Building Realtime End to End Sales Forecasting AI from Scratch
In this in-depth, hands-on project, we’ll design, build, and deploy a production-grade Sales Forecasting Machine Learning Pipeline using Astro, the modern data orchestration platform powered by Apache Airflow, the world’s most widely used open-source tool for orchestrating data pipelines.
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We’ll walk through everything; from system and MLOps architecture to data ingestion, transformation, model training, evaluation, deployment, and real-time inference with a Streamlit UI.
By the end of this video, you’ll have the skills and knowledge to deploy your own scalable, production-ready ML pipeline.
💡 What You’ll Learn:
✅ Designing robust System & MLOps architectures for ML pipelines
✅ Setting up Astro for multi-service orchestration
✅ Ingesting, validating & transforming realistic sales, promotions & traffic data
✅ Training & evaluating advanced forecasting models
✅ Deploying ML models to production with registry integration
✅ Creating a real-time inference UI with Streamlit
✅ Validating and testing end-to-end ML workflows
Timestamps:
0:00 Introduction
2:19 System Architecture Overview
5:34 MLOps Architecture
8:24 Data Flow, Deployment, ML Pipeline and Inference Architecture
15:49 Component Interaction Process Flow
27:35 Getting Started with Astro
31:55 Environment and Project Setup
37:27 Adding More Services to Astro
51:21 Sales Forecast Model Training
1:01:22 Advanced Realistic Sales Data Extraction
2:01:53 Sales, Promotion and Traffic Data Validation
2:19:32 End to End ML Models Training
4:14:30 ML Models Evaluation, Model Registration and Production Deployment
4:37:15 End to End Pipeline Testing and Validation
4:40:10 Pipeline Results and Validation
4:44:00 Streamlit UI Deployment and Realtime Inference
4:58:00 Outro
🔗 Resources & Links:
Full Source Code → https://github.com/airscholar/astro-s...
Astro by Astronomer → https://www.astronomer.io/astro
Apache Airflow → https://airflow.apache.org/
💬 Have questions or ideas? Drop them in the comments — I reply to every one!
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