🚀 Automate Machine Learning Pipelines with AWS Lambda | Build Serverless ML in Python
🎓 Full Course HERE 👉 https://community.superdatascience.co...
In this practical session, we build an automated machine learning pipeline with AWS Lambda. Discover how Lambda connects with S3 to trigger training, retrain models, and run predictions automatically. Using a Random Forest model, we’ll code everything step by step in Python, while exploring why serverless compute is so cost-effective.
You’ll learn how to set up event-driven ML pipelines, manage models in S3, trigger inference on new data, and receive automatic email notifications on retraining. This is a complete guide to building scalable, low-cost ML workflows.
✅ Automate ML training & retraining with AWS Lambda
✅ Train a Random Forest model directly in Python
✅ Save & manage models in AWS S3
✅ Trigger inference on new data automatically
✅ Receive real-time email notifications
✅ Understand AWS cost savings with serverless compute
🔗 Also find us here:
🌐 Website: https://www.superdatascience.com/
💼 LinkedIn: / superdatascience
📬 Contact: support@superdatascience.com
⏱️ Timestamps & Chapters
00:00 – Intro: Automated ML with Lambda
00:25 – S3 and Lambda Workflow Setup
01:00 – Coding the Training Step in Python
01:40 – Building & Saving a Random Forest Model
02:20 – Automating Predictions with Lambda
03:00 – Notifications on Model Retraining
03:40 – Cost Efficiency: Why Lambda Saves Money
04:20 – Wrap-Up & Next Steps
🧠 Hashtags
#AWSLambda #MachineLearning #Serverless #MLOps #DataScience #AutomatedML #CloudComputing #RandomForest #AWSML #MLPipeline
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