Large Scale Machine Learning Course
Master large-scale machine learning for real-world systems. Learn data ingestion, feature engineering, distributed training, low-latency serving, monitoring, and cost control to design reliable, high-performance ML pipelines that power modern tech products.
from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
The Large Scale Machine Learning Course shows you how to design, train, and serve high-performance recommendation models end to end. Learn scalable data ingestion, streaming pipelines, feature engineering, and low-latency serving with ANN systems. Build reliable, cost-efficient ML platforms with monitoring, observability, governance, security, and robust evaluation so you can confidently operate models at tens-of-millions scale.
Elevify advantages
Develop skills
- Design large-scale recommenders: two-tower, ANN and matrix factorization.
- Train ML models at scale: negative sampling, tuned loss and hyperparameters.
- Build low-latency ML serving: feature stores, ANN retrieval and A/B tests.
- Engineer batch and streaming features with Spark, Kafka and Parquet.
- Operationalize ML: monitoring, cost control, governance and retraining.
Suggested summary
Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
Your classes are perfect. I purchased the one-year package and finally have the opportunity to follow various topics of my interest without needing to change platforms... I thank you for everything you do, I've already recommended you to other people...
Giulio CarloDigital Marketing Student
I like how the lessons are straight to the point and how I can switch chapters and skip content I don't need.
Mariana FerresPhotography Student
I like the content and the way videos are presented and transcribed, which speeds up the process!
Luciana AlvarengaNail Design Student
The platform is fast, simple to use. The diversity of content and complementary videos really help with learning.
André FelipePrompt Engineering Student
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