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Analytics and AI Services Overview: How Analytics and AI Services Drive Data-Driven Decisions

Lesson 9/18 | Study Time: 20 Min

Advanced analytics and artificial intelligence (AI) services are transforming the way businesses gather insights and make decisions.

On AWS, organizations can leverage scalable, integrated tools to collect, store, analyze, and visualize data from across their operations, enabling faster, more informed, and data-driven decision-making.

By adopting AWS analytics and AI services, companies can unlock hidden value in their data, automate complex tasks, and enhance strategic outcomes in competitive markets.

AWS Analytics Services: Empowering Actionable Insight

AWS offers a comprehensive suite of analytics services designed to handle every stage of the data lifecycle—from data ingestion to visualisation.


1. Amazon Redshift: A fully managed data warehousing service that enables fast querying and analysis of large datasets for business intelligence.

2. Amazon Athena: An interactive query service allowing users to analyse data directly in Amazon S3 using SQL—ideal for quick analytics without setting up infrastructure.

3. Amazon EMR: Supports big data processing using open-source frameworks like Apache Spark and Hadoop, helping organisations analyse massive datasets.

4. Amazon Kinesis: Provides real-time data streaming and analytics for rapid response to live business events.

5. Amazon QuickSight: Delivers intuitive, AI-powered data visualisation and business intelligence, making it easy for users across the organisation to explore data.


With these services, businesses can efficiently prepare, transform, process, and visualise data, leading to faster insights and better decision-making.​

AWS AI Services: From Data to Intelligent Action

AWS enables organisations to incorporate machine learning and AI-driven systems at any level of expertise, automating processes and enriching business intelligence.


1. Amazon SageMaker: Simplifies building, training, and deploying machine learning models to solve complex challenges such as demand forecasting or customer segmentation.

2. Amazon Forecast: Uses machine learning to provide accurate time-series forecasts, improving planning and resource management.

3. Amazon Kendra: Delivers AI-powered search across enterprise information, enhancing data discovery and usage.

4. Amazon Rekognition: Leverages computer vision for image and video analysis, supporting tasks like object detection and sentiment analysis.

5. Amazon Comprehend: Uses natural language processing to analyse unstructured text for sentiment, key phrases, and topic exploration.


These services enable automation, smart recommendations, fraud detection, predictive maintenance, and advanced analytics—all vital for proactive decision-making and competitive advantage.​

Nate Parker

Nate Parker

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Class Sessions

1- Cloud Computing Basics: Definition, Essential Characteristics, and Deployment Models (Public, Private, Hybrid) 2- AWS Cloud Overview: What is AWS, History, Scale, and Global Infrastructure 3- Cloud Benefits for Business: Agility, Scalability, Cost Efficiency, Innovation Enablement 4- Strategic Advantages: How Cloud Computing Drives Business Transformation and Competitive Advantage 5- Business Outcomes Enabled by AWS: Speed to Market, Improved Customer Experience, Operational Resilience 6- Financial Impact: Cost Avoidance vs. Cost Optimization, CapEx vs. OpEx Models 7- Compute and Storage Essentials: Introduction to Amazon EC2, S3, and Databases in a Business Context 8- Networking and Content Delivery: CloudFront, VPC Basics, and Relevance to Business Continuity 9- Analytics and AI Services Overview: How Analytics and AI Services Drive Data-Driven Decisions 10- AWS Security Framework: Shared Responsibility Model, Key Security Concepts 11- Compliance Programs: Relevant Compliance Certifications and Their Importance for Business Trust 12- Risk Management and Governance: Business Controls, Auditability, and Compliance Monitoring 13- Migration Approaches: Rehost, Replatform, Refactor—Business Considerations for Each 14- Cloud Adoption Framework: Organizational Readiness, Governance, and Change Management 15- Challenges and Risks: Common Business Risks and Mitigation Strategies in Cloud Adoption 16- AWS Pricing Models: Pay-as-You-Go, Reserved Instances, Savings Plans Explained Simply 17- Cost Management Tools: Billing Dashboards, Budgeting, and Cost Optimization Strategies for Business Leaders 18- Building a Business Case: TCO Analysis, ROI Estimation, and Stakeholder Alignment