Add Airbnb Zipline

README.md

@@ -60,6 +60,7 @@ P.S., Want a summary of ML advancements? 👉[`ml-surveys`](https://github.com/e
 3. [Unbundling Data Science Workflows with Metaflow and AWS Step Functions](https://netflixtechblog.com/unbundling-data-science-workflows-with-metaflow-and-aws-step-functions-d454780c6280) `Netflix`
 4. [How DoorDash is Scaling its Data Platform to Delight Customers and Meet Growing Demand](https://doordash.engineering/2020/09/25/how-doordash-is-scaling-its-data-platform/) `DoorDash`
 5. [Revolutionizing Money Movements at Scale with Strong Data Consistency](https://eng.uber.com/money-scale-strong-data/) `Uber`
+6. [Zipline - A Declarative Feature Engineering Framework](https://www.youtube.com/watch?v=LjcKCm0G_OY) `Airbnb`
 
 ## Data Discovery
 1. [Amundsen — Lyft’s Data Discovery & Metadata Engine](https://eng.lyft.com/amundsen-lyfts-data-discovery-metadata-engine-62d27254fbb9) `Lyft`
@@ -81,14 +82,18 @@ P.S., Want a summary of ML advancements? 👉[`ml-surveys`](https://github.com/e
 
 
 ## Feature Stores
-1. [Introducing Feast: an open source feature store for machine learning](https://cloud.google.com/blog/products/ai-machine-learning/introducing-feast-an-open-source-feature-store-for-machine-learning) ([Code](https://github.com/feast-dev/feast))`Gojek`
+1. [Introducing Feast: An Open Source Feature Store for Machine Learning](https://cloud.google.com/blog/products/ai-machine-learning/introducing-feast-an-open-source-feature-store-for-machine-learning) ([Code](https://github.com/feast-dev/feast)) `Gojek`
 2. [Feast: Bridging ML Models and Data](https://blog.gojekengineering.com/feast-bridging-ml-models-and-data-efd06b7d1644) `Gojek`
 3. [Building a Scalable ML Feature Store with Redis, Binary Serialization, and Compression](https://doordash.engineering/2020/11/19/building-a-gigascale-ml-feature-store-with-redis/) `DoorDash`
 4. [Michelangelo Palette: A Feature Engineering Platform at Uber](https://www.infoq.com/presentations/michelangelo-palette-uber/) `Uber`
 5. [Distributed Time Travel for Feature Generation](https://netflixtechblog.com/distributed-time-travel-for-feature-generation-389cccdd3907) `Netflix`
+6. [Fact Store at Scale for Netflix Recommendations](https://databricks.com/session/fact-store-scale-for-netflix-recommendations) `Netflix`
 6. [The Architecture That Powers Twitter's Feature Store](https://www.youtube.com/watch?v=UNailXoiIrY) `Twitter`
+7. [Building the Activity Graph, Part 2 (Feature Storage Section)](https://engineering.linkedin.com/blog/2017/07/building-the-activity-graph--part-2) `LinkedIn`
 7. [Rapid Experimentation Through Standardization: Typed AI features for LinkedIn’s Feed](https://engineering.linkedin.com/blog/2020/feed-typed-ai-features) `LinkedIn`
 8. [Accelerating Machine Learning with the Feature Store Service](https://technology.condenast.com/story/accelerating-machine-learning-with-the-feature-store-service) `Condé Nast`
+9. [Building a Feature Store](https://nlathia.github.io/2020/12/Building-a-feature-store.html) `Monzo Bank`
+10. [Zipline: Airbnb’s Machine Learning Data Management Platform](https://databricks.com/session/zipline-airbnbs-machine-learning-data-management-platform) `Airbnb`
 
 ## Classification
 1. [High-Precision Phrase-Based Document Classification on a Modern Scale](https://engineering.linkedin.com/research/2011/high-precision-phrase-based-document-classification-on-a-modern-scale) ([Paper](http://web.stanford.edu/~gavish/documents/phrase_based.pdf)) `LinkedIn`
@@ -312,7 +317,7 @@ P.S., Want a summary of ML advancements? 👉[`ml-surveys`](https://github.com/e
 3. [Matchmaking in Lyft Line (Part 1)](https://eng.lyft.com/matchmaking-in-lyft-line-9c2635fe62c4) [(Part 2)](https://eng.lyft.com/matchmaking-in-lyft-line-691a1a32a008) [(Part 3)](https://eng.lyft.com/matchmaking-in-lyft-line-part-3-d8f9497c0e51) `Lyft`
 4. [The Data and Science behind GrabShare Carpooling](https://ieeexplore.ieee.org/document/8259801) (**PAPER NEEDED**) `Grab`
 5. [Optimization of Passengers Waiting Time in Elevators Using Machine Learning](https://www.youtube.com/watch?v=vXndCC89BCw&t=4s) `Thyssen Krupp AG`
-6. [Think out of the package: Recommending package types for e-commerce shipments](https://www.amazon.science/publications/think-out-of-the-package-recommending-package-types-for-e-commerce-shipments) ([Paper](https://assets.amazon.science/0c/6c/9d0986b94bef92d148f0ac0da1ea/think-out-of-the-package-recommending-package-types-for-e-commerce-shipments.pdf)) `Amazon`
+6. [Think Out of The Package: Recommending Package Types for E-commerce Shipments](https://www.amazon.science/publications/think-out-of-the-package-recommending-package-types-for-e-commerce-shipments) ([Paper](https://assets.amazon.science/0c/6c/9d0986b94bef92d148f0ac0da1ea/think-out-of-the-package-recommending-package-types-for-e-commerce-shipments.pdf)) `Amazon`
 
 
 ## Information Extraction