16nov4:00 PM5:30 PM10X Faster Machine Learning from R&D to ProductionAzin Asgarian - Applied Research Scientist | Kyryl Truskovskyi - ML Engineer | Christopher Tee - ML Engineer, Georgian4:00 PM - 5:30 PM
Abstract:In recent years, we have seen astonishing leaps in the application of machine learning in various industries. However, as the complexity of machine learning models and the
In recent years, we have seen astonishing leaps in the application of machine learning in various industries. However, as the complexity of machine learning models and the size of the datasets increase, experimentation with these models and productionizing them also become more complex and time-consuming! To overcome these challenges and facilitate the adoption of these models in the industry, various solutions are proposed by the ML community over the last few years. In this workshop, we walk you through some of these solutions and show you useful practices to overcome the aforementioned challenges! More specifically, we show you how you can supercharge your machine learning experimentation pipeline with tools like PyTorch Lightning and DVC and make your path to production smoother and faster using Kubeflow and its add-ons.
What You’ll Learn:
In this workshop, you will learn about useful tools and practices which can help you to supercharge the whole machine learning cycle from R&D experimentation to production. More especially, our workshop is split into two parts:
1) In the first half of the workshop, we will focus on supercharging the R&D experimentation cycle. Here we will show you how you can make your experimentation pipeline faster, more memory efficient and reproducible using tools like PyTorch Lightning and DVC
2) In the second half of the workshop, we will focus on the productionization side and explain how tools such as KubeFlow Pipelines, Kserve, SeldonIO, aws-cdk and many more that can be used to make the path to production easier, faster, and smoother! We hope that by learning about these practices and tools, you can unleash the untapped potential of complex ML models for your own problems!
Azin Asgarian is currently an applied research scientist on Georgian’s R&D team where she works with companies to help adopt applied research techniques to overcome business challenges. Prior to joining Georgian, Azin was a research assistant at the University of Toronto and part of the Computer Vision Group where she was working on the intersection of Machine Learning, Transfer Learning, and Computer Vision.
Chris is a machine learning engineer at Georgian’s R&D team where he builds scalable machine learning pipelines and cloud infrastructure. Prior to this, Chris was an applied research scientist intern at Georgian focusing on AutoML and domain adaptation for NLP. He contributed to open-sourced toolkits based on these applied research areas.
Kyryl has over seven years of experience in the field of Machine Learning. For the bulk of this career, he has helped build machine learning startups, from inception to a product. He has also developed expertise in choosing and implementing state-of-the-art deep learning architectures and large-scale solutions based on them.
(Tuesday) 4:00 PM - 5:30 PM