$200 Gift Package: 12 Free Courses on Amazon SageMaker for Machine Learning

Machine learning is rapidly integrating into business operations, becoming a cornerstone of enterprise operations. The emergence of this technology has brought many tangible impacts to various enterprises, such as improving processes, increasing efficiency, and accelerating innovation. The development of machine learning technology is changing rapidly, combined with high-performance computing options and vast amounts of data, bringing a transformative storm to organizations of all sizes.

However, like many emerging technologies, the practice and application of machine learning also face many challenges.

The machine learning workflow is a time-consuming, iterative process. From preparing data and selecting algorithms to building, training, and deploying models, as well as iterating repeatedly, this involves many steps. Decisions also need to be made regarding the infrastructure, such as selecting the appropriate computing resources for training and inference, and considering cloud, on-premises, and edge deployments.

For enterprises and developers, how can they enhance their machine learning skills and more easily get started with machine learning?

To this end, Machine Heart collaborated with Amazon Web Services last week to bring three online sharing sessions, the full review is as follows:

Session 1: Detailed Explanation of Machine Learning Practices and Industry Application Cases at Amazon

The first session was presented by Meng He, Senior Product Manager of Machine Learning at Amazon Web Services, who detailed the key resources that AWS will provide to help developer teams enhance their machine learning skills, starting from relevant application cases in the supply chain and autonomous driving fields, thus better applying AI.

Video link: https://jmq.h5.xeknow.com/s/3sRGZ3 (Click to read the original text for direct access)

Session 2: How Overseas Enterprises Quickly Build AI Applications

The second session was presented by Li Yuan, Product Manager of Machine Learning at AWS, and Wang Shishuai, Technical Expert of Machine Learning at AWS. They introduced the image and video analysis service Amazon Rekognition and the personalized recommendation service Amazon Personalize based on Amazon.com technology. They also demonstrated hands-on how to achieve user identity recognition and content review of images and videos using Amazon Rekognition; and how to provide personalized recommendations for users using Amazon Personalize.

Video link: https://jmq.h5.xeknow.com/s/4EtHRQ (Click to read the original text for direct access)

Session 3: Quickly Build and Flexibly Scale KubeFlow Machine Learning Projects Based on AWS

The third session was presented by Guo Ren, Technical Expert of Machine Learning at AWS, who detailed how to quickly build KubeFlow applications using AWS services and flexibly scale computing resources using the SageMaker Operator, making it easier to build, train, and deploy machine learning models at scale.

Video link: https://jmq.h5.xeknow.com/s/rW8Vk (Click to read the original text for direct access)

Additionally, 9 Sessions on Amazon SageMaker

Amazon SageMaker is a powerful fully managed service that helps developers and data scientists quickly build, train, and deploy machine learning (ML) models. This tool significantly eliminates the heavy lifting in the machine learning process, making it easier to develop high-quality models.

In addition to covering the entire workflow of machine learning, SageMaker has been adopted by tens of thousands of companies in a reliable and user-friendly manner, with its managed Spot bidding instance training capability potentially reducing the training costs of machine learning models by up to 90%.

To help developers quickly get started with Amazon SageMaker, Machine Heart collaborated with AWS to offer 9 free courses, covering the following topics:

• Hands-on, End-to-End, Mastering Generative Adversarial Networks (GAN)

• Simplifying Machine Learning Task Management on Kubernetes Using SageMaker Operator
• Time Series Forecasting with DeepAR
• Building Intelligent Edge with Amazon SageMaker and NVIDIA Jetson Platform
• Building a Recommendation System Based on Gluon with Amazon SageMaker
• Practical Amazon SageMaker for Chinese Named Entity Recognition with ALBERT
• Detailed Explanation of Amazon SageMaker Studio
• Building a Sentiment Analysis “Bot” with Amazon SageMaker
• DGL Graph Neural Networks and Their Practice on Amazon SageMaker

Click to read the original text, to access the video page directly.

Amazon SageMaker $200 Gift Package

We have prepared a $200 free credit for developers to personally experience Amazon SageMaker, making it easier to develop high-quality models, which is highly recommended for developers to try. Scan the QR code below to claim it.

Click to read the original text, to watch 12 free Amazon SageMaker courses.