Spark analytics platform with EMR Serverless and Athena
Big Data Analytics
Deploy a comprehensive analytics platform combining EMR Serverless for Spark processing, Athena for interactive queries, and Glue for data cataloging.
Read playbook KairoAI Systems operational playbooks
Explore the detailed frameworks, architectures, and checklists our teams use to deploy resilient ML platforms. Each article is designed as a practical guide you can implement immediately.
CloudFormation stack management with change sets and nested stacks
Infrastructure as Code
Implement sophisticated CloudFormation workflows with automated change set creation, approval processes, rollback capabilities, and nested stack management.
Read playbook Real-time feature store pipeline with Kinesis and SageMaker
Feature Engineering
Build a real-time feature ingestion pipeline using Kinesis Data Streams, Lambda processors, and SageMaker Feature Store for millisecond-latency feature serving.
Read playbook A/B testing infrastructure for ML models
Model Deployment
Deploy A/B testing infrastructure using SageMaker multi-model endpoints, API Gateway for traffic routing, and CloudWatch for comprehensive metrics analysis.
Read playbook Automated data quality monitoring with AWS Glue
Data Quality
Build automated data quality monitoring using AWS Glue, EventBridge, and SNS to validate data freshness, completeness, and accuracy continuously.
Read playbook Cloudflare-fronted app with CloudFront and AZ failover
Resilience & Edge
Follow this runbook to combine Cloudflare WAF/CDN with AWS Route 53 failover, CloudFront secondary, and dual-AZ origins that keep applications reachable during Cloudflare or AZ outages.
Read playbook Modernize legacy ingestion into an AWS-native data lake
Case Studies
Use this playbook to replicate the retailer engagement where we replaced bespoke on-prem collectors with a modular AWS data lake that eliminated silos and licensing spend.
Read playbook Automate ML training data pipelines with serverless AWS services
Case Studies
Follow this guide to stitch EventBridge, Glue, Lambda, Step Functions, and SageMaker into a resilient ML feature supply chain that keeps training data fresh without manual extracts.
Read playbook Batch data pipelines with Glue and EMR Serverless
Data Platforms
Use this playbook to decide when to lean on Glue ETL jobs, EMR Serverless Spark, or supporting services like Step Functions and MWAA for curated batch delivery.
Read playbook Designing zero-ETL intake on AWS
Data Platforms
Build an event-driven analytics plane where Redshift, Athena, and Glue operate directly on governed S3 data instead of duplicating extracts into staging warehouses.
Read playbook Blueprint for enterprise feature stores
Feature Engineering
Codify a feature platform that scales beyond a single team by enforcing ownership, change management, and automated quality bars.
Read playbook Reference architecture: multi-cloud ML delivery
Platform Architecture
Coordinate multi-cloud pipelines with consistent controls, portable artifacts, and shared governance while respecting each provider’s strengths.
Read playbook Handling multiple streams of unstructured data
Data Integration
Operationalize unstructured sensor feeds by combining streaming ingestion, Glue ETL, and a governed feature store with end-to-end lineage.
Read playbook Zero-ETL analytics foundation on AWS
Data Integration
Give teams governed access to fresh data without waiting for heavyweight ETL pipelines by leaning on S3, Glue, and Athena.
Read playbook Redshift intake with Glue, Step Functions, and Athena
Data Integration
Stand up a resilient Redshift Serverless intake using Glue jobs for curation, Step Functions for orchestration, and Athena for diagnostics.
Read playbook Monitoring with Model Monitor, CloudWatch, and CloudTrail
Operational Excellence
Stand up an observability plane that captures drift, alerts operators, and records every action for auditors without human babysitting.
Read playbook AWS automation services: Step Functions, Lambda, EventBridge
MLOps Automation
Design an event-driven automation backbone that reacts to production signals, orchestrates retraining, and documents every decision.
Read playbook Automating model training and registration
MLOps Automation
Instrument nightly SageMaker Pipelines to surface training data drift, compare against historical baselines, and register winning artifacts automatically.
Read playbook Monitoring deployment and inference
Operational Excellence
Combine automated endpoint updates with proactive drift detection so production predictions stay trustworthy without manual babysitting.
Read playbook Adaptive automation loops
Continuous Improvement
Automate the path from drift detection to retraining, evaluation, and production promotion while guarding against regressions.
Read playbook Creating dashboards and other visualizations
Analytics Enablement
Translate raw ML telemetry into executive-ready dashboards that drive trust, adoption, and rapid iteration.
Read playbook Iterating on unstructured data to uncover insights
Data Discovery
Structure exploratory programs that let teams experiment rapidly while keeping governance over ambiguous unstructured datasets.
Read playbook Eliminating covariant data points in real time
Feature Quality
Deploy streaming analytics that detect and remediate high-covariance features before they degrade model stability.
Read playbook Setting up ETL pipelines crash course
Data Engineering
Jumpstart your ETL program with modular ingestion, transformation, and governance patterns proven in production.
Read playbook Feature engineering on transaction data with SageMaker Processing
Retail Intelligence
Transform raw transaction feeds into competitive intelligence features using SageMaker Processing Jobs and governed storage.
Read playbook Detecting and addressing market shift
Resilient Operations
Blend scheduled and event-driven retraining to respond instantly when competitor prices swing sharply.
Read playbook Offline evaluation of model performance
Model Assurance
Design offline evaluation harnesses that mirror production conditions and quantify trade-offs before deployment.
Read playbook Unit testing data using AWS services
Data Quality
Integrate unit tests into your AWS-native data platforms using managed services and open-source frameworks.
Read playbook Blue/green deployment process for SageMaker
Deployment Excellence
Orchestrate traffic shifting, bake times, and rollback strategies tailored to SageMaker endpoints.
Read playbook AWS automation services: Step Functions, Lambda, EventBridge
Automation Toolkit
Understand when to use Step Functions, Lambda, and EventBridge individually or together for robust automation.
Read playbook Taming conflicting Lambda functions
Serverless Engineering
Refactor a sprawl of Lambda functions into maintainable domains with consistent interfaces and observability.
Read playbook Training with SageMaker Pipelines, Processing, HPO, and Feature Store
Model Development
Coordinate end-to-end training workflows that reuse curated features, scale experimentation, and capture lineage.
Read playbook Monitoring with Model Monitor, CloudWatch, CloudTrail
Observability
Capture data drift, operational metrics, and governance evidence across your ML estate using AWS-native tooling.
Read playbook Deployment using SageMaker Endpoints
Production Delivery
Master endpoint strategies that minimize downtime, optimize cost, and support diverse model portfolios.
Read playbook Governance with Model Registry, IAM, and AWS Config
Governance
Launch a governance foundation that captures lineage, enforces encryption, and separates duties for model promotion from day zero.
Read playbook Multi-AZ edge delivery with CloudFront, API Gateway, and Lambda
Resilience & Edge
Use this playbook to provision and operate CloudFront, API Gateway (HTTP API), Lambda, and an ALB-backed application tier with S3 failover so user traffic and data ingestion stay online during AZ disruption.
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