Deploy Decision Platforms in Real-Time

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Deploy Decision Platforms in Real-Time

This course is part of AI Techniques, Causal Inference & Business Optimization Specialization

Instructor: Hurix Digital

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4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

3 hours to complete

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

3 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Real-time decision systems need end-to-end latency optimization, covering data ingestion, processing, logic, and actions, not just speed.
Platform evaluation must balance performance with governance, usability, and operational needs for successful enterprise adoption.
Streaming architectures require fault-tolerant designs to ensure reliability and continuity in automated decision workflows.
Validating performance under realistic load conditions is essential to ensure production readiness.

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This course is part of the AI Techniques, Causal Inference & Business Optimization Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
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There are 4 modules in this course

Transform your organization's decision-making speed with real-time AI platforms.

This Short Course was created to help data professionals accomplish enterprise-grade decision automation that delivers insights within seconds, not hours. By completing this course, you'll be able to configure high-performance alerting systems, evaluate platforms against business criteria, and build streaming pipelines that trigger automated decisions. You'll master the critical skills needed to ensure your decision intelligence systems meet strict SLA requirements while maintaining scalability and governance standards. By the end of this course, you will be able to: configure alerting rules with sub-60-second latency, evaluate platforms using structured scorecards, implement Kafka-Spark decision pipelines, and validate performance under load. This course is unique because it combines hands-on platform configuration with real-world performance validation techniques used by leading enterprises. To be successful in this project, you should have a background in streaming technologies and data platform management.

This module introduces learners to configuring alerting rules within an AI decision-intelligence platform to detect performance and operational issues. Learners also validate end-to-end data-to-decision latency to ensure timely, reliable, and actionable insights within strict real-time performance thresholds.

What's included

1 video1 reading1 assignment1 ungraded lab

This module equips learners to assess AI platform capabilities across usability, scalability, and governance, synthesize findings into a structured scorecard, and communicate evidence-based recommendations effectively to senior leadership.

What's included

2 videos1 assignment1 ungraded lab

This module guides learners to design and implement a real-time Kafka–Spark streaming pipeline that monitors KPIs, detects threshold breaches, and automatically triggers data-driven decisions with low-latency, production-ready reliability.

What's included

1 video1 reading2 assignments

This module enables learners to measure and analyze system throughput and end-to-end latency under load, validate performance against defined SLAs, and identify bottlenecks to ensure reliable, scalable, and compliant system operation.

What's included

2 videos1 reading2 assignments

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Instructor

Hurix Digital

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