| Platform | Description |
|---|---|
| Qlik Sense Cloud | 🕒 Qlik Sense integrates with Qlik’s Associative Engine, which includes built-in machine learning capabilities for data exploration and analysis.
🕒 Qlik's Cognitive Engine allows users to perform advanced analytics, including predictive analytics and anomaly detection, directly within Qlik Sense. 🕒 Qlik Sense Cloud supports AutoML and the integration of custom machine learning models developed using third-party tools or platforms. |
| Tableau Online | 🕕 Tableau Online offers machine learning integrations through its Extensions API, allowing users to incorporate custom ML models and algorithms into Tableau dashboards.
🕕 Tableau's integration with external ML platforms, such as Python and R, enables users to leverage advanced analytics capabilities directly within Tableau. |
| Looker | 🕘 Looker provides integration with external ML platforms and libraries, such as TensorFlow and scikit-learn, allowing users to perform predictive analytics and other ML tasks.
🕘 Looker's data modeling layer enables the incorporation of custom ML models and algorithms for advanced analytics and insights. |
| Sisense Cloud | 🕛 Sisense integrates with external ML platforms and services, such as Amazon SageMaker and Google Cloud AI, to enable predictive analytics, anomaly detection, and other ML-driven capabilities.
🕛 Sisense's ML capabilities include automated anomaly detection, trend analysis, and forecasting to uncover actionable insights from data. |
| Microsoft Power BI | 🕖 Power BI offers built-in machine learning capabilities through its AI-powered features, such as Quick Insights, AutoML, and AI visuals.
🕖 AutoML enables users to build machine learning models to solve predictive analytics problems, such as forecasting and classification, directly within Power BI. 🕖 Power BI also supports custom ML models through integration with Azure Machine Learning. |
These are some examples of cloud-based BI platforms that offer machine learning capabilities. It's essential to evaluate each platform's ML features based on your specific requirements, such as the types of ML tasks supported, integration options, ease of use, and scalability, to determine the best fit for your organization's needs. Additionally, consider factors like data security, compliance, and support when selecting a BI platform with machine learning capabilities.
🔗 Qlik Sense SaaS | 🔗 Tableau Online | 🔗 Looker | 🔗 Sisense Cloud | 🔗 Microsoft Power BI