On a long-term goal, SnT and Amazon Web Services (AWS) would like to collaborate more together, this why we’re happy to invite you to the workshop on Computing & Machine Learning platform where AWS will present you what tools they’re offering and how to use them in your research.
✍️ Registration is required: registration link
Participants need to bring their own laptop!
This workshop will be composed of two parts:
- Understanding Data for Machine Learning: SageMaker Introduction, ML Basics,Feature Engineering for Machine Learning. Learn all about the built-in notebook instances with Amazon SageMaker-Feature Engineering and Data Labeling. Followed by a lab on SageMaker Studio Notebooks & Feature Engineering: Get hands-on experience with SageMaker Console and Jupyter Notebook. Play around code to do feature engineering of sample dataset.
SageMaker Canvas and JumpStart: Amazon SageMaker Canvas makes it easy for business analysts and domain experts to generate highly accurate ML predictions on their own without any ML experience.SageMaker JumpStart helps you easily and quickly bring machine learning applications to market Learn the fundamentals and then dive deep into these features. Followed by two labs:
– Build ML Model with No Code Using Sagemaker Canvas: Get hands on Amazon SageMaker Canvas – a Visual, No Code Machine Learning Capability for Business Analysts.
– Train, Tune and Deploy model using SageMaker Built-in Algorithm: Get hands-on experience in one of the most famous in-built ML algorithm Xgboost to build you model. Learn how you can get the best version of your machine learning model using hyperparameter tuning. Amazon SageMaker enables you to quickly and easily deploy your ML models to the most scalable infrastructure. You will learn deployment options and autoscaling for your ML models endpoint. Real time and batch inference techniques.