The observability blog

Learn how model observability can help you stay on top of ML in the wild and bring value to your business.

October 14th, 2021

Scaling model observability with Superwise & New Relic

Let’s skip the obvious, if you’re reading this it’s a safe bet that you already know that ML monitoring is a must; data integrity, model drift, performance degradation, etc., are already the basic standard of any MLOps monitoring tool. But as any ML practitioner will attest to, it’s one thing to monitor a single machine...
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May 12th, 2021

Stories from the ML trenches

What led us to create the #MLTalks initiative Back in February when we were on our 3rd lockdown, my team and I regrouped to think about our next steps. As we are in a fortunate position to meet with dozens of leading DS teams every week to brainstorm and discuss their challenges with scaling ML,...
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April 19th, 2021

Thinking about building your own ML monitoring solution?

“We already have one!” That’s the first sentence most of our customers said when we met to discuss AI assurance solutions. Most AI-savvy organizations today have some form of monitoring. Yet, as they scale their activities, they find themselves at a crossroads: should they invest more in their homegrown solution or receive support from vendor...
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April 19th, 2021

Facing the challenges of day 2 with your models in production

AI is everywhere. Businesses from all verticals are promptly adopting AI algorithms to automate some of their most important decisions: from approving transactions to diagnosing cancers, to granting credit, and so much more. As the AI race is booming, more organizations step into “Day 2”, the day their models are moved out of the realms...
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March 18th, 2021

Framework for a successful continuous training strategy

ML models are built on the assumption that the data used in production will be similar to the data observed in the past, the one that we trained our models on. While this may be true for some specific use cases, most models work in dynamic data environments where data is constantly changing, and where...
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March 4th, 2021

Measuring the performance of sub-groups

Just like in some cases “the whole is greater than the sum of its parts”, in machine learning “the performance of the model is not a reflection of the sum of its sub-groups.” Indeed, the good performance of machine learning models does not necessarily mean that every sub-group is optimized. Quite the contrary. Models don’t...
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December 9th, 2020

Fundamentals for efficient ML model monitoring

Today’s enterprises rely on machine learning-powered predictions to guide business strategies, such as forecasting demand and mitigating risk. For an increasing number of businesses, machine learning (ML) underpins their core business model, like financial institutions that use ML models to approve or reject loan applications. As ML is drastically different from other software or traditional...
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November 26th, 2020

How to get your business closer to your models?

If I were to select one image to describe the relationships of business teams with AI, I would choose the opening scene from 2001 space odyssey. Do you remember that scene, and the use of the black monolith, often interpreted as the allegory of change? It seems so well adjusted to the depiction of how...
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November 5th, 2020

Part II: Safely rolling out models to production

This piece is the second part of a series of articles on production pitfalls and how to rise to the challenge.  – In the first part of this article, we looked at the reasons that make “the ML Orchestra” such a complex one to tune, and laid out the best practices and processes to be...
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October 29th, 2020

Part I: Safely rolling out ML models to production

This piece is the first part of a series of articles on production pitfalls and how to rise to the challenge.  – Best CICD practices for the painless deployment of machine learning models and versions For any data scientist, the day you roll out your model’s new version to production is a day of mixed...
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October 11th, 2020

AI for marketing: how well is it working for you?

While it is true to say that AI is everywhere, this is especially accurate when it comes to marketing. Every leading marketing team today knows that machine learning can dramatically help them boost their effectiveness and their impact. Whether it’s to identify and engage users who are most likely to convert, ensure that the lifetime...
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September 22nd, 2020

How efficient are your fraud & data science teams?

Have you ever tried to find a needle in the haystack? That’s what fraud prevention feels like for most, and this is why fraud detection vendors leveraging machine learning have been burgeoning these past years. After all, it is, to date, the best way to identify issues in the millions of transactions that go through...
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