Azure Machine Learning Services is an integrated, end-to-end data science and advanced analytics solution for professional data scientists to prepare data, develop experiments, and deploy models at cloud scale.
Microsoft recently announced updates to Azure Machine Learning Services, with the aim of boosting productivity and bringing data science to all.
During this session we will discuss these new capabilities, including:
- Automated Machine Learning User Interface: Enables business domain experts to train machine learning models on data without writing any code.
- Azure Machine Learning Visual Interface: Powerful drag and drop workflow capability that simplifies the process of building, training, and deploying machine learning models.
- Hosted notebooks in Azure Machine Learning: New notebook based authoring directly integrated into Azure Machine Learning.
- Feature Engineering Updates: Improvements in sweeping different combinations of algorithms for algorithm selections and hyperparameters and the addition of new algorithm’s such as XGBoost. Forecasting with automated machine learning also now includes capabilities that improve the accuracy and performance of recommended models with time series data.
We will also walk through some examples of the above new features, to demonstrate how these features help accelerate the machine learning lifecycle.