This Article Is Based On The Microsoft Article 'What’s new with ML.NET Automated ML (AutoML) and tooling'. All Credit For This Research Goes To The Researchers of This Project 👏👏👏 Please Don't Forget To Join Our ML Subreddit
Right now, one of the fascinating subfields of Data Science is Automated Machine Learning (AutoML). It sounds fantastic for those unfamiliar with machine learning, but it concerns present Data Scientists. The media presentation of AutoML suggests that technology has the potential to drastically transform the way we produce models by removing the need for Data Scientists. In principle, utilizing AutoML to automate the process entirely is a brilliant idea, but it introduces several opportunities for bias and misunderstanding in practice.
Machine learning model training can be a time-consuming process. Automated Machine Learning (AutoML) makes identifying the best strategy for your circumstance and dataset easier. ML.NET is an open-source, cross-platform machine learning framework for .NET developers that allows custom machine learning to be integrated into .NET applications. Microsoft changed the AutoML implementation in its Model Builder and ML.NET CLI tools based on Microsoft Research’s Neural Network Intelligence (NNI) and Fast and Lightweight AutoML (FLAML) technology last year. These improvements provided various advantages and improvements over the previous system, including:
- Increased number of models under consideration
- Minimized rate of time-out errors
- Accurate performance metrics
NNI / FLAML AutoML implementations have been incorporated into the ML .NET framework, allowing use from a code-first perspective.
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