Case Study: Mozilla achieves scalable, faster machine learning deployments with Outerbounds

Mozilla’s Journey to Scalable Machine Learning with Outerbounds

Mozilla, a privacy-focused software and technology company, faced challenges with its fragmented and inefficient machine learning infrastructure. Each of its teams used different, disjointed stacks for model deployment, leading to difficulties in scaling, monitoring, and troubleshooting. To unify and scale its ML operations while strictly adhering to its data privacy requirements, Mozilla partnered with vendor Outerbounds and adopted its platform.

Mozilla significantly increased its model deployment frequency, improved integration speed from weeks to days, and gained critical transparency into job performance for easier diagnostics, freeing up engineering resources to focus on building better models.

Mozilla

Chelsea Troy

Lead Engineer