Descriptions
Build an AWS Machine Learning Pipeline for Object Detection In this course, we’ll cover all the necessary steps to build a robust and reliable machine learning pipeline, from data preprocessing to hyperparameter optimization for object detection. First, we’ll introduce you to the basics of AWS Sagemaker, a fully managed service that provides developers and data scientists with the ability to quickly and easily build, train, and deploy machine learning models. You’ll learn how to use Sagemaker to preprocess and prepare your data for machine learning, and how to use Sagemaker’s built-in algorithms to build and train your own machine learning models. Next, we’ll cover AWS Step Functions, which help you coordinate and manage the different steps of your machine learning pipeline.
You’ll learn how to build a scalable, secure, and robust machine learning pipeline using step functions, and how to use Lambda functions to trigger the different steps of your pipeline. Additionally, we’ll cover topics related to deep learning, including using neural networks for object detection and using hyperparameter tuning to optimize your machine learning models for different use cases. Finally, we’ll walk you through building a web application that interacts with your machine learning pipeline. You’ll learn how to use React, Next.js, Express, and MongoDB to build a web app that lets users send data to your pipeline, view the results, and track the progress of their jobs.
What you will learn
- Learn how to use any custom dataset you want with Google’s Open Images Dataset V7
- Create Sagemaker domains
- Upload and stream data into your Sagemaker environment
- Learn how to set up secure IAM roles in AWS
- Create a production-ready object detection algorithm
- Use Pandas and Numpy for feature and data engineering
- Understanding Object Recognition Annotations
- Visualizing images and bounding boxes with Matplotlib
- Learn how Sagemaker’s Elastic File System (EFS) works
- Use the object detection algorithm built into AWS with transfer learning
- How to set up transfer learning with VGG-16 and ResNet-50 in AWS
- Learn how to save images in RecordIO format
- Learn what the RecordIO format is
- Learn what LST files are and why we need them with object detection in AWS
Who is this course suitable for?
- For developers who want to take their machine learning skills to the next level by not only building machine learning models, but also integrating them into a complex, secure, production-ready machine learning pipeline
Specifications for building an AWS Machine Learning pipeline for object detection
- Editor: Udemy
- Teacher: Patrick Szepesi
- Language: English
- Level: Intermediate
- Number of courses: 137
- Duration: 16 hours and 18 minutes
Contents of 2023-3
Requirements
- Laptop with internet access
- AWS Account
- Knowledge of Python and basic machine learning
- Spend $20-50 on AWS if you want to follow me. Note that you can still follow along without paying any money
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6.16GB