Watch this webinar hosted by Anyscale, the company behind Ray, the unified framework for scalable computing, and Prefect, the modern data workflow and observability for your data science. Learn how Ray and Prefect combine to provide orchestration and observability for your data workflow at any scale easily, and hear a live case study from LiveEO, the satellite based infrastructure monitoring service, on how they use Prefect and Anyscale to streamline their workloads for their customers.
Learn about key aspects of ML scaling:
Bridge the gap between development and production
Understand how to scale ML workloads from your laptop to the cloud with no code changes. With a single script prepare data, tune, train and scale your workloads.
Scale across multiple dimensions
Hear how organizations are benefitting from embarrassingly parallel experiments and scaling across multiple cores, nodes, and data sources
Increase developer velocity and speed experimentation
See how to speed model development and iterations without scaling complexity. Visualize, optimize, collaborate and standardize models and data pipelines.
Orchestration as a service
Operationalize, automate and manage your workflows without having to worry about moving your data.
Increase visibility across all of your data and ML workflows.
Alex is the Technical Integrations Lead within the Engineering team at Prefect. Alex is a full stack engineer with broad experience across the data ecosystem, including at Capital One as a software engineer, as the CTO of a startup, lead platform engineer at a consulting firm and at Prefect.
Toby Rahloff is the Head of Solution Architecture at LiveEO, a SaaS company that provides predictive maintenance solutions to utility companies using satellite imagery and machine learning. He recently bootstrapped the DataOps team at LiveEO, which is responsible for designing and implementing data pipelines and high-performance compute clusters with outstanding DevEx for geospatial and machine learning engineers.
Phi has been working with Fortune 500 customers in Retail, CPG, HCLS, Financial services and startups to accelerate their machine learning practices. This includes a wide range of engagements such as helping teams organize and build a center of excellence for ML, MLOps processes and automation, ML use cases development and feasibility to providing cloud best practices combining Ray and public cloud such as AWS and GCP or open source projects running on Kubernetes.