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Seleziona i singoli sprint per creare un percorso su misura, oppure scegli i laboratori pratici per ogni modulo completo.
Data Engineering on AWS
Covers ingestion patterns, ETL, Data Lakes, Orchestration, and Cost Management.
Sprint 1: Foundations & Ingestion
โฌ350Intro to Data Engineering & Blueprint
Batch Ingestion Architecture
Streaming Ingestion Foundations
Sprint 2: Transformation & Orchestration
โฌ350Data Transformation & ETL
Data Lake Foundations
Pipeline Orchestration
Sprint 3: Optimization & Capstone
โฌ350Optimization & Cost Management
End-to-End Lab
Building Data Analytics Solutions Using Amazon Redshift
Deep dive into Redshift architecture, performance, and lakehouse integration.
Sprint 1: Redshift Deep Dive
โฌ450Redshift Architecture
Lakehouse Integration
Build Modern Applications with AWS NoSQL Databases
Mastering DynamoDB modeling, access patterns, and event-driven processing.
Sprint 1: NoSQL & DynamoDB
โฌ450DynamoDB Modeling
Scaling & Streams
Building Streaming Data Analytics Solutions on AWS
Real-time analytics with Kinesis, SQL analytics, and stream-to-lakehouse patterns.
Sprint 1: Advanced Streaming
โฌ450Streaming Architecture
Kinesis Data Analytics
Complex Event Processing
Designing and Implementing Storage on AWS
Storage fundamentals, security, governance, lifecycle management, and operational excellence.
Sprint 1: Fundamentals & Security
โฌ350Storage Fundamentals
Governance & IAM
Encryption
Sprint 2: Lifecycle & Recovery
โฌ350Lifecycle & Cost
Backup & DR
Monitoring
Sprint 3: Exam Prep
โฌ350Exam Scenario Workshop
Readiness & Recap
Machine Learning Engineering on AWS (MLEA)
Foundations, Data Feature Engineering, Training, Tuning, and Basic Deployment.
Sprint 1: Foundations
โฌ350ML Foundations
Business Challenges
Data Processing
Sprint 2: Feature Engineering & Training
โฌ350Feature Engineering
Modeling Approaches
Model Training
Sprint 3: Tuning & Deployment
โฌ350Evaluation & Tuning
Deployment Foundations
Capstone
Amazon SageMaker Studio for Data Scientists
Studio workflows, Data Processing, Experiments, Pipelines, and Advanced Inference.
Sprint 1: Studio & Processing
โฌ350Studio Setup
Data Processing
Scale
Sprint 2: Features & Experiments
โฌ350Feature Store
Development
Debugger & Bias
Sprint 3: Pipelines & MLOps
โฌ350Deployment & Pipelines
Inference
Capstone
MLOps Engineering on AWS (MLOE)
MLOps Maturity, Orchestration, Scaling, Governance, Monitoring, and Troubleshooting.
Sprint 1: Culture & Environment
โฌ350Intro to MLOps
Experimentation
Repositories
Sprint 2: Orchestration & Scaling
โฌ350Orchestration
Advanced Workflows
Scaling
Sprint 3: Reliability
โฌ350Monitoring
Troubleshooting
Final Project
Riepilogo Ordine
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