Machine learning is inherently a highly collaborative process, where the combination of domain expertise and technical skills is the cornerstone of success, often requiring multiple iterations and experiments. Compared to research projects or prototype validations, a machine learning project that can be truly applied in a production environment needs to comprehensively consider all aspects of the workflow, including data preprocessing, framework deployment and configuration, algorithm selection and optimization, model training and hyperparameter tuning, data and model security, model interpretability for business, A/B testing of models, continuous monitoring and optimization after model deployment, requirements for model compilation in different hardware environments, management and operation of basic resources, total cost of ownership optimization, and so on. To address these issues, Amazon SageMaker, which was opened to developers in China in mid-year, is a great choice. Amazon SageMaker is a fully managed service that helps machine learning developers and data scientists quickly build, train, and deploy models. Amazon SageMaker completely eliminates the heavy lifting of various steps in the machine learning process, making it easier to develop high-quality models. To help developers get started with Amazon SageMaker faster, Machine Heart collaborated with AWS to hold six public courses in June, detailing how to use SageMaker to build Generative Adversarial Networks, run Chinese Named Entity Recognition, simplify machine learning task management on Kubernetes, and more, with over 1000 developers participating in learning and discussions. Now, Machine Heart, in collaboration with AWS, is launching public courses again, with three online sharing sessions, covering the following topics:
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Detailed Overview of Amazon SageMaker Studio
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Building a Sentiment Analysis ‘Robot’ Using Amazon SageMaker -
DGL Graph Neural Networks and Their Practice on SageMaker
October 15, First Sharing Session
Detailed Overview of Amazon SageMaker Studio
Sharing Time: October 15, 20:00-21:00
Instructor Introduction: Huang Debin, AWS Senior Solutions Architect, serves the global customer sales team, responsible for technical architecture design and consulting, dedicated to the application and deployment of enterprise-level applications on AWS cloud services, with nearly twenty years of rich experience in the development and implementation of large-scale enterprise applications.
Sharing Summary: Introduction to relevant components of Amazon SageMaker, such as Studio, Autopilot, etc., with online demonstrations showcasing how these core components enhance the efficiency of AI model development.
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