Qlik AutoML - Detailed Review

Analytics Tools

Qlik AutoML - Detailed Review Contents

Add a header to begin generating the table of contents

Qlik AutoML - Product Overview

Introduction to Qlik AutoML

Qlik AutoML is an AI-driven predictive analytics tool integrated into the Qlik Cloud platform, aimed at simplifying machine learning processes for businesses.

Primary Function

The primary function of Qlik AutoML is to enable users to create, deploy, and interpret machine learning models without the need for extensive coding or data science expertise. It helps users predict trends, identify key drivers in their data, and make informed decisions based on predictive insights.

Target Audience

Qlik AutoML is designed for analysts and business users of all skill levels, particularly those who need to make data-driven decisions but may not have a deep background in machine learning or data science.

Key Features

Streamlined Workflow

Qlik AutoML simplifies the machine learning process into six steps: Connect data, Select target, Generate model, Predict outcomes, Explore drivers, and Take action. This workflow ensures users can go from raw data to actionable insights quickly.

Automated Model Generation

The tool automatically creates and optimizes machine learning models based on the selected target variable. It determines the most suitable model category, such as binary classification, multiclass classification, or regression, depending on the type of the target.

Data Preparation

Qlik AutoML automatically applies preprocessing steps to ensure the data is clean and appropriate for model training. It also provides insights into the unique attributes of each data field, helping users make informed decisions about their model’s inputs.

What-if Scenarios

Users can conduct dynamic “what-if” analyses by altering feature values and observing how these changes affect predicted outcomes. This feature is particularly useful for exploring the impact of different factors on business objectives.

Explainability and Transparency

The tool provides full explainability of the predictions, allowing users to understand what might happen and why. This ensures that the decisions made are based on clear and trustworthy insights.

Integration and Collaboration

Qlik AutoML integrates seamlessly with the Qlik Cloud platform, enabling collaboration, experimentation, and deployment of machine learning models. It also supports integration with external platforms and third-party AI models.

By leveraging these features, Qlik AutoML empowers businesses to accelerate their data-driven decision-making processes and derive actionable insights from their data without requiring expert intervention.

Qlik AutoML - User Interface and Experience

User Interface of Qlik AutoML

The user interface of Qlik AutoML is crafted to be intuitive and user-friendly, making it accessible to users of all skill levels, whether they are data scientists, analysts, or business users.

Intuitive Interface

Qlik AutoML integrates seamlessly into the Qlik Cloud platform, leveraging Qlik’s intuitive user interface. This allows users to effortlessly build, deploy, and interpret machine learning models without the need for extensive technical expertise. The interface is designed with self-service in mind, enabling users to explore data, build visualizations, and engineer features on the fly.

Data Preparation and Model Building

The process of using Qlik AutoML begins with the selection of a target variable and the identification of relevant features. The system provides a comprehensive analysis of historical data, including summary statistics for each column, to help users make informed decisions about their model’s inputs. Qlik AutoML automatically applies preprocessing steps to ensure the data is clean and suitable for model training, eliminating much of the guesswork involved in data preparation.

Experimentation and Deployment

Users can configure experiments and deployments with ease. Qlik AutoML offers features like ‘experiments’ and ‘deployments,’ which allow users to test different algorithms and sets of features, track model performance, and deploy the best-performing models. This process is highly iterative and creative, with the ability to add new features, remove redundant ones, and develop new models quickly.

Real-Time Predictions and What-If Scenarios

One of the standout features of Qlik AutoML is its ability to generate real-time predictions. Users can conduct dynamic “what-if” analyses by altering feature values and instantly observing how these changes might affect predicted outcomes. This capability is particularly valuable for exploring the impact of different factors on business objectives and allows for interactive and intuitive decision-making.

Customization and Notifications

Users can customize their Qlik AutoML experience to fit their preferences. This includes customizing the activity center, adding or removing items from favorites and collections, and adjusting notification settings. Notifications can be set up for various events such as model training completion, deployment creation, and changes in prediction capabilities.

