AutoML Automated Machine Learning BootCamp (No Code ML)
AutoML Automated Machine Learning BootCamp (No Code ML), Build State of the Art Machine Learning Models without a single line of code.
“No code” machine learning (ML) refers to the use of ML platforms, tools, or libraries that allow users to build and deploy ML models without writing any code. This approach is intended to make ML more accessible to a wider range of users, including those who may not have a strong programming background.
Amazon SageMaker is a fully managed machine learning service provided by Amazon Web Services (AWS) that enables developers and data scientists to build, train, and deploy machine learning models at scale. SageMaker also includes built-in algorithms, pre-built libraries for common machine learning tasks, and a variety of tools for data pre-processing, model tuning, and model deployment. SageMaker also integrates with other AWS services to provide a complete machine learning environment.
AutoML in SageMaker refers to the automatic selection and tuning of machine learning models to improve the accuracy and performance of the models. This can be done by using SageMaker’s built-in algorithms and libraries or by using custom algorithms and libraries. SageMaker also includes a feature called Automatic Model Tuning which allows for tuning of the hyper-parameters of the models to improve their performance.
SageMaker Studio Canvas is a feature that allows users to interact with their data, build and visualize workflows, and create, run, and debug Jupyter notebooks, all within the same web-based interface. The Canvas provides a visual and interactive way to explore, manipulate and visualize data, and allows users to create Jupyter notebooks and drag-and-drop pre-built code snippets, called “recipes” to quickly perform common data pre-processing, data visualization, and data analysis tasks.
SageMaker Studio Canvas also allows users to easily share their notebooks, recipes, and data with other users and collaborate on projects. This helps to simplify the machine learning development process, accelerate the development of machine learning models, and improve collaboration among teams.
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