Key Takeaways
Accelerate Machine Learning with Amazon SageMaker Canvas
- SageMaker Canvas enables users to build high-quality ML models without writing any code
- It provides a visual, no-code interface for end-to-end ML, from data preparation to model training and deployment
- All the work done in SageMaker Canvas can be easily exported as Python code and Jupyter notebooks, enabling collaboration between experts and non-experts
Enabling Tangible Business Results
- SageMaker Canvas was used by GOFT, a retail company in Thailand, to automate their demand forecasting and replenishment processes
- GOFT saw a 95% reduction in the time spent on replenishment activities, from 120 minutes to 1.5 minutes per day
- They also achieved a 5% decrease in stock days and a 40% decrease in stock-outs, leading to significant operational efficiency and improved customer satisfaction
Leveraging AWS Services for ML Automation
- GOFT used a combination of AWS services, including AWS Step Functions, AWS Glue, and Amazon SageMaker, to build a fully automated ML pipeline
- This allowed them to handle massive amounts of data, scale their forecasting and replenishment processes, and achieve high accuracy without the need for manual intervention
Overall, the session highlighted how Amazon SageMaker Canvas and the broader AWS ML ecosystem can help organizations of all sizes accelerate their ML initiatives, automate critical business processes, and realize tangible benefits, even without deep ML expertise.