Issue link: https://read.uberflip.com/i/1301617
5 Using Amazon SageMaker, Voodoo can decide in real time which ad to show to their players and invoke their endpoint over 100 million times by over 30 million users daily, representing close to a billion predictions per day. With AWS Machine Learning, Voodoo put an accurate model into production in less than a week, supported by a small team, and has built on top of it continuously as their team and business grow. Using TensorFlow on Amazon SageMaker, Siemens Financial Services developed an NLP model to extract critical information to accelerate investment due diligence, reducing time to summarize diligence documents from 12 hours to 30 seconds. Celgene uses Apache MXNet on Amazon SageMaker for toxicology prediction to analyze biological impacts of potential drugs virtually, without putting patients at risk. A model that previously took 2 months to train can now be trained in 4 hours. ADP uses AWS machine learning, including Amazon SageMaker, to quickly identify workforce patterns and predict outcomes before they happen, for example employee turnover or the impact of an increase in compensation. ADP reduced the time to deploy machine learning models from 2 weeks to just 1 day. Conclusions Amazon SageMaker is a fully managed ML service that lets you build, train, tune, and deploy models at scale. It eliminates the overhead costs of provisioning hardware and systems software and managing infrastructure and operations. As a result, you can focus on your business problems, develop new modeling techniques, and take ML projects from idea to production faster. The total cost of ownership of Amazon SageMaker over a three-year horizon can be 54% lower compared to other cloud-based ML options such as self-managed EC2 and managed Kubernetes (K8s) on AWS. In addition to the lower TCO, Amazon SageMaker's fully managed and integrated features let you put ML ideas into production faster and improve data scientist productivity by up to 10 times. March 2020