Quebec Artificial Intelligence Institute (Mila) and IBM have teamed up to accelerate artificial intelligence (AI) and machine learning research using open-source technology. Mila and IBM have been collaborating since early 2020 on a project that is meant to make a key component of AI, known as hyperparameter optimization, more accessible. The organizations claim that this would improve machine learning model performances and pinpoint within the'black box' of AI where models need work. "A collaboration with…IBM is a great opportunity to accelerate the development of an open-source solution…initiated at Mila." – Yoshua Bengio, Mila The two organizations are looking to integrate the Quebec institute's open-source software, Oríon, with IBM's Watson Machine Learning Accelerator, an AI model training and inference tool that the tech giant offers to businesses. The overall goal, they claim, is to "improve the development, deployment, and ongoing management of complex AI and deep learning models, as well as to make tools more accessible to a larger base of scientists, engineers, and developers through automation."

GPUs are designed and sized to run some of the most complex deep learning models such as RESNET, NMT, Transformer, DeepSpeech, and NCF. Most enterprise models being trained or deployed use only a fraction of the GPU compute and memory capacity. So, how do you reclaim this memory and compute headroom so that you can get the most out of your GPU investment? Watson Machine Learning Accelerator provides facilities to share GPU resources across multiple small jobs. This allows maximal return-on-investment for IT teams in enterprises where GPUs are in high demand. Additionally, you benefit from sharing a GPU across multiple jobs when your jobs are waiting for GPU resources or your distributed jobs running across GPUs might be stacked on top of each other on as few GPUs as possible to reduce the execution footprint.

Model training and hyperparameter search is an iterative process that can take days, weeks, or even months. Data scientists can spend a significant amount of time training models to achieve the wanted accuracy. Together, IBM Watson Studio Local 2.0.2 and IBM Watson Machine Learning Accelerator 1.2.0 form an enterprise AI platform for accelerating the model training process, combining speed and accuracy to drive value and reduce the model's time to go live to market. Model training is GPU-accelerated and can scale up automatically, which allows for allocations of more GPUs where available. A data scientist can get results faster and reach the accuracy level needed with our enterprise AI platform.

With Watson Machine Learning Accelerator you can drive faster time to results and accuracy, running in special AI hardware in the Cloud on On-Premises. WML Accelerator comes with SnapML library. We have developed an effi cient, scalable machine-learning library that enables very fast training of generalized linear models. We have demonstrated that our library can remove the training time as a bottleneck for machine-learning workloads, paving the way to a range of new applications. For instance, it allows more agile development, faster and more fine-grained exploration of the hyper-parameter space, enables scaling to massive datasets and makes frequent retraining of models possible in order to adapt to events as they occur.