From creation to deployment and operationalization, the DataRobot automated machine learning platform delivers an end-to-end solution for predictive modelling. DataRobot provides five approaches for dealing with model deployment, allowing businesses to swiftly deploy the winning model. It gives you the tools and visualizations you need to investigate and evaluate different models. The built-in leaderboard makes it simple to see which models are the most effective with data.
To know the DataRobot software better, the users can have a look at the website and go through the various features offered about it. For more information and a demo, the users can drop a request at the site and get across the demo soon https://www.datarobot.com/request-a-demo/
DataRobot Automated Machine Learning platform
MLDev & MLOps for 5 users (user limit) + 10 model workers + 5 models deployed is priced at $65,000 for 12 months
Innovative open source algorithms - DataRobot uses the latest and most powerful open-source machine learning libraries, including R, Python, sci-kit-learn, H2O, TensorFlow, Vowpal Wabbit, Spark ML, and XGBoost.
Automated feature engineering - DataRobot prepares data automatically, performing operations like one-hot encoding, missing value imputation, text mining, and standardization to transform features for optimal results.
Time-aware forecasting - DataRobot can automate the development of sophisticated time series models that predict the future values of a data series based on its history and trend. The platform automatically detects stationary and seasonality and implements backtesting to achieve the highest possible accuracy.
Multiclass model support - DataRobot permits classification on targets with up to 100 distinct values, offering real-time and batch support for uncovering the predictive class and showing its probability across all classes.
Built-in guardrails - With DataRobot, modeling projects follow a consistent methodology based on data science best practices. Novice users can’t “forget” to perform a critical step, such as model validation.
Advanced machine learning techniques - DataRobot incorporates the techniques advanced data scientists use: boosting, bagging, random forests, kernel-based methods, generalized linear models, deep learning, and many others.
Unsupervised anomaly detection - Uncover anomalies in a dataset with DataRobot’s unsupervised ensemble blend model, which can offer new insights, even in familiar datasets.
Manual tuning capabilities - DataRobot automates model tuning but also supports manual tuning that can tune and adjust machine learning algorithms for even better results.
Monotonicity constraints - Apply a forced directional relationship between features and the target based on business knowledge or industry requirements.