Machine Learning
made easy
Harness the power of machine learning without the need for extensive coding or technical expertise. Streamline the entire machine learning lifecycle, from data preparation and feature engineering to model training, validation, and deployment.
Streamline the entire machine learning workflow
Democratise machine learning with consistent, efficient, and reproducible processes. Unlock your data’s true potential with no-code feature engineering
Save Time
Accelerate Model Development, Parallel Training, Validation, and Evaluation Across Multiple Configurations.
Collaborate
Seamlessly work together on projects. Project sharing and access control. Concurrent editing.
Feature Engineering
THE PRODUCT offers a comprehensive suite of tools and functionalities to streamline the feature engineering process, empowering users to extract valuable insights from their data without the need for extensive coding or domain expertise.
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Importance Analysis
Understanding the relative importance of features is crucial for building accurate and interpretable models. THE PRODUCT provides comprehensive feature importance analysis, allowing users to visualize and rank the significance of each feature in relation to the target variable.
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Feature Selection
With THE PRODUCT interface, you can easily select the most relevant features for their machine learning models. The platform employs state-of-the-art feature selection algorithms, such as recursive feature elimination, correlation-based selection, and regularization techniques, to identify the most informative features while reducing dimensionality and preventing overfitting.
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Feature Interactions and
Transformations
THE PRODUCT supports the creation of interaction terms and non-linear transformations of features, enabling users to capture complex relationships and patterns in their data. This includes techniques such as polynomial features, logarithmic transformations, and feature crosses, all accessible through our user-friendly interface. You can also write your custom transformation or filtering formulas.
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Automatic Feature Generation
THE PRODUCT leverages advanced algorithms and techniques to automatically generate relevant features from your input data. This includes techniques such as one-hot encoding for categorical variables, scaling and normalization for numerical features, and the creation of derived features through mathematical operations or domain-specific transformations.
Model Development
THE PRODUCT offers a comprehensive suite of state-of-the-art machine learning algorithms and models, catering to a wide range of problem domains and data complexities.
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Gradient Boosting Algorithms
Gradient Boosting: The platform includes powerful gradient boosting algorithms, which have proven to be highly effective for a variety of tasks, including classification, regression, and ranking problems.
Extreme Gradient Boosting (XGBoost): XGBoost, one of the most popular and efficient implementations of gradient boosting, known for its speed, performance, and ability to handle sparse data.
Light Gradient Boosting Machine (LightGBM): A highly efficient and scalable gradient boosting framework that excels in handling large-scale data and high-dimensional features.
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Decision Tree-Based Models
Decision Trees: Access to robust decision tree algorithms, which are versatile and interpretable models suitable for both classification and regression tasks.
Random Forests: The platform offers random forest models, which combine multiple decision trees to improve predictive accuracy and robustness, while also providing feature importance insights.
Model Validation
THE PRODUCT provides robust model validation capabilities to ensure the reliability and generalizability of the models you build. The platform offers a comprehensive set of techniques for model validation, allowing you to evaluate your models thoroughly and make informed decisions about their deployment.
- Cross-validation is a powerful technique for estimating the performance of a model on unseen data.
- Hyperparameter Tuning is essential for optimizing model performance.
Model Evaluation
THE PRODUCT supports a wide range of evaluation metrics tailored to different problem types, such as classification (e.g., accuracy, precision, recall, F1-score, AUC-ROC), regression (e.g., mean squared error, mean absolute error, R-squared), and ranking (e.g., mean average precision, normalized discounted cumulative gain).
- You can select multiple evaluation metrics to comprehensively assess your model's performance from various perspectives.
- Visualizations and reports facilitate the interpretation and comparison of evaluation metrics across different models and validation techniques.
Model Deployment
THE PRODUCT offers seamless integration with AWS Lambda, enabling you to deploy your trained models as serverless functions with ease. Deploying models as AWS Lambda functions provides several advantages, including scalability, cost-effectiveness, and streamlined maintenance. Here's how the platform simplifies the process of deploying models as AWS Lambda functions:
Once you've trained and validated your machine learning model using our platform, the intuitive interface allows you to package the model and its dependencies for deployment. The platform automatically handles the complexities of serializing the model, including any necessary data preprocessing steps, feature engineering transformations, and model artifacts.
Our platform is tightly integrated with AWS Lambda, allowing you to deploy your packaged model directly to the AWS Lambda environment. With just a few clicks, you can specify the AWS credentials, Lambda function configuration (e.g., memory allocation, timeout settings), and any additional dependencies required by your model.
By deploying your model as an AWS Lambda function, you benefit from the serverless architecture, which automatically scales your model's compute resources based on incoming traffic. This means your model can handle variable workloads without the need for manual provisioning or scaling, ensuring optimal performance and cost-efficiency.
The product provides an API key for http requests that will trigger your deployed model for inference. This seamless integration enables you to build event-driven applications that leverage your machine learning models in real-time.
Whenever you retrain or update your machine learning model within our platform, you can easily redeploy the updated version to your existing AWS Lambda function. Our platform streamlines the process of updating your deployed models, minimizing downtime and ensuring your applications always leverage the latest and most accurate models.
Collaboration
THE PRODUCT is designed to foster seamless collaboration among teams, enabling efficient teamwork and knowledge sharing throughout the machine learning lifecycle.
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Project Sharing
Projects can be easily shared with team members, allowing multiple users to work on the same machine learning task simultaneously. Access controls ensure secure collaboration by granting appropriate permissions (read, write, or admin) to each user.
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Concurrent Editing
The platform supports concurrent editing, enabling team members to work on the same project components (datasets, models, workflows) simultaneously. Changes are synchronised instantly, ensuring everyone is working with the latest version.
Streamline the entire machine learning lifecycle, from data preparation and feature engineering to model training, validation, and deployment.