Ludovico Bessi

Ludovico Bessi

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Ludovico Bessi

#21 Online serving

Table of contents 1. Introduction. 2. Online predictions with batch features. 3. Online predictions with online features: real time vs near real time. 4. When does it make sense to move things online? Introduction As a follow up of my past article related to Batch Serving: Machine Learning at scale:...

Ludovico Bessi

#19 Batch predictions

Table of contents 1. Introduction. 2. Batch prediction in depth. 3. Does it always make sense to try and go online? Introduction In today's article, I will discuss the ideas behind Batch prediction. First of all, what do I mean with it? Batch prediction = offline predictions of ML models computed...

Ludovico Bessi

#17 Uber's Offline Platform For Optimal Feature Discovery.

1. Introduction 2. Optimal feature discovery through a centralised Feature Store 3. Results Introduction Today's topic is about feature selection. As a Machine Learning engineer, you are tempted to ingest as many features as possible from different teams in hope to improve your model performance. However, improving model performance by...

Ludovico Bessi

#16 Robust machine learning models in an adversarial world.

Table of contents 1. Introduction. 2. Adversarial examples and robust classifiers. 3. How to generate adversarial examples. 4. How to defend your precious Machine Learning models against adversarial examples. Introduction Today's article will dive deep into adversarial examples. There are two major reasons adversarial examples are important to understand: 1....

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