Speed and Versioning

Qlik AutoML is known for its speed in generating new models and optimizing existing ones. The platform allows for quick experimentation and deployment, which is crucial for iterative machine learning projects. Additionally, the versioning feature helps users keep track of model performance for different algorithms and sets of features, making it easier to manage and deploy new model versions.

Overall User Experience

The overall user experience with Qlik AutoML is streamlined and efficient. It fits seamlessly with existing BI workflows, reducing the barriers to predictive and prescriptive analytics. The platform enables users to deliver a high volume of projects quickly, making it beneficial for various departments within an organization. The integration with Qlik Sense allows for the development of predictive analytics-oriented applications that tell a story of future outcomes and enable interactive exploration by end-users.

Conclusion

In summary, Qlik AutoML’s user interface is designed to be user-friendly, intuitive, and highly interactive, making it an excellent tool for users across different skill levels to engage with machine learning and predictive analytics.

Qlik AutoML - Key Features and Functionality

Qlik AutoML Overview

Qlik AutoML is a powerful tool within the Qlik analytics suite that integrates AI to simplify and enhance predictive analytics. Here are the main features and how they work:

Connect Data

Qlik AutoML allows users to easily integrate various data sources to build a comprehensive dataset. This feature ensures that all relevant data is centralized and ready for analysis, making it simpler to prepare the data for machine learning processes.

Select Target

Users define their prediction goal with clarity and precision. This step involves specifying what they want to predict, such as sales forecasts, customer churn, or other business outcomes. This clear definition helps the AI system to focus on the specific problem at hand.

Generate Model

The AI in Qlik AutoML automatically creates and optimizes machine learning models based on the selected target. This process involves running several different algorithms, such as binary classification models, multi-class classification models, and regression models, to determine the best fit for the data. This automation eliminates the need for extensive data science expertise.

Predict Outcomes

Once the model is generated, Qlik AutoML applies it to the data to generate forward-looking predictions. These predictions can be made against datasets in various forms, including files or real-time data through REST APIs. This feature enables businesses to anticipate trends and make proactive decisions.

Explore Drivers

Users can gain deep insights into the factors influencing their predictions. Qlik AutoML provides prediction explanation scores, which help in understanding the drivers behind the predictions. This transparency is crucial for building trust in the AI models and making informed strategic decisions.

Take Action

Based on the predictive insights, users can make informed decisions and implement strategies. The what-if scenario modeling capability in Qlik Sense allows users to understand how changes in input data impact the model, enabling more accurate and proactive decision-making.

Model Explainability

Qlik’s latest AutoML enhancements emphasize full model explainability, ensuring that users have confidence in the reliability and performance of their AI models. This feature provides clear and actionable outcomes, explaining the predictions in real-time, which is essential for trust and transparency in AI-driven decision-making.

Real-Time API Access

Qlik AutoML offers real-time API access, allowing for up-to-date insights. This feature ensures that the models can be integrated into business operations seamlessly, providing the most current data for decision-making.

Associative Exploration and What-If Analysis

Users can perform associative exploration and what-if analysis for scenario planning. This capability helps in understanding how different scenarios might impact the business, enabling more informed and proactive decision-making.

Enhanced Security

The enhancements include enhanced security for data protection, ensuring that sensitive data is handled securely. This is crucial for maintaining the integrity and confidentiality of business data.

Multi-Language Support and AI Model Monitoring

Qlik AutoML supports multiple languages, ensuring global accessibility. Additionally, AI model monitoring and retraining capabilities keep the predictions accurate over time, ensuring the models remain reliable and relevant.

Conclusion

In summary, Qlik AutoML integrates AI to automate the machine learning process, from data preparation to model deployment, without requiring extensive coding or data science expertise. Its features are designed to enhance predictive analytics, ensure model explainability, and support proactive decision-making, all while maintaining data security and global accessibility.

Qlik AutoML - Performance and Accuracy

Evaluating the Performance and Accuracy of Qlik AutoML

Evaluating the performance and accuracy of Qlik AutoML involves examining its key features, capabilities, and any inherent limitations.

