AWS Machine Learning Engineer

Colleagues, the AWS Machine Learning Engineer program enables you to meet the growing demand for machine learning engineers and master the job-ready skills that will take your career to new heights. master the skills necessary to become a successful ML engineer. Learn the data science and machine learning skills required to build and deploy machine learning models in production using Amazon SageMaker. Training modules with hands-on projects cover: 1) Introduction to Machine Learning In this course, you'll start learning about machine learning through high level concepts through AWS SageMaker. Create machine learning workflows, starting with data cleaning and feature engineering, to evaluation and hyperparameter tuning. Finally, you'll build new ML workflows with highly sophisticated models such as XGBoost and AutoGluon (Project: Bike Sharing Demand with AutoGluon), 2) Developing Your First ML Workflow - machine learning workflows on AWS. Learn how to monitor machine learning workflows with services like Model Monitor and Feature Store. With all this, you’ll have all the information you need to create an end-to-end machine learning pipeline (Project: Build and ML Workflow on SageMaker), 3) Deep Learning Topics within Computer Vision and NLP - train, finetune, and deploy deep learning models using Amazon SageMaker. Learn about advanced neural network architectures like Convolutional Neural Networks and BERT, as well as how to finetune them for specific tasks. Finally, you will learn about Amazon SageMaker and you will take everything you learned and do them in SageMaker Studio (Project: Image Classification using AWS SageMaker), 4) .Operationalizing Machine Learning Projects on SageMaker - deploy professional machine learning projects on SageMaker. It also covers security applications. Learn how to deploy projects that can handle high traffic and how to work with especially large datasets (Project: Operationalizing an AWS ML Project), and 5) Capstone Project - .Inventory Monitoring at Distribution Centers. To build this project, students will have to use AWS Sagemaker and good machine learning engineering practices to fetch data from a database, preprocess it and then train a machine learning model. This project will serve as a demonstration of end-to-end machine learning engineering skills that will be an important piece of their job-ready portfolio.

Enroll today (eams & execs welcome): https://tinyurl.com/yckjbdnb 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy