Qlik AutoML · 0 reviews

Qlik AutoML

Democratize machine learning and predictive analytics without data science expertise

Data Science and Machine Learning Platforms

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About Qlik AutoML

Qlik AutoML is an enterprise-grade machine learning platform that democratizes predictive analytics by eliminating the need for advanced data science expertise. The platform automatically builds, trains, and deploys AI-generated predictive models directly within existing analytics workflows, enabling business analysts and operational teams to uncover actionable insights and make data-driven decisions with confidence and speed. Qlik AutoML integrates seamlessly with Qlik's analytics ecosystem and third-party data sources, providing automated feature engineering, model selection, and performance optimization. Through AiDOOS marketplace integration, organizations gain enhanced deployment flexibility, streamlined governance frameworks, accelerated model operationalization, and optimized scalability across hybrid cloud and on-premise environments. The platform reduces time-to-insight from weeks to days, democratizes ML adoption across business units, and ensures consistent model performance monitoring and governance.

Challenges It Solves

Organizations lack data science resources to build predictive models for business decisions
Manual feature engineering and model selection consume excessive time and resources
Business teams struggle to translate complex ML outputs into actionable business insights
Models lack proper governance, monitoring, and compliance frameworks in production

Faster time-to-value for predictive analytics projects

Reduced dependency on specialized data science teams

Improved decision-making accuracy across business operations

Use Cases

Sales Forecasting

Predict future sales trends and customer demand to optimize inventory and resource allocation. Enable sales teams to identify high-value opportunities and prioritize accounts.

68% Improved forecast accuracy and sales pipeline management

Customer Churn Prediction

Identify at-risk customers before they leave and trigger proactive retention campaigns. Optimize marketing spend by targeting customers most likely to convert.

55% Reduced customer attrition and improved retention rates

Risk & Fraud Detection

Detect unusual patterns and anomalies in transactions to identify fraud or operational risks. Enable compliance teams to respond to threats in real-time.

79% Enhanced security posture and fraud prevention capability

Operational Efficiency

Predict equipment failures, maintenance needs, and operational bottlenecks. Optimize resource allocation and reduce downtime through preventive actions.

42% Lower operational costs and improved asset utilization

Customer Segmentation

Automatically segment customers based on behavioral and demographic patterns. Enable targeted marketing and personalized customer experiences at scale.

61% Increased campaign relevance and customer lifetime value

Pricing

Pricing available on request

Qlik AutoML pricing is customized based on your team size, integrations, and requirements. AiDOOS will get you a scoped proposal — for free.

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Key Features

Automated Model Creation

Build production-ready models without coding

Deploy predictive models in hours instead of weeks

Intelligent Feature Engineering

Automatic data preparation and feature optimization

Reduces manual data engineering work by 80%

Explainable AI

Understand model predictions and business impact

Increase stakeholder trust and model adoption rates

Model Performance Monitoring

Real-time model health tracking and alerts

Detect and address model drift automatically

Seamless Integration

Works within existing Qlik analytics environment

Embed predictions directly into business dashboards

Automated Retraining

Keep models current with fresh data

Maintain model accuracy without manual intervention

Reviews

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Enterprise Readiness

Role-Based Access Control (RBAC)

Data Encryption

Model Governance Framework

Privacy Protection

Compliance & Audit Logging

Integrations

8 total apps

Native integration enables embedding predictive models directly into interactive dashboards and analytics applications

Seamless connectivity allows existing QlikView users to augment analytics with automated machine learning capabilities

Integrate customer predictions and churn models to enhance CRM workflows and sales team decision-making

Direct data source connectivity for building models on enterprise data warehouses and transactional databases

Cloud data warehouse integration for scalable model training and deployment on large datasets

Native support for BigQuery datasets enabling rapid model development on cloud-native data platforms

Leverage distributed computing for processing large-scale data and training complex machine learning models

Expose trained models via REST endpoints for integration into third-party applications and systems

AiDOOS Managed Deployment

Deploy Qlik AutoML in

AiDOOS handles setup, CRM integration, SSO config, and user provisioning. Your team goes live — not your IT department.

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Deployments

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Adoption rate

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Post-deploy sat.

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Time to value

Prerequisites

Configuration Options

Virtual Delivery Center · A new delivery category

A Virtual Delivery Center for Qlik AutoML

Pre-vetted experts and AI agents in the loop, assembled as a delivery pod. Pay in Delivery Units — universal pricing across roles, seniority, and tech stacks. No hiring, no contracting, no procurement cycle.

Plans from $2,000 — Starter Pack, 10 Delivery Units, 90 days
Refundable on unused Delivery Units, anytime — no questions asked
Re-delivery guarantee on acceptance miss
Pre-flight delivery sizing — you see the plan before you commit

Get a delivery plan for Qlik AutoML What’s a Virtual Delivery Center?

How a Virtual Delivery Center delivers Qlik AutoML

Outcome-based delivery via AiDOOS’s VDC model. Why VDC vs traditional consulting? →

Outcome-Based

Pay for results, not hours

Milestone-Driven

Clear deliverables at each phase

Expert Network

Access to certified specialists

Implementation Timeline

Discover

Requirements & assessment

Integrate

Setup & data migration

Validate

Testing & security audit

Rollout

Deployment & training

Optimize

Performance tuning

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Frequently Asked Questions

Do I need data science skills to use Qlik AutoML?

No. Qlik AutoML is specifically designed for business analysts and operational teams without advanced data science expertise. The platform automates model creation, feature engineering, and optimization, allowing non-technical users to build production-ready predictive models through an intuitive interface.

How does Qlik AutoML ensure model quality and accuracy?

The platform uses automated feature engineering, intelligent algorithm selection, and cross-validation techniques to optimize model performance. Continuous monitoring tracks model accuracy in production, with automated retraining and alerts when model drift is detected, ensuring predictions remain reliable.

Can Qlik AutoML integrate with our existing analytics infrastructure?

Yes. Qlik AutoML integrates seamlessly with Qlik Sense and QlikView, and connects to major data sources including SQL Server, Redshift, BigQuery, and Spark. Through AiDOOS marketplace, organizations can optimize deployment across hybrid cloud and on-premise environments.

How long does it take to deploy a predictive model with Qlik AutoML?

Typical deployment timelines are 2-6 weeks from project initiation to production, compared to 3-6 months with traditional data science teams. The exact timeline depends on data readiness, business requirements, and organizational change management processes.

What governance and compliance features does Qlik AutoML provide?

The platform includes comprehensive audit logging, role-based access controls, model version control, approval workflows, and compliance tracking. These features ensure models meet regulatory requirements and organizational governance standards across healthcare, finance, and other regulated industries.

How does AiDOOS enhance Qlik AutoML deployment?

AiDOOS marketplace provides enhanced deployment flexibility, streamlined governance frameworks, accelerated model operationalization, and optimized scalability. Organizations can leverage managed services for faster implementation, governance automation, and performance optimization across hybrid environments.