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Artefact Newsletter - May 30th, 2022.

MLOps EDITION​

For this monthly edition, we are focusing on Artefact's MLOPs with insightful analyses from our experts, a library of must-read articles on MLops, and proudly sharing the success of our team during an international Hackathon. MLOps methodology, which delivers scalable AI models quickly and effectively, is also further described here.
As usual, we're also sharing some of our recent client cases and company achievements to keep you updated!

We hope you enjoy!
The Artefact Team

In this article, we have selected a few stories and blog posts that we found insightful, then took a step back, and tried to infer what to expect from those “signs” for 2022, such as:
- Taming the indecency of foundation models 
- Making AI sustainable 
- Adding a touch of zen to your MLOPs
- Making data more of a product than a simple input 

Read more    
​​​​​
 

This blog article posted on Medium explains that data scientists should focus on the accuracy of their models while ML Engineers should prioritize ensuring that the models can be used by the wider business.

In summary, defining models and building the software surrounding those models should be two priorities from the start of each project. Therefore, having separate streams with different responsibilities can help teams concentrate on both in parallel.

Read more    
​​​​​
 

MLflow is a commonly used tool for machine learning experiments tracking, models versioning and serving.
In this first article of a series of three, we explain how to deploy the tracking instance on Kubernetes and use it to log experiments and store models. ​​​​​

Read more    
 
In the second article, we explain how ML models serve an API on K8s.  
​​​​
​​​To close out the 3-part series,  we describe the scalability of our deployments. ​​​​​​
 
 
MLOps Library
Selection of "must read" articles from our experts
MLOPs in 10 minutes  - How MLOps helps across all stages of ML project

The levels of MLOps: Continuous delivery and automation pipelines in machine learning

A rubric for ML production readiness and technical debt reduction: the ML Test Score

Our guide: The Rules of Machine Learning
 
 

​​​​​​During the five-week virtual Hackathon, Artefact’s team impressed the judges by developing a NER (Named Entity Recognition) pipeline to detect brands in the beauty and cosmetics sector in Twitter posts with an integrated feedback loop, winning second place out of 13 participating teams. 
 

Read and watch the video pitch   
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ARTEFACT LAST NEWS

Watch Vincent Luciani, CEO of Artefact, explaining how Artefact assisted the CARREFOUR Group in building a sales-prevision algorithm, notably for its bakery sections, to calculate how much of each product was needed everyday. Thus, the Carrefour Group was able to significantly reduce their food waste.
Justine Nerce, Managing Partner of Artefact, also mentions how the company is addressing the challenge of recruitment with the Artefact School of Data, to train the new generation of data leaders

Watch the video   
 

Webhelp Enterprise turned to Artefact to develop the "Webhelp Lead Factory" a model capable of providing accurate and comprehensive data sets to help salespeople leverage available data and better address new demographics or regions.
“Taking action on the right signals and customer business drivers can give a boost to a sales operation and create additional value.”
This resulted in a 40% increase in productivity on some projects as well as a 1:4 return on investment in others. 

Read the case    
 
Artefact is a next-generation data services company, specialising in data consulting and data-driven digital marketing. We're dedicated to transforming data into business impact.

14 Countries | +300 Clients | +1000 Employees
 
 
 
 
 
 
 
 
 
DATA CONSULTING | DATA & DIGITAL MARKETING | DIGITAL COMMERCE
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