AWS SageMaker is a fully managed service that enables developers and data scientists to build, train, and deploy machine learning models quickly. It provides tools for every step of the machine learning workflow, including data labeling, model tuning, and deployment to production.
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About AWS SageMaker
AWS SageMaker was launched by Amazon Web Services in 2017. It was created to simplify the process of building, training, and deploying machine learning models by providing a fully managed service. The goal was to make machine learning more accessible to developers and data scientists by offering a comprehensive suite of tools within the AWS ecosystem.
Strengths of AWS SageMaker include its fully managed environment, integration with other AWS services, and scalability. Weaknesses include potential high costs and a steep learning curve for beginners. Competitors include Google Cloud AI Platform, Microsoft Azure Machine Learning, and IBM Watson Studio.
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How to hire a AWS SageMaker expert
An AWS SageMaker expert must have skills in Python programming, machine learning algorithms, and data preprocessing. They should be proficient in using SageMaker's built-in algorithms and frameworks like TensorFlow and PyTorch. Knowledge of AWS services such as S3, EC2, and IAM is also essential for managing data storage, compute resources, and security.
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