Recent trends in data and machine learning technologies

Machine learning involves a lot of experimentation. Data scientists spend days, weeks, or months performing algorithm searches, model architecture searches, hyperparameter searches, etc. Shashank Prasanna breaks down how you can easily run large-scale machine learning experiments using containers, Kubernetes, Amazon ECS, and SageMaker.

ML has been advancing rapidly, but only a few contributors focus on the infrastructure and scaling challenges that come with it. Jonathan Peck explores why ML is a natural fit for serverless computing, a general architecture for scalable ML, and common issues when implementing on-demand scaling over GPU clusters, providing general solutions and a vision for the future of cloud-based ML.

Sophie Watson and William Benton demonstrate high-level open source tools that build on Kubernetes to solve machine learning workflow pain points. They explain why Kubernetes is great for ML and present tools that effortlessly provision custom research environments, publish reproducible notebooks, operationalize models and pipelines as services, and detect data drift automatically.

ML has been advancing rapidly, but only a few contributors focus on the infrastructure and scaling challenges that come with it. Jonathan Peck explores why ML is a natural fit for serverless computing, a general architecture for scalable ML, and common issues when implementing on-demand scaling over GPU clusters, providing general solutions and a vision for the future of cloud-based ML.

Charlotte Werger outlines the components necessary to transform a traditional wealth manager into a data-driven business, paying special attention to devising and executing a transformation strategy by identifying key business subunits where automation and improved predictive modeling can result in significant gains and synergies.

Robert Cohen discusses the skills that employers are seeking from employees in digital jobs, linked to the new software hierarchy driving digital transformation. Robert describes this software hierarchy as one that ranges from DevOps, CI/CD, and microservices to Kubernetes and Istio. This hierarchy is used to define the jobs that are central to data-driven digital transformation.