Why you need LLMOps
This article introduces LLMOps, a specialised branch merging DevOps and MLOps for managing the challenges posed by Large Language Models (LLMs)...
Unleashing the Power of LangChain Expression Language (LCEL): from proof of concept to production
LangChain has become one of the most used Python library to interact with LLMs in less than a year, but LangChain was mostly a library for POCs as it lacked the ability to create complex and scalable applications.
How we handled profile ID reconciliation using Treasure Data Unification and SQL
In this article we explain the challenges of ID reconciliation and demonstrate our approach to create a unified profile ID in Customer Data Platform, specifically Treasure Data. Data was gathered from an offline Data Warehouse and online web site tracking.
Snowflake’s Snowday ’23: Snowballing into Data Science Success
As we reflect on the insights shared during the ‘Snowday’ event on November 1st and 2nd, a cascade of exciting revelations about the future of Snowflake awaits us. This article dives into the noteworthy data science features unveiled during the event, shedding light on the innovation and advancements that took center stage.
How we interview and hire software engineers at Artefact
We go through the skills we are looking for, the different steps of the process, and the commitments we make to all candidates.
Encoding categorical features in forecasting: are we all doing it wrong?
We propose a novel method for encoding categorical features specifically tailored for forecasting applications.
How we deployed a simple wildlife monitoring system on Google Cloud
We collaborated with Smart Parks, a Dutch company that provides advanced sensor solutions to conserve endangered wildlife...
Deploying Stable Diffusion on Vertex AI
This article provides a guide for deploying Stable Diffusion model, a popular image generation model, on Google Cloud using Vertex AI.
All you need to know to get started with Vertex AI Pipelines
Presentation of a tool that demonstrates, practically, our experience using Vertex AI Pipelines in a project running in production.
