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AWS Partner Engagement Cloud / AI — Amazon Web Services

No-Code ML Pipeline: Stock Prediction & Visualization

How Tech Stack Playbook designed and delivered an end-to-end no-code machine learning pipeline for AWS — from raw NASDAQ data to time-series forecasting with SageMaker Canvas to interactive QuickSight dashboards.

AWS
Direct Client
Zero
Lines of Code
<1hr
Pipeline Standup
Published
Reference Architecture
Executive Summary

Overview

Tech Stack Playbook partnered with Amazon Web Services to design and build an end-to-end no-code machine learning pipeline demonstrating the full lifecycle of predictive analytics on AWS — without writing a single line of code.

The project ingests stock market data from NASDAQ, performs staging in S3, builds time-series forecasting models using Amazon SageMaker Canvas, and delivers interactive dashboards through Amazon QuickSight. The result is a complete, reproducible reference architecture published for the AWS ecosystem.

The Challenge

The Objective

SageMaker Canvas represents AWS's vision for democratizing machine learning. The challenge was demonstrating this capability in a way that is tangible, end-to-end, and immediately relatable — proving that a complete ML prediction pipeline could exist entirely within AWS's visual, no-code service layer.

  • Complete pipeline from raw data to trained model to dashboard — without writing code
  • Real-world dataset that makes the workflow reproducible and credible
  • Use case relatable to a broad audience: financial time-series forecasting
  • Demonstration that no-code can deliver meaningful results without traditional SageMaker overhead
  • Published reference that serves as both a learning resource and credibility asset
What We Delivered

Four-Stage Pipeline Architecture

Each stage leverages a purpose-built AWS service optimized for no-code operation.

01
Data Acquisition & Staging
NASDAQ historical data cleaned, normalized, and staged in S3 as the single source of truth — mirroring enterprise data platform patterns.
02
SageMaker Canvas Forecasting
Time-series model with AutoML, probabilistic multi-quantile output (p10/p50/p90), and built-in accuracy validation — all through a visual interface.
03
Model Output & Export
Structured forecast data with upper bound, expected value, and lower bound predictions enabling confidence range visualization.
04
QuickSight Dashboards
Interactive executive-ready dashboards with time-series charts, confidence intervals, and filtered trading-day views.
Key Insight
The entire pipeline — from raw NASDAQ data to interactive QuickSight dashboards — can be stood up in under an hour, compared to days or weeks for a traditional code-based ML pipeline.
Results

Outcomes & Business Impact

Published Reference Architecture Complete technical asset for the AWS ecosystem demonstrating SageMaker Canvas in a production-style context.
Full-Lifecycle Demonstration Proved the complete ML lifecycle can be executed entirely within AWS's no-code service layer.
Accessibility Proof Point Meaningful ML predictions built by non-technical users, validating the democratization thesis.
Rapid Time-to-Insight Under one hour from raw data to interactive dashboards, versus days for traditional approaches.
Technical Stack

Technologies Used

Amazon SageMaker Canvas Amazon QuickSight Amazon S3 AutoML Time-Series Forecasting NASDAQ Data Probabilistic ML