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Introduction to Amazon SageMaker Ground Truth
AWS
AWS
Are you struggling with clean data labeling for your machine learning data sets? Amazon SageMaker Ground Truth helps you with automatic labeling and providing a managed experience for your end to end data labeling jobs.
Read more
Are you struggling with clean data labeling for your machine learning data sets? Amazon SageMaker Ground Truth helps you with automatic labeling and providing a managed experience for your end to end data labeling jobs.
Read more
Are you struggling with clean data labeling for your machine learning data sets? Amazon SageMaker Ground Truth helps you with automatic labeling and providing a managed experience for your end to end data labeling jobs.
Are you struggling with clean data labeling for your machine learning data sets? Amazon SageMaker Ground Truth helps you with automatic labeling and providing a managed experience for your end to end data labeling jobs. This lesson will explain the basics and provide a quick demo showing these capabilities; allow you to decide if Amazon SageMaker Ground Truth will work for your environment.
Register for this course and see more details by visiting:
OpenCourser.com/course/3rgrv5/introduction
What's inside
Syllabus
Introduction to Amazon SageMaker Ground Truth
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Guides learners through the process of data labeling for machine learning data sets
Demonstrates how to use Amazon SageMaker Ground Truth for comprehensive data labeling
Appropriate for learners of varying levels of experience with data labeling
Instructors are recognized for expertise in the field of data labeling
Course materials focus on a niche topic, which may not be suitable for all
Could be more widely applicable if it covered a broader range of data labeling techniques
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Introduction to Amazon SageMaker Ground Truth.
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Career center
Learners who complete Introduction to Amazon SageMaker Ground Truth will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts who require automatic data labeling and an experience to manage their end-to-end data labeling jobs, for machine learning datasets, will find Amazon SageMaker Ground Truth useful. This platform can help build a foundation for a Data Analyst who seeks to automate some tasks of a large-scale data labeling task by organizing, training, and evaluating human labelers.
Data Scientist
Data Scientist
Data Scientists who require automatic data labeling and an experience to manage their end-to-end data labeling jobs, for machine learning datasets, will find Amazon SageMaker Ground Truth useful. This platform can help build a foundation for a Data Scientist who seeks to automate some tasks of a large-scale data labeling task by organizing, training, and evaluating human labelers.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for the development and deployment of Machine Learning models. Amazon SageMaker Ground Truth can help build a foundation for a Machine Learning Engineer, as many of their duties include organizing, training, and evaluating human labelers to collect large samples of data to train a Machine Learning model.
Data Engineer
Data Engineer
Data Engineers are responsible for building and maintaining the infrastructure and pipelines that move data around an organization. Amazon SageMaker Ground Truth can help build a foundation for a Data Engineer, as it can be used to automate some tasks of a large-scale data labeling task by organizing, training, and evaluating human labelers.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts collect, analyze, and interpret data to help businesses improve their performance and make data-driven decisions. Amazon SageMaker Ground Truth can help build a foundation for a Business Intelligence Analyst, as it can be used to automate some tasks of a large-scale data labeling task by organizing, training, and evaluating human labelers.
Software Engineer
Software Engineer
This course may be useful for Software Engineers who require automatic data labeling and an experience to manage their end-to-end data labeling jobs, for machine learning datasets. Amazon SageMaker Ground Truth can help build a foundation for a Software Engineer who seeks to automate some tasks of a large-scale data labeling task by organizing, training, and evaluating human labelers.
Product Manager
Product Manager
This course may be useful for Product Managers who require automatic data labeling and an experience to manage their end-to-end data labeling jobs, for machine learning datasets. Amazon SageMaker Ground Truth can help build a foundation for a Product Manager who seeks to automate some tasks of a large-scale data labeling task by organizing, training, and evaluating human labelers.
Cloud Architect
Cloud Architect
This course may be useful for Cloud Architects who require automatic data labeling and an experience to manage their end-to-end data labeling jobs, for machine learning datasets. Amazon SageMaker Ground Truth can help build a foundation for a Cloud Architect who seeks to automate some tasks of a large-scale data labeling task by organizing, training, and evaluating human labelers.
Data Architect
Data Architect
This course may be useful for Data Architects who require automatic data labeling and an experience to manage their end-to-end data labeling jobs, for machine learning datasets. Amazon SageMaker Ground Truth can help build a foundation for a Data Architect who seeks to automate some tasks of a large-scale data labeling task by organizing, training, and evaluating human labelers.
Systems Analyst
Systems Analyst
This course may be useful for Systems Analysts who require automatic data labeling and an experience to manage their end-to-end data labeling jobs, for machine learning datasets. Amazon SageMaker Ground Truth can help build a foundation for a Systems Analyst who seeks to automate some tasks of a large-scale data labeling task by organizing, training, and evaluating human labelers.
Business Analyst
Business Analyst
This course may be useful for Business Analysts who require automatic data labeling and an experience to manage their end-to-end data labeling jobs, for machine learning datasets. Amazon SageMaker Ground Truth can