AWS SCHOLARSHIP COHORT•Amazon Web Services (AWS) × Dicoding Indonesia• Active Cohort (In Progress)

AWS AI Academy 2026

Cloud Infrastructure, Machine Learning & Spec-Driven Generative AI Engineering

AWS CloudGenerative AIAmazon BedrockMachine LearningSpec-Driven DevPython

TRACK COMPLETION PROGRESS:1/100 (1%)

TIMELINE: AUGUST 2026 – DECEMBER 2026STATUS: ACTIVE COHORT (IN PROGRESS)

CORE OBJECTIVE & TECHNICAL FOCUS

"Transitioning local predictive modeling and statistical analytics into scalable, production-ready cloud AI pipelines through enterprise AWS infrastructure and modern spec-driven development."

COHORT:AWS AI ACADEMY 2026

STATUS:VERIFIED SCHOLAR ENROLLED

PROGRESS:1 / 100 (1%)

01AI-ASSISTED WORKFLOWSActive (1/100)

Spec-Driven Development dengan Kiro

Spec-first architecture, prompt calibration & automated contract testing.

Hover for details ↗Dicoding ↗

CLASS 01 • INTERMEDIATE

Active (1/100)

Spec-Driven Development dengan Kiro

STRUKTUR MODUL:

Modul 01:Fondasi Spec-Driven & Schema ContractsLab: Definisi JSON Schema Contract

Modul 02:Prompt Calibration & Kiro AI PairingLab: AI Function Generation

Modul 03:Verifikasi Otomatis & CI/CD Spec FixturesLab: Microservice Test Governance

SKILLS:

Spec-Driven DesignKiro AI ToolingPrompt CalibrationContract Testing

CAPSTONE TARGET:

Microservice data pipeline yang sepenuhnya ter-generate dan tervalidasi via Kiro spec.

kiro.spec.json

{
  "service": "lead-time-engine",
  "contracts": { "input": { "state": "SP" }, "output": { "predicted_days": 2.4 } }
}

02ENTERPRISE CLOUDEnrolled

Belajar Dasar Cloud dan Gen AI di AWS

Infrastruktur cloud AWS, Amazon Bedrock & Foundation Models (Claude 3, Titan).

Hover for details ↗Dicoding ↗

CLASS 02 • FOUNDATIONAL / ENTERPRISE

Enrolled

Belajar Dasar Cloud dan Gen AI di AWS

STRUKTUR MODUL:

Modul 01:AWS Core: S3 Data Lakes, EC2, LambdaLab: Deploy Serverless REST API

Modul 02:Amazon Bedrock & Foundation ModelsLab: Boto3 Invoke Claude 3 Sonnet

Modul 03:RAG & Enterprise Security GovernanceLab: Document Q&A RAG Pipeline

SKILLS:

Amazon Web ServicesAmazon BedrockAWS LambdaRAG Vector Architecture

CAPSTONE TARGET:

Serverless generative analytics summarizer yang menganalisis dataset analitik.

bedrock_invoke.py

client = boto3.client("bedrock-runtime")
res = client.invoke_model(modelId="anthropic.claude-3-sonnet", body=payload)

03CORE PYTHONFast-Track

Memulai Pemrograman dengan Python

Python fundamental & modular data pipeline engineering.

Hover for details ↗Dicoding ↗

CLASS 03 • FOUNDATIONAL

Fast-Track

Memulai Pemrograman dengan Python

STRUKTUR MODUL:

Modul 01:Struktur Data & Memory EfficiencyLab: Data Parsing dengan Generators

Modul 02:Dataclasses & Functional ParadigmsLab: Modular Data Extractor

Modul 03:Package Management & Pytest FixturesLab: Production-Ready Package

SKILLS:

Python 3.12DataclassesType AnnotationsPytest Fixtures

CAPSTONE TARGET:

Library data processing berbasis tipe statis dengan pengujian unit otomatis.

data_stream.py

@dataclass(frozen=True)
class OrderTx: id: str; gmv: float; installments: int

04APPLIED MLActive (1/100)

Belajar Machine Learning untuk Pemula

Scikit-Learn preprocessing, supervised/unsupervised ML & model evaluation.

Hover for details ↗Dicoding ↗

CLASS 04 • FOUNDATIONAL / APPLIED

Active (1/100)

Belajar Machine Learning untuk Pemula

STRUKTUR MODUL:

Modul 01:Data Preprocessing & Feature ScalingLab: Scikit-Learn ColumnTransformer

Modul 02:Supervised Classification & ClusteringLab: Decision Trees & K-Means

Modul 03:Confusion Matrix & Cross ValidationLab: Model Evaluation k-Fold

SKILLS:

Scikit-LearnFeature ScalingCross-ValidationSupervised ML

CAPSTONE TARGET:

Model ML prediktif dengan k-fold validation yang diekspor untuk cloud serving.

ml_pipeline.py

pipeline = Pipeline([('scaler', StandardScaler()), ('clf', RandomForestClassifier())])
pipeline.fit(X, y)

CLOUD & AI ARCHITECTURE PIPELINE(Hover node untuk melihat tools & perannya)

01Spec & Contracts

02Preprocessing

03Machine Learning

04Generative AI

05Serverless Cloud

// ENROLLED MODULES & CURRICULUM (4)

Spec-Driven Development dengan KiroIntermediate

Spec-first development methodology, prompt architecture, AI-assisted code generation, and test-driven specification governance.

Status: Active (1/100)Curriculum ↗

Belajar Dasar Cloud dan Gen AI di AWSFoundational

AWS Cloud Practitioner fundamentals, global infrastructure, Amazon Bedrock, Foundation Models, and enterprise GenAI deployment.

Status: EnrolledCurriculum ↗

Memulai Pemrograman dengan PythonFoundational

Advanced Python scripting, data structures, functional paradigms, and modular pipeline design for production analytics.

Status: Fast-TrackCurriculum ↗

Belajar Machine Learning untuk PemulaFoundational / Applied

Supervised and unsupervised learning, Scikit-Learn pipelines, feature engineering, model evaluation, and cross-validation techniques.

Status: Active (1/100)Curriculum ↗

01. Program Objective & Context

The AWS AI Academy 2026 is a merit-based technical scholarship organized by Amazon Web Services (AWS) in collaboration with Dicoding Indonesia.

The program bridges empirical statistical analysis with enterprise cloud computing—transitioning standalone Python scripts into distributed, serverless cloud AI pipelines.

02. Architectural Transformation

Dimension	Local Prototyping Paradigm	Enterprise Cloud AI Standard
Ingestion	Static local CSV / database dumps	Event-driven Amazon S3 data streams
Data Processing	Ad-hoc in-memory scripts	Modular Python packages with static typing
Modeling	Standalone Jupyter Notebooks	Versioned Scikit-Learn pipelines
Generative AI	Manual LLM copy-pasting	Managed Amazon Bedrock SDK invocations
Governance	Unconstrained code generation	Spec-driven contracts via Kiro specifications

03. Applied Capstone Milestones

Serverless AI Inference Pipeline: Deploying lightweight scikit-learn models onto AWS Lambda with REST API Gateway endpoints.
Generative Analytics Summarizer: Integrating Amazon Bedrock (Claude 3 / Titan) to automatically synthesize analytical insights from tabular datasets.
Spec-Driven Quality Standard: Enforcing schema invariants and contract testing across all interactive portfolio data modules.

NOTE

Course progress, modular certifications, and capstone implementations are continuously updated on this live track as milestones are completed.