Скачать с ютуб Stephen McCullough: Leveraging AWS SageMaker for Scalable AI Deployment with Python в хорошем качестве

Stephen McCullough: Leveraging AWS SageMaker for Scalable AI Deployment with Python

Stephen McCullough: Leveraging AWS SageMaker for Scalable AI Deployment with Python: A Real-World Journey The power of artificial intelligence (AI) has been unleashed across various domains, driving numerous innovative solutions. However, the process of building, training, and deploying machine learning models at scale can be a complex task. This talk will focus on how Python, as a leading programming language in the AI field, can interact with AWS SageMaker to streamline this process and make scalable AI deployment achievable. During this session, I will take the audience on a journey through the world of AWS SageMaker, an integrated platform designed to assist developers and data scientists to build, train, and deploy machine learning models. I will focus on how Python can be utilised to maximise the benefits of SageMaker, demonstrating its use through real-world examples. In this presentation, I will cover: Introduction to AWS SageMaker: Understand the fundamental features of SageMaker and why it is a potent tool for AI and ML development. Python & SageMaker: Dive into the synergies between Python and SageMaker. Learn how to write Python scripts that harness the power of SageMaker for model training, evaluation, and deployment. Real-world Case Studies: Walk through a series of real-world use cases that illustrate the successful application of SageMaker in Python-based AI projects. Best Practices and Pitfalls: Gain insights into the best practices when using Python with SageMaker, and learn about common challenges and how to overcome them. Future of AI with Python and SageMaker: Discussion around the potential advancements and improvements we can anticipate in the AI world, with Python and AWS SageMaker at its core. Ground Floor Room 2 Sat 10:35 am - 11:10 am