Fennel (Acquired by Databricks)

Fully-managed, realtime feature engineering platform that combines the simplicity of Python with the performance of Rust.

[Fennel (Acquired by Databricks) @FennelAI]

Personalized search is the way of the future; this post will cover the most popular #MachineLearning features and techniques to get the job done👇

https://t.co/8ZpowvYfw3

[Fennel (Acquired by Databricks) @FennelAI]

Wondering what goes into #FeatureEngineering for Fraud Detection? Learn all about it in our latest post by @nickparsons on the @FennelAI blog 🔗👇

https://t.co/yhWgdoWc2O

[Fennel (Acquired by Databricks) @FennelAI]

Join @nikhilgarg28 at the @aicampai meetup on 12/13 for an exciting talk on realtime #MachineLearning

RSVP at https://t.co/8KPetLlsMN

[AICamp @aicampai]

Next ML meetup in SF/Bay will feature topics on real-time ML. @nikhilgarg28 from @FennelAI ,@Igor__Markov from @MetaAI will dive deep into real-time ML, architecture & e2e ML platform, with 🍕🍺🎁 @nickparsons
⏰ Dec 13, In-person | Virtual
📌 https://t.co/3gPIWCMIFf

[Fennel (Acquired by Databricks) @FennelAI]

Learn the 5 serving stages of #RecSys 👇

🔍 Retrieval of relevant items
⏳ Filtering to narrow down options
🏗️ Feature extraction to capture key characteristics
⚽️ Scoring to evaluate relevance & quality
🥇 Ranking to present top choices to users

https://t.co/kX4tuQNvVu

[Nikhil Garg @nikhilgarg28]

Once ANY marketplace hits enough scale to meet product market fit, THE most powerful growth lever almost always is improving "matching" via ranking/recsys.

[Fennel (Acquired by Databricks) @FennelAI]

It turns out realtime #ML models cheat on tests, too 👀

Learn about the two most common types of information leakage and how to eliminate them 👇

https://t.co/3dz60A6oSv

[Pau Labarta Bajo @paulabartabajo_]

Behind every great data scientist...
... there is a greater data engineer.

[Fennel (Acquired by Databricks) @FennelAI]

Wondering why so many companies are leaning into realtime #ML? This post by @nikhilgarg28 was written just for you👇

https://t.co/IFETGeEXCN

[Pau Labarta Bajo @paulabartabajo_]

Feature Store-less ML is still a thing...

[Fennel (Acquired by Databricks) @FennelAI]

Everything you need to know (and watch out for) when making the transition from batch 🐢 to realtime #ML 🚀

https://t.co/RaTGs2pVou

[Matt Harrison @__mharrison__]

When do you use lambdas in Python? 🤔

[Fennel (Acquired by Databricks) @FennelAI]

Introducing our second post on #FeatureEngineering for #RecSys. Click the 🔗 below to learn about:

🕹️ Max/min pool-related options
🧮 Wilson intervals to normalize rate features
🪟 Rolling time windows
🔌 Making counter/rate features realtime

https://t.co/kOotlBiQkU

[Fennel (Acquired by Databricks) @FennelAI]

Learn about best practices for writing counter and rate-based features in this post by @nikhilgarg28 of @FennelAI 👇

https://t.co/L2XOWtMY3B

#MachineLearning #FeatureEngineering #RecSys

[Xavier (Xavi) Amatriain @xamat]

A lot has been speculated about TikTok's recommendations. This is the first paper I've read by the team, and it has many interesting details: expirable embeddings, parameter server, online training... Good #recsys stuff https://t.co/r8UBCrnZi8

[Andrew Sova, CFA @andrew_sova_]

Fifth speaker: @nikhilgarg28 of @FennelAI

[Astasia Myers @AstasiaMyers]

My Real-Time Infra Landscape⚡:

#ML: @ClaypotAI
#Data Processing: @ApacheFlink
Pipelines: @confluentinc @redpandadata
Query Engines: @ClickHouseDB @MaterializeInc

Who else should we include?