacted by data scientists ability to use them. More often than not, these tools require a significant amount of know-how, as well as time to adequately identify a solution space for an arbitrary problem. Know-how and well-known solution spaces can lead to patterns being created to solve problems identified within said solution space. Patterns used by data scientists typically require manual effort to clean, curate, partition, purge and analyze data, before modeling or deployment can occur. Other data science responsibilities, such as feature engineering, require time investment, as data will not always come in a mature state ready for ingestion. Model selection and hyper-parameter often lead to frustration before they lead to results.
Alleviating these issues is AutoAI, which ingests tabular data in .csv format and returns a trained, deployable pipeline that includes feature engineering, model selection, and hyper-parameter optimization based on your optimization criteria! AutoAI reduces the overall time involved to take data and begin making predictions with it. AutoAI can be used for rapid-prototyping, as it frees up data scientists from having to spend time finding correct feature transforms, model selection, and hyper-parameter selection, to name a few. The model procured can also be exported as a notebook to fine-tune even further. Ultimately, AutoAI can accelerate data science workflows, making their work predictive while they are being productive elsewhere! Check out the link below to get started!
Build machine learning models with and without AutoML: https://developer.ibm.com/technologies/artificial-intelligence/articles/compare-model-building-with-and-without-automated-machine-learning/#Featured-area-2#Featured-area-2-home