Fennel (Acquired by Databricks)

Personalized search is the way of the future; this post will cover the most popular #MachineLearning features and techniques to get the job done👇 bit.ly/3WcTtTB

Wondering what goes into #FeatureEngineering for Fraud Detection? Learn all about it in our latest post by @nickparsons on the @FennelAI blog 🔗👇 bit.ly/3uU4RHX

Join @nikhilgarg28 at the @aicampai meetup on 12/13 for an exciting talk on realtime #MachineLearning RSVP at aicamp.ai/event/eventdet…

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 bit.ly/3UxNLtL

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

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 👇 bit.ly/3Vwpg14

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

Wondering why so many companies are leaning into realtime #ML? This post by @nikhilgarg28 was written just for you👇 bit.ly/3AHkMNb

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

Everything you need to know (and watch out for) when making the transition from batch 🐢 to realtime #ML 🚀 bit.ly/3XrR8p1

When do you use lambdas in Python? 🤔

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 bit.ly/3TEeN2d

Learn about best practices for writing counter and rate-based features in this post by @nikhilgarg28 of @FennelAI 👇 fennel.ai/blog/feature-e… #MachineLearning #FeatureEngineering #RecSys

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 arxiv.org/abs/2209.07663

Fifth speaker: @nikhilgarg28 of @FennelAI

My Real-Time Infra Landscape⚡: #ML: @ClaypotAI #Data Processing: @ApacheFlink Pipelines: @confluentinc @redpandadata Query Engines: @ClickHouseDB @MaterializeInc Who else should we include?