Netflix's Unified ML Model: Deep Dive into Model Consolidation Manageability & Deployment Strategies
Channel: DataTrek
Date: 2024-01-24

#data #machinelearning #artificialintelligence #datascience #statistics #llms #deeplearning 

Blog Link: https://netflixtechblog.medium.com/lessons-learnt-from-consolidating-ml-models-in-a-large-scale-recommendation-system-870c5ea5eb4a
Other Recommendation System Videos on the channel
Evolution of Recommendation Systems | Deep Learning | Instagram Pinterest X Recsys explored | WADLA https://youtu.be/lgoyJn7MsH8 
Overcoming Biases for a Better Recommender System: How Tech-Titans Combat Recommender System Biases https://youtu.be/oGb_mIdO0tA 
Innovative Linkedin's Deep-leaning based CTR Modeling: The Deep, Wide, and Shallow Towers Explained youtu.be/7l0HLYVFEuU
Lessons from Instagram’s Cutting-Edge Recommendation System youtu.be/Myna6rnmCG8
How Twitter recommends Tweets: Inside Twitter's Recommendation Algorithm 🐦 youtu.be/IhGq9jgcxFM
How eBay Built a Next-Generation Language Model to Revolutionise Online Shopping 🛒 youtu.be/h51nbWr7feo
Leveraging Pre-trained Bert to generate Entity Embeddings across domain 📚 youtu.be/v-0J7o-nDBE
Uplift Modeling to Detect Causal Effect | Uplift Modeling & Causal Inference to Improve Instagram ✨youtu.be/rKzG0Ct_ReA
Advertiser Recommendation Systems at Pinterest | Machine Learning & Algorithm-driven Ads Platform 📌youtu.be/KEiBJ0fal3Q
Dynamic Pricing Strategy to effectively fill slots | E-commerce | Travel | Hospitality Domains 💰youtu.be/a_CXpnsvPa0
Train your own Product Embeddings | Embeddings across Domains | Training on 1.02 Billion rows on GPU 💡youtu.be/DN4S96oHRhE
Model-Based Account Recommendations @ Twitter | Recommendation System | Retrieval & Ranking | RECSYS 📊 youtu.be/Xqo8fwgjxW4
Approximate Nearest Neighbour and a popular Library Annoy for k-Nearest Neighbour Search & Product Quantizer 🔍youtu.be/DSQOrBTqmYA & youtu.be/50PNumB7s3U
PID Controller to add a layer of diversity to the recommender systems 🎛️ youtu.be/laTxgnzjfR0

In this video, we delve into a Netflix blog discussing the consolidation of multiple Machine Learning models into a unified one, exploring the lessons learned in the process.

The approach offers several advantages, first and foremost being that the unified model learns from the goodness of multiple tasks and multiple labels, and as a result, it becomes better at each individual task. It proves to be easy to maintain, and any feature upgrade benefits all consolidated tasks.

The blog also explores how deployment can vary based on specific environment needs, including considerations for latency, throughput, data freshness, candidate generators, and business requirements.

Furthermore, the blog also touches upon scenarios where model consolidation may not be as beneficial, such as situations where the tasks are very different. Interestingly, the blog also draws parallels with large foundation models (LLMs) in Natural Language Processing (NLP), favouring model consolidation.


#datascience #machinelearning #statistics #deeplearning #programming #python #datatrek #youtube #interview #interviewpreparation #interviewquestions #datascientist #dataanalytics #machinelearningengineer #datasciencejobs #datasciencetraining #datasciencecourse #datascienceenthusiast #career #careeropportunities #careergrowth #careerdevelopment #datascienceenthusiast #interviewing #ml #ai

#datatrek #datascience #machinelearning #statistics #deeplearning #ai

About DataTrek Series
youtu.be/YvUMvFVwC_E

Business Enquiries: collabwithdatatrek@gmail.com
Find me on Instagram: www.instagram.com/simplyspartanx/



Music: www.bensound.com/royalty-free-music