AWS Machine Learning Engineer

Colleagues, according to Glassdoor the average salary for a Machine Learning Engineer is $123,524. The AWS Machine Learning Engineer program from Udacity enables you to master the skills necessary to become a successful ML engineer. The skill-based training modules - each with a hands-on project - include: 1) Introduction to Machine Learning - begin by using SageMaker Studio to perform exploratory data analysis. Know how and when to apply the basic concepts of machine learning to real world scenarios. 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: Predict Bike Sharing Demand with AutoGluon); 2) Developing Your First ML Workflow - create general machine learning workflows on AWS. You’ll begin with an introduction to the general principles of machine learning engineering. From there, you’ll learn the fundamentals of SageMaker to train, deploy, and evaluate a model. Following that, you’ll learn how to create a machine learning workflow on AWS utilizing tools like Lambda and Step Functions. Finally, you’ll 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 an ML Workflow on SageMaker); 3) Deep Learning Topics within Computer Vision and NLP - train, finetune, and deploy deep learning models using Amazon SageMaker. You’ll begin by learning what deep learning is, where it is used, and which tools are used by deep learning engineers. Next we will learn about artificial neurons and neural networks and how to train them. After that we will learn about advanced neural network architectures like Convolutional Neural Networks and BERT, as well as how to finetune them for specific tasks (Project: Image Classification using AWS SageMaker); 4) Operationalizing Machine Learning Projects on SageMaker - deploying professional machine learning projects on SageMaker. It also covers security applications. You will learn how to maximize output while decreasing costs 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. 

Enroll today (teams & execs welcome): https://tinyurl.com/5y4ac8mj

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Much career success, Lawrence E. Wilson - Artificial Intelligence Academy (share & subscribe)