Data Mining and Machine Learning
Our AI platforms are offered though our partners. We support the end-to-end data mining and machine learning. Moreover with a comprehensive visual and programming interface.
Subsequently we empower analytics team members of all skill levels. Most importantly with a simple, powerful and automated way to handle all tasks in the analytics life cycle.
Automated insights & interpretability
Firstly automatically generate insights. Certainly including summary reports about the project. Get champion models and challenger models. Moreover., Simple language from embedded natural language generation. Certainly facilitates report interpretation and reduces the learning curve for business analysts.
Automated feature engineering & modeling
Secondly, you save time and improves analytics team productivity. Automated feature engineering selects the best set of features for modeling. For example, ranking them to indicate their importance in transforming your data. Visual pipelines are dynamically generated from your data, yet are editable to remain as a white box model.
Public API for automated modeling
Thirdly, this lets you take advantage of the public API for automated modeling. This is best for for end-to-end model development. Similarly deployment is simplified choosing the automation option. Use our API to build and deploy your own custom predictive modeling applications.
Provides best practices templates that enable a quick, consistent start to building models. This ensures consistency among the analytics team. Analytical capabilities include
- different types of regression
- random forest,
- gradient boosting models
- support vector machines,
- natural language processing
- topic detection, etc.
Deep learning with Python & ONNX support
In conclusion, enables Python users to access high-level APIs for deep learning functionalities within Jupyter notebooks. For instance via Deep Learning with Python (DLPy) open source package on GitHub. Certainly DLPy supports the Open Neural Network Exchange (ONNX) . And for easily moving models between frameworks.
Integrated data preparation, exploration & feature engineering
Lets data engineers quickly build and run transformations. certainly to augment data and join data within the integrated visual pipeline of activities using a drag-and-drop interface. Performs all actions in memory to maintain data structure consistency.
Highly scalable in-memory analytical processing
To sum up, enables concurrent access to data in memory in a secure, multiuser environment. Distributes data and analytical workload operations across nodes – in parallel – multithreaded on each node for very fast speeds.
Computer vision & biomedical imaging
In addition, this lets you acquire and analyze images with model deployment on server, edge or mobile. Similarly supports the end-to-end flow for analyzing biomedical images, including annotating images.
Code in your language of choice
Lets modelers and data scientists access capabilities from their preferred coding environment. For example – Python, R, Java or Lua – and add the power of our cloud p to other applications with REST APIs.
Cloud- & container-ready
Certainly, deploy in the cloud (private and public) or hybrid on-site and cloud. For example available as a predefined Docker container with recipes available on GitHub.
Machine Learning and Artificial Intelligence systems rarely make use of a single model. They often require structured and unstructured data as well as non-ML capabilities to function.
To sum up, yes its fascinating. Use the basic tool-kits and principles
- Prediction (regression/classification)
- Optimization (optimization/simulation/reinforcement learning)