Performance Enhancements

Qlik AutoML has introduced several features to enhance performance and trust in machine learning models. Here are some notable improvements:

Intelligent Model Optimization: This feature automates parts of the model development process, applying pretrained best practices to improve model performance while reducing the effort required from developers and data scientists.
Native Machine Learning Analytics: This allows users to observe model performance through autogenerated dashboards, providing insights into what the models predict and the factors driving those predictions.
MLOps Capabilities: Automated monitoring for issues like model drift, model retraining, and lifecycle management helps maintain model accuracy and reliability. This ensures users can quickly identify when models start losing accuracy and retrain them as needed.

Accuracy and Trust

The updates to Qlik AutoML are focused on fostering trust in machine learning outputs. Here’s how they achieve this:

Visibility and Control: The new features provide greater visibility into model performance and offer easy model management, which directly fosters trust among users. Automated monitoring and autogenerated dashboards help users understand if models are performing at a trustworthy level or need adjustments.
Explainability: Qlik AutoML generates predictions with full explainability, allowing users to understand what might happen and why. This explainability is crucial for building trust in the model outputs.

Limitations

While Qlik AutoML offers significant capabilities, there are some limitations to consider:

Dataset Size: The maximum number of columns in a dataset is limited to 500, and the free tier allows datasets up to 50,000 cells. Exceeding these limits can restrict the functionality available.
API Rate Limit: There is an API rate limit of 300 requests per minute, which could impact high-volume usage scenarios.
Subscription Tiers: Advanced features such as hyperparameter optimization and real-time API are not included in the free tier and require a paid subscription. This can limit the automation and real-time prediction capabilities for users on the free tier.

Areas for Improvement

Users have highlighted a few areas where Qlik AutoML could be improved:

Communication and Documentation: There is a need for better communication and documentation when new products or features are released, especially regarding the limitations and what is accessible with or without a subscription.
Model Deployment and Sharing: Users have expressed a desire for better management and sharing of models between developers, which is currently limited.

Overall, Qlik AutoML has made significant strides in enhancing the performance and accuracy of machine learning models, but it is important to be aware of the existing limitations and areas where further improvement is needed.

Qlik AutoML - Pricing and Plans

The Pricing Structure of Qlik AutoML

The pricing structure of Qlik AutoML is segmented into different tiers, each with varying levels of features and capacities. Here’s a breakdown of what you can expect from each tier:

Included Tier

This tier is included with subscriptions to Qlik Sense Enterprise SaaS, Qlik Cloud Analytics Standard, Qlik Cloud Analytics Premium, Qlik Cloud Enterprise, or Qlik Talend Cloud (Standard, Premium, or Enterprise).
It offers basic AutoML functionality, suitable for trial purposes and evaluating business needs, but not for production use cases.
Limitations include:

Sequential model training (models are run one after another).
Maximum of 100,000 cells (e.g., 10,000 records with 10 columns).
No hyperparameter optimization (HPO), scheduled predictions, real-time API, or Qlik Sense connector integration.
Manual batch predictions are available, but other advanced features are not included.

Paid Tiers

These tiers are available as additional paid add-ons to a Qlik Cloud subscription.
They offer comprehensive AutoML capacities necessary for production use cases.
Key features include:

Concurrent model training (ability to train multiple models simultaneously).
Increased dataset size for training models.
Hyperparameter optimization for fine-tuning models.
Scheduled and real-time predictions.
Integration with the Qlik AutoML connector in Qlik Cloud Analytics.
API access and real-time API capabilities.

Subscription Metrics

The usage metrics are measured based on the use of AutoML services through the user interface, key driver analysis in Qlik Sense apps, and public APIs.
The number of deployed models, concurrent training capacity, and dataset size limits vary by tier.

Upgrading and Pricing

To exceed the limitations of the included tier, you need to upgrade to a higher-tier license. This involves contacting your Qlik Sales or account representative for specific pricing details, as the pricing depends on factors like the number of models to deploy, dataset size, API rate, and advanced features.

