2024-09-26 –, Louis Armand 2 - Ouest
Scaling machine learning at large organizations like Renault Group presents unique challenges, in terms of scales, legal requirements, and diversity of use cases. Data scientists require streamlined workflows and automated processes to efficiently deploy models into production. We present an MLOps pipeline based on python Kubeflow and GCP Vertex AI API designed specifically for this purpose. It enables data scientists to focus on code development for pre-processing, training, evaluation, and prediction. This MLOPS pipeline is a cornerstone of the AI@Scale program, which aims to roll out AI across the Group.
We choose a Python-first approach, allowing Data scientists to focus purely on writing preprocessing or ML oriented Python code, also allowing data retrieval through SQL queries. The pipeline addresses key questions such as prediction type (batch or API), model versioning, resource allocation, drift monitoring, and alert generation. It favors faster time to market with automated deployment and infrastructure management. Although we encountered pitfalls and design difficulties, that we will discuss during the presentation, this pipeline integrates with a CI/CD process, ensuring efficient and automated model deployment and serving.
Finally, this MLOps solution empowers Renault data scientists to seamlessly translate innovative models into production, and smoothen the development of scalable, and impactful AI-driven solutions.
Topic: This presentation delves into the development and implementation of a robust MLOps pipeline at Renault, designed to address the unique challenges of scaling machine learning deployments in large, complex organizations.
Audience: This talk is primarily aimed at data scientists, ML engineers, and anyone interested in:
• Learning about efficient MLOps implementation for large-scale deployments.
• Utilizing Python for comprehensive ML workflows, including data retrieval with SQL.
• Addressing key considerations for production-ready model deployment and management.
Attendees will gain insights into:
• Implementing an MLOps pipeline tailored for real-world use cases.
• Efficiently automating machine learning workflows for production environments.
• Utilizing Python code for all stages of model development and deployment.
• Addressing key considerations for scalable ML deployments at large organizations.
• Potential pitfalls in creating a custom MLOPS solutions.
Background Knowledge:
• Basic understanding of Machine Learning and Python programming is recommended.
• Familiarity with MLOps concepts and CI/CD pipelines would be beneficial.
I have been working as a Data Scientist at Renault Digital for 3 years, focusing on applications for the industry and manufacturing of electric cars. More recently, I became involved in the development of a MLOPS pipeline for Renault, and started to act as a relay with the DevOps team. Finally, I also contribute to the development Data Science best-practices with dedicated mentoring sessions.
I'm a data scientist and ML engineer at Renault, working on putting our ML models into production.