Scaling ML pipelines with KALE — the Kubeflow Automated Pipeline Engine - Salman Iqbal, Learnk8s
One of the most common hurdles with developing AI and deep learning models is to design data pipelines that can operate at scale and in real-time. Data scientists and engineers are often expected to learn, develop and maintain the infrastructure for their experiments. What’s the best setup and what’s involved in getting models being production-ready? Where do you start? In this talk, you will learn about KALE — the Kubeflow Automated Pipeline Engine. With KALE you can finally link the work done by data scientists in Jupyter Notebooks to a production-grade pipeline that trains the models at scale and serves them in real-time.