In summary, while the included tier provides a basic introduction to Qlik AutoML, the paid tiers offer the full range of features and capacities needed for serious and production-level machine learning tasks. For precise pricing and to discuss your specific needs, it is recommended to contact Qlik’s sales team directly.

Qlik AutoML - Integration and Compatibility

Integrating Qlik AutoML

Integrating Qlik AutoML with other tools and ensuring its compatibility across various platforms involves several key aspects.

Integration Methods

Qlik AutoML can be integrated with Qlik Sense and other data visualization platforms through various methods:

API Integration: Many AutoML platforms, including those compatible with Qlik, provide APIs that allow users to push data from Qlik Sense to the AutoML platform, train models, and then pull predictions or model results back into Qlik Sense.
Data Connectors: Some AutoML tools offer pre-built connectors or plugins for popular data visualization platforms like Qlik Sense. However, the free tier of Qlik AutoML does not include Qlik Sense connector integration.
Export/Import Data: Users can export data from Qlik Sense, build and evaluate models using an AutoML tool, and then import the results back into Qlik Sense for visualization and further analysis.
Custom Scripts: Users can use Qlik Sense’s scripting capabilities to interact with AutoML models, automating the data flow between Qlik Sense and the AutoML platform.

Compatibility with Other BI Tools

Qlik AutoML can be used in conjunction with other Business Intelligence (BI) tools, even if those tools do not have built-in AutoML capabilities:

Power BI, Tableau, and Other Tools: Users can leverage Qlik AutoML alongside other BI tools like Power BI or Tableau, especially for advanced analytics. For instance, users can develop models in Qlik AutoML and then integrate the results back into their primary BI tool for visualization and analysis.
R and Python: Models trained and deployed with Qlik AutoML can be called via APIs from programming languages like R or Python, allowing for seamless integration into various analytical workflows.

Limitations of the Free Tier

The free tier of Qlik AutoML, included with Qlik Cloud, has several limitations that affect its compatibility and usability:

Data Size: Limited to 100,000 cells (e.g., 10,000 records with 10 columns).
No HPO Features: No hyperparameter optimization features are available.
No Scheduled Predictions: No ability to schedule predictions.
No API Access: No access to APIs for integrating with other tools.
No Real-Time API: No real-time API capabilities.
No Qlik Sense Connector: No integration with Qlik Sense connectors in the free tier.

To overcome these limitations, users need to upgrade to a higher-tier license, which provides more extensive features and larger data handling capabilities.

Real-Time and Predictive Analytics

Qlik AutoML integrates well with Qlik Sense to provide real-time predictive analytics and what-if scenario planning. Users can develop predictive analytics-oriented applications that allow for interactive exploration and real-time scenario planning using Qlik Sense. This integration enables decision-makers to understand predictions and take immediate actions based on the insights generated.

Qlik AutoML - Customer Support and Resources

Support Options for Qlik AutoML Customers

For customers using Qlik AutoML, several support options and additional resources are available to ensure a smooth and effective experience.

Customer Support

Qlik provides a dedicated customer support system. If you are already a customer and need assistance, you can visit the Customer Support Portal, which offers a range of resources and help articles. For immediate support, you can fill out a contact form or call the sales representatives directly at 1 (833) 443-7545.

Documentation and Guides

Qlik offers comprehensive documentation and guides to help users get started with Qlik AutoML. The official Qlik Community page has a detailed guide on “How To Get Started with Qlik AutoML,” which includes information on system requirements, types of models supported, and software updates.

Connector and Integration Support

For users integrating Qlik AutoML with other Qlik products, such as Qlik Cloud, there is specific documentation available. The Qlik AutoML connector guide explains how to load data via an analytics connection, the required permissions, and any limitations, such as the 200k rows per request limit.

Workflow and Process Guidance

The Qlik AutoML workflow is outlined in a straightforward six-step process: Connect data, Select target, Generate model, Predict outcomes, Explore drivers, and Take action. This workflow is designed to be intuitive and helps users transition from raw data to actionable insights quickly.

Additional Resources

Qlik also provides access to various resources through their website, including product information, case studies, and testimonials from other users. These resources can offer insights into how other businesses are leveraging Qlik AutoML for predictive analytics and decision-making.

