Machine learning drives many aspects of people's experience on Facebook. We use automatic language translation systems to remove communication barriers and enable people to interact with each other even if they speak different languages. Our image classification systems not only allow people to search for photos of their favorite moments, but also provide an immersive experience for the visually impaired with “talking images” that can be read with your fingertips. We also use machine learning in speech recognition, object and facial recognition, style transfer, video understanding, and many other services across our family of applications.
Given the growing demand of machine learning workloads, Facebook has been dedicated to advancing the state of artificial intelligence and its disciplines through open source contributions and collaboration. Building cutting-edge platforms to support and accelerate the growing demand of AI has been one of our key focuses. Over the past few years, we have been increasing our investments in machine learning hardware in our data centers to focus on accelerating the use of neural networks in our products and services. In 2013, we began our initial deployments with the HP SL270s G8 system for AI research. We learned a great deal about deploying GPUs at scale in our data centers, and identified serviceability, thermal efficiency, performance, reliability, and cluster management to be focus areas for our next generation systems. Subsequently, we contributed two server designs to the Open Compute Project (OCP) — Big Sur, followed by Big Basin — and have added them to our data center fleet.