Qlik AutoML Reviews

Qlik AutoML is a product offered by Qlik that provides no-code, automated machine learning to analytics teams. It allows users to create machine learning experiments, identify key drivers in their data, and train models to predict potential outcomes.

With Qlik AutoML, users can generate models easily, make predictions, and test scenarios without the risk of failure or the need for excessive effort like coding. It also allows users to publish their experiments quickly to Qlik Cloud Analytics for interactive exploration, real-time models, and what-if analyses.

The tool works by auto-generating machine learning models. Users simply select a target field in their dataset and let Qlik AutoML do the work. It determines key drivers in the historical data, auto-generates predictive models with unlimited tuning and refinement, and selects and deploys the best-performing models based on scoring and ranking.

Qlik AutoML also provides explainability data (SHAP values) at the record level, so users know why predictions were made and how to best influence outcomes. Users can explore predictive data and test what-if scenarios using Qlik's Associative Engine, which provides real-time, instant calculations to explore. Users can make interactive selections and shift parameters in what-if scenarios, then evaluate the results to get a feel for what actions will lead to the best outcome.

Qlik AutoML supports a variety of use cases across different domains such as sales, marketing, finance, operations, HR, IT, supply chain, service, and support. It also easily connects with top AI and machine learning tools like Open AI, Amazon Bedrock, Azure ML, or Databricks ML.

In conclusion, Qlik AutoML is a powerful tool that brings the power of machine learning to analytics teams without the need for coding, making it easier to leverage data for predictive analytics.

Pros of Qlik AutoML:

• No-code, Automated Machine Learning: Allows analytics teams to create machine learning experiments, identify key drivers in data, and train models without the need for coding.
• Auto-generate Machine Learning Models: Simply select a target field in the dataset and let Qlik AutoML generate predictive models with unlimited tuning and refinement.
• Predict Potential Outcomes: Load data to automatically generate likely outcomes based on the predictive model. It provides explainability data (SHAP values) at the record level for understanding why predictions were made and how to best influence outcomes.
• Explore Predictive Data and Test What-if Scenarios: Use Qlik's Associative Engine for real-time, instant calculations to explore. Make interactive selections and shift parameters in what-if scenarios, then evaluate the results to understand what actions will lead to the best outcome.
• AI and Machine Learning Connectors: Easily connect with top AI and machine learning tools like Open AI, Amazon Bedrock, Azure ML, or Databricks ML.

Cons of Qlik AutoML:

• Limited Customization: While the no-code approach is beneficial for non-technical users, it may limit the level of customization and control that more experienced data scientists and developers might prefer.
• Dependent on Quality of Data: The effectiveness of the machine learning models generated by Qlik AutoML is highly dependent on the quality of the input data. Garbage in, garbage out.
• Learning Curve: Despite its user-friendly interface, there can be a learning curve associated with understanding how to best use the platform and interpret the results it produces.
• Cost: Depending on the pricing plan, the cost of Qlik AutoML may be a barrier for some organizations, particularly small businesses or startups with limited budgets.