Machine learning, a subset of the field of artificial intelligence, has enabled impressive progress on a range of problems from identifying objects in images to translating French into English. At the University of Notre Dame, the Cyberinfrastructure to Accelerate Machine Learning (CAML) resource allows faculty across the university to leverage machine learning to address problems within their disciplines. CAML uses graphical processing units (GPUs) to provide a significant boost in the speed of training machine learning algorithms, enabling researching to solve problems faster and to train more complex models capable of addressing more difficult problems. CAML benefits a wide range of research activities, from searching for new particles at the Large Hadron Collider to exploring new chemicals leading to medical breakthroughs. CAML also benefits the broader community as part of the Open Science Grid, serving researchers from universities and labs across the US. Furthermore, CAML is used for education and outreach involving students ranging from high school to graduate school, helping to train the next generation to tackle data science challenges in the public and private sector.<br/><br/>CAML provides GPU resources for accelerating machine learning to the research community both locally at the University of Notre Dame and nationally through the Open Science Grid (OSG). CAML physically hosts GPU resources suitable for accelerating the training of models from standard Deep Learning libraries, but also enables on-demand cloud access to more experimental architectures like FPGA resources. Configured for both interactive and batch access, CAML supports both small-scale explorations to large-scale discovery science.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.