By leveraging these support options and resources, users of Qlik AutoML can ensure they are making the most out of the product and addressing any issues or questions they may have efficiently.

Qlik AutoML - Pros and Cons

Advantages of Qlik AutoML

Integration and Resource Utilization

Qlik AutoML seamlessly integrates with existing Qlik resources, allowing you to leverage an already connected and sanitized catalog of data. This integration enables your ML models to be used across different dashboards, automation, and ML projects, making resource utilization efficient.

Speed and Efficiency

Qlik AutoML accelerates the machine learning process by quickly generating new models, adding or removing features, and optimizing models in minutes. This speed does not bottleneck the critical stages of the project, such as serving predictions to users or systems.

Discovery and Exploration

The associative engine in Qlik AutoML facilitates deep data exploration. You can build visualizations, filter data, select multiple dimensions, and create measures on the fly, all within one platform. This feature is beneficial for both data scientists and analysts, enabling them to make sense of the data and predictions effectively.

Versioning and Model Performance

Qlik AutoML makes it easy to track model performance across different algorithms and feature sets. You can see the full story of how model performance is affected and deploy new model versions while removing old ones, all from the same screen.

Real-Time Predictions

The tool is capable of producing real-time predictions, which opens up new use cases such as optimizing industrial processes in real-time. It also supports “what-if” scenarios, allowing end users to interact dynamically with the data and predictions.

Model Explainability and Trust

Qlik’s latest AutoML enhancements focus on model explainability, providing users with clear insights into how the AI models are performing. This ensures trust and transparency in AI-driven decision-making, leading to more informed and confident strategic decisions.

User-Friendly Interface

Qlik AutoML offers a no-code interface, making it accessible to users of all skill levels. It simplifies the creation, configuration, and deployment of machine learning models, allowing businesses to accelerate their data-driven decision-making processes.

Disadvantages of Qlik AutoML

While the available resources highlight several advantages, there are some potential drawbacks to consider:

Cost

Although Qlik Sense, the broader platform, is relatively affordable, the per-user price can make it more expensive for larger organizations.

Learning Curve for Advanced Features

While Qlik AutoML is designed to be user-friendly, some advanced features and fine-tuning options may still require a certain level of technical expertise to fully utilize. This could be a barrier for users who are not familiar with machine learning concepts.

Dependence on Qlik Ecosystem

The full benefits of Qlik AutoML are best realized within the Qlik ecosystem. Users who are not already invested in Qlik products might find it less advantageous compared to those who are already using Qlik’s analytics platform.

In summary, Qlik AutoML offers significant advantages in terms of integration, speed, data exploration, and real-time predictions, making it a powerful tool for predictive analytics. However, it may have some cost implications and a learning curve for advanced features, particularly for those outside the Qlik ecosystem.

Qlik AutoML - Comparison with Competitors

When Comparing Qlik AutoML with Other AI-Driven Analytics Tools

Several unique features and potential alternatives stand out.

Qlik AutoML Unique Features

Streamlined Workflow: Qlik AutoML simplifies the machine learning process into six straightforward steps: connecting data, selecting a target, generating models, predicting outcomes, exploring drivers, and taking action. This workflow is intuitive and does not require coding.
Automated Model Optimization: Qlik AutoML automatically handles feature engineering, correlation analysis, target leakage checks, and outlier handling, ensuring faster and better models without the need for deep data science expertise.
Embedded Analytics: It integrates tightly with Qlik Sense, allowing users to compare models side-by-side, analyze predictions using SHAP values, and visualize results within existing dashboards.
MLOps Enhancements: Qlik AutoML offers a fully serverless lifecycle for training, deployment, and monitoring models, along with granular access controls and automated retraining to maintain model reliability.

Competitors and Alternatives

Tableau

Tableau is known for its user-friendly interface and AI-powered recommendations. It uses natural language processing (NLP) through features like Ask Data and Explain Data, enabling users to query data in plain English and receive automated explanations of data trends. Tableau also offers predictive modeling to forecast future trends, but it may not have the same level of automated model optimization as Qlik AutoML.

