This post is co-written by Zdenko Estok, Cloud Architect at Accenture and Sakar Selimcan, DeepRacer SME at Accenture.
With the increasing use of artificial intelligence (AI) and machine learning (ML) for a vast majority of industries (ranging from healthcare to insurance, from manufacturing to marketing), the primary focus shifts to efficiency when building and training models at scale. The creation of a scalable and hassle-free data science environment is key. It can take a considerable amount of time to launch and configure an environment tailored for a specific use case and even harder to onboard colleagues to collaborate.
According to Accenture, companies that manage to efficiently scale AI and ML can achieve nearly triple the return on their investments. Still, not all companies meet their expected returns on their AI/ML journey. Toolkits to automate the infrastructure become essential for horizontal scaling of AI/ML efforts within a corporation.
AWS DeepRacer is

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