Amazon Sagemaker in 11 minutes | AWS скачать в хорошем качестве

Amazon Sagemaker in 11 minutes | AWS

In this video, I will give a high-level overview of Amazon Sagemaker in 11 minutes. Many data scientists develop, train, and deploy ML models within a hosted environment. Regrettably for them, they do not have the convenience and facility for scaling up or scaling down resources as and when required based on their models. This is where AWS SageMaker comes into picture! It solves the issue by facilitating developers to build and train models in order to get faster production with bare minimum efforts at an economical cost. Launched in 2017, Amazon SageMaker is a cloud-based machine-learning platform that is fully-managed and decouples your environments across developing, training and deploying, letting you scale them separately whilst helping you optimise your spend and time. AWS SageMaker includes modules that can be used together or independently to build, train, and deploy ML models at any scale by the data scientists and developers. AWS SageMaker empowers everyday developers and scientists to use machine learning without any previous experience. A whole lot of developers across the world are adopting SageMaker in various ways, some for end-to-end flow while others to scale up training jobs. A big umbrella of all the ML services, Sagemaker tries to provide one single place for all your Machine Learning and Data science workflows. It tries to cover all steps involved right from Provisioning Cloud Resources and Importing Data to Cleaning the data, Labelling the data (including manual labelling) and Training models to Automation and Deploying productionised models. FOLLOW ME ON MEDIUM:   / syal.anuj​   INSTAGRAM:   / ​   TWITTER:   / anuj_syal   GITHUB: https://github.com/syalanuj​ WEBSITE: https://anujsyal.com #sagemaker #machinelearning #deeplearning #aws #amazon #cloud #amazonsagemaker #awscloud