Google Analytics

Google Analytics uses machine learning to identify patterns and trends in website traffic and user behavior. It predicts future user actions like potential purchases or churn, but it is more focused on web analytics rather than the broad predictive analytics capabilities of Qlik AutoML. Google Analytics is particularly useful for marketers looking to optimize their web presence.

Microsoft Power BI

Microsoft Power BI is a cloud-based BI platform that integrates with Microsoft Azure for advanced analytics and machine learning. It offers interactive visualizations and data modeling, but its AI capabilities, while strong, are more generalized and may not match the specific automated machine learning features of Qlik AutoML. Power BI is a good choice for organizations already invested in Microsoft products.

Salesforce Einstein Analytics

Salesforce Einstein Analytics uses machine learning to analyze customer data, predict sales outcomes, and personalize marketing campaigns. While it provides deep insights into customer behavior, it is more specialized towards CRM and sales analytics compared to the broader predictive analytics capabilities of Qlik AutoML.

SAS Visual Analytics

SAS Visual Analytics uses AI to automate data analysis and provide insights, including predictive models for forecasting customer behavior or sales trends. It is strong in uncovering hidden patterns and trends but may require more technical expertise compared to Qlik AutoML’s code-free approach.

Conclusion

Qlik AutoML stands out for its streamlined workflow, automated model optimization, and tight integration with Qlik Sense. While other tools like Tableau, Google Analytics, Microsoft Power BI, Salesforce Einstein Analytics, and SAS Visual Analytics offer powerful AI-driven analytics, they each have their own strengths and focus areas. Depending on your specific needs, such as web analytics (Google Analytics), CRM-focused analytics (Salesforce Einstein Analytics), or user-friendly data visualization (Tableau), one of these alternatives might be more suitable. However, for a comprehensive and automated predictive analytics solution that does not require deep data science expertise, Qlik AutoML is a strong contender.

Qlik AutoML - Frequently Asked Questions

What is Qlik AutoML and how does it work?

Qlik AutoML is an AI-powered predictive analytics tool that simplifies the machine learning process. It guides users through six straightforward steps: connecting data, selecting a target, generating a model, predicting outcomes, exploring drivers, and taking action. This workflow transforms raw data into actionable insights without requiring coding.

What kind of data is suitable for Qlik AutoML?

For Qlik AutoML, you need data that includes an outcome you care about and a set of attributes (features) that existed prior to that outcome. Historical data typically used for analytics and reporting is not suitable because it is captured at the same point in time. The data should be prepared with the necessary ingredients to train machine learning models, such as dimensions, measures, and time periods.

What are the new features in the recent Qlik AutoML update?

The recent update includes four new features: intelligent model optimization, native machine learning analytics, comprehensive machine learning operations (MLOps) capabilities, and full integration with Qlik Cloud. These features simplify model development, provide visibility into model performance, ensure model accuracy, and offer easy access to data infrastructures.

How does Qlik AutoML ensure trust in machine learning outputs?

Qlik AutoML ensures trust through several features. It includes automated monitoring for issues like model drift, model retraining, and lifecycle management. The tool also provides autogenerated dashboards that display model performance details, allowing users to understand if models are performing reliably. These features foster greater visibility, control, and easy model management.

Can users without coding skills use Qlik AutoML?

Yes, Qlik AutoML is designed to be user-friendly and does not require coding skills. It empowers analysts to make proactive decisions by automating the machine learning process, making it accessible to users of all skill levels.

How does Qlik AutoML integrate with other Qlik tools?

Qlik AutoML is fully integrated with Qlik Cloud, providing users with easy access to their data infrastructures. This integration simplifies the user experience and enables efficient data-informed decisions. Additionally, it can be used in conjunction with other Qlik analytics tools to enhance overall analytics capabilities.

What kind of predictive analytics can be conducted with Qlik AutoML?

With Qlik AutoML, you can conduct predictive analytics and explore “what-if” scenarios. The prediction API allows for real-time predictive analytics integration, enabling you to simulate various scenarios and instantly observe how changes might affect predicted outcomes. This is particularly useful for assessing the impact of different factors on business objectives.

