Our MLOps methodology delivers scalable AI models quickly and effectively.
Machine Learning (ML) is a form of AI that lets a system continuously learn from data through virtuous algorithms rather than explicit programming. It offers potential value for companies that use data to better understand the subtle changes in their customers’ behaviours, preferences and levels of satisfaction.
Maar ondanks deze mogelijkheden brengt het machinaal leren ook uitdagingen en risico's met zich mee. Ten eerste moeten complexe ML-modellen regelmatig worden ververst, wat hoge productiekosten met zich mee kan brengen. Ten tweede kan de AI, als de datakwaliteit niet nauwlettend wordt bewaakt, snel last hebben van prestatiedrift en bias.
To solve these challenges, we close the gap between Proofs of Concepts (POC) and Production by applying our Machine Learning Operations (MLOps) methodology to all of our Data and AI projects.
Our methodology is inspired by the DevOps approach used by the most innovative software companies, combining software development (Dev) and IT operations (Ops).
It aims to shorten the systems development life cycle and provide continuous delivery with high software quality.
Our MLOps approach helps companies seamlessly industrialise and scale their AI products.
Het traditionele gebruik van de mogelijkheden van Machine Learning heeft een aantal nadelen:
Data Scientists hardly foresee production constraints. They work in silos without interaction with software or data engineers. Their one-shot analyses in Python notebooks need to be reworked by downstream engineers to fit industrialisation requirements. This induces slowness and reduces time to market.
A lack of agility, which leads to high operational risk. In case the produced algorithms reveal themselves biased, unstable or prone to customer dissatisfaction, companies will not be able to respond in an acceptable time frame.
We think “product first” to help companies progress their AI assets smoothly to production while anticipating industrialisation constraints and risks. Our MLOps model is based on a solid ecosystem, and we apply the same processes for every AI project we deliver, from POC to product deployment.
SUCCESSFUL MLOPS APPROACH
To avoid the common pitfalls faced by many organisations looking to accelerate their data transformation.
A solid monitoring stack.
We testen alle data, functies en modellen voor elke nieuwe release om kwaliteits- of prestatiedrift te voorkomen.
Onze data, modellen en leerexperimenten worden allemaal in een logboek bijgehouden om een snelle heropbouw in geval van productie-incidenten te garanderen.
A resilient machine learning infrastructure.
We integreren alle Machine Learning-middelen (code, data, modellen) in een Continuous Integration and Continuous Delivery pipeline (CICD) om een snelle en naadloze uitrol naar de productie te garanderen.
A strong collaboration culture.
We zorgen ervoor dat alle belanghebbenden op dezelfde manier werken en passen de best practices op het gebied van software engineering toe op Data Science-projecten (versiebeheer, implementatieomgevingen, testen).
Lees onze Data Science blog post waarin we uitleggen hoe we MLOPS toepassen voor onze klanten.
