Everyone works in the same environment.
Firstly, easily solve complex problems with insights.
Visual Data Mining and Machine Learning automatically generates insights. This enables you to identify the most common variables across all models.
Above all natural language generation capabilities are used to create a project summary written in simple language, enabling you to easily interpret reports.
Likewise, similarly, in the same vein Analytics team members can add project notes to the insights.
Secondly, empower users.
Visual Data Mining and Machine Learning lets you embed open source code within an analysis, and call open source algorithms seamlessly within a Model Studio flow. For example this facilitates collaboration across your organization. Users can program in their language of choice.
In conclusion, you can also take advantage of Cloud First Deep Learning with Python (DLPy), our open source package on GitHub, to use Python within Jupyter notebooks to access high-level APIs.
This makes sense for deep learning functionalities. Examples including computer vision, natural language processing, forecasting and speech processing. DLPy supports the Open Neural Network Exchange (ONNX) for easily moving models between frameworks.
Thirdly, find the optimal solution.
Above all get superior performance from massive parallel processing.
Finally compare multiple approaches rapidly.
This allows you to quickly and easily find the best parameter settings for diverse machine learning algorithms – including decision trees, random forests, gradient boosting, neural networks, support vector machines and factorization machines. Just simply by selecting the option you want.
In conclusion complex local search optimization routines work hard in the background to efficiently and effectively tune your models.
You can also combine unstructured and structured data in integrated machine learning programs for more valuable insights from new data types. And reproducibility in every stage of the analytics life cycle delivers answers and insights you can trust.
Fourthly Boost productivity.
Data scientists, business analysts and other analytics professionals get accurate results from a single, collaborative environment that supports the entire machine learning pipeline. So a variety of users can access and prepare data and perform analysis.
You will need to build and compare machine learning models. Create score code for predictive models and finally execute one-click model deployment.
To sum up…do it all faster than ever before with our automated modeling API.
Fifthly, Reduce decision time.
To enhance collaborative understanding, the solution provides all users with business-friendly annotations. Within each node describing what methods are being run, as well as information about the methods, results and interpretation.
All reports are available in all modeling nodes. Including LIME, ICE, Kernel SHAP, PD heatmaps, etc., with explanations in simple language.