How is the pricing structured for Qlik AutoML?

Qlik AutoML is part of the Qlik Cloud Analytics plans. The pricing is capacity-based, meaning it is a fixed fee based on an agreed capacity limit, providing predictable costs. The Standard plan starts at $825/month (billed annually) for 25 GB of data and 20 full users, while the Premium plan starts at $2,500/month (billed annually) for 50 GB of data and additional features like predictive analytics with automated machine learning.

What kind of support does Qlik offer for AutoML users?

Qlik offers 24×7 critical customer support for its users. Additionally, the Premium plan includes guided customer success onboarding to help users get started effectively.

Can Qlik AutoML handle large datasets and multiple data sources?

Yes, Qlik AutoML can handle large datasets and multiple data sources. It allows you to connect and combine hundreds of data sources, including cloud, SAP, mainframe, and legacy systems. This capability is particularly useful for enterprises that need to analyze extensive and diverse data sets.

How does Qlik AutoML manage model lifecycle and accuracy?

Qlik AutoML includes comprehensive MLOps capabilities to manage model lifecycle and accuracy. It features automated monitoring for model drift, model retraining, and lifecycle management to ensure that models remain reliable and accurate over time.

Qlik AutoML - Conclusion and Recommendation

Final Assessment of Qlik AutoML

Qlik AutoML stands out as a powerful tool in the analytics tools AI-driven product category, offering a range of benefits that make it an attractive option for various users.

Key Benefits

Enhanced Predictive Analytics

Qlik AutoML automates the creation of predictive models, enabling users to generate forecasts and predictions without requiring deep expertise in machine learning. This is particularly useful for business decision-making, allowing teams to move from reactive to proactive strategies.

User-Friendliness

The platform is designed for straightforward deployment of machine learning models within production environments. It uses a no-code approach, making it accessible to business analysts and analytics teams who may not have specialized skills in machine learning.

Improved Accuracy

AutoML employs sophisticated techniques to optimize models, which can improve accuracy and performance compared to manually developed models. This ensures that the insights generated are reliable and trustworthy.

Full Model Explainability

Qlik’s latest enhancements include full model explainability, providing users with clear insights into the drivers behind the predictions. This transparency is crucial for building trust in AI-driven decision-making.

Seamless Integration

Qlik AutoML integrates efficiently with Qlik Sense, allowing users to deploy models directly within the environment. This streamlines workflows and enhances data analysis processes. It also connects seamlessly to a wide range of data sources through the Qlik Cloud Catalog.

Speed and Efficiency

The platform enables quick generation of new models, allowing for iterative and creative machine learning projects. Users can add new features, remove redundant ones, and develop new models in minutes, which is essential for fast-paced business environments.

Real-Time Predictions

Qlik AutoML supports real-time predictions, enabling use cases such as optimizing industrial processes in real-time. It also allows for “what-if” scenario modeling, which can be highly intuitive and engaging for end-users.

Who Would Benefit Most

Analytics Teams

Teams involved in predictive analytics will greatly benefit from Qlik AutoML. It simplifies the process of building and deploying high-performing machine learning models, providing native analytics to explain predictions in real-time.

Business Analysts

Analysts without specialized machine learning skills can use Qlik AutoML to leverage AI-driven insights. The no-code interface makes it accessible and user-friendly.

Organizations with Existing Qlik Infrastructure

Companies already using Qlik Sense can leverage Qlik AutoML to enhance their analytics capabilities. The integration with existing resources and data catalogs makes it a seamless addition to their current workflows.

Overall Recommendation

Qlik AutoML is a highly recommended tool for organizations looking to enhance their predictive analytics capabilities without the need for extensive machine learning expertise. Its user-friendly interface, improved accuracy, and full model explainability make it an excellent choice for analytics teams and business analysts. The seamless integration with Qlik Sense and support for real-time predictions further add to its value, making it a valuable asset for any organization aiming to make proactive, data-driven decisions.