Become a Machine Learning Engineer | Udacity

What you will learn

1. 

   AWS Machine Learning Engineer

   Estimated 5 months to complete

   You’ll 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.

   Related Nanodegree programs

   Prerequisite knowledge

   Basic knowledge of machine learning algorithms and Python programming.

   1. Introduction to Machine Learning

      In this course, you'll start learning about machine learning through high level concepts through AWS SageMaker. You'll 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.

   2. Developing Your First ML Workflow

      In this course you will learn how to 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.

   3. Deep Learning Topics within Computer Vision and NLP

      In this course you will learn how to 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. Finally, you will learn about Amazon SageMaker and you will take everything you learned and do them in SageMaker Studio.

   4. Operationalizing Machine Learning Projects on SageMaker

      This course covers advanced topics related to deploying professional machine learning projects on SageMaker. It also covers security applications. You will learn how to maximize output while decreasing costs. You will also learn how to deploy projects that can handle high traffic and how to work with especially large datasets.

   5. CAPSTONE PROJECT: Inventory Monitoring at Distribution Centers

      Distribution centers often use robots to move objects as a part of their operations. Objects are carried in bins where each bin can contain multiple objects. In this project, students will have to build a model that can count the number of objects in each bin. A system like this can be used to track inventory and make sure that delivery consignments have the correct number of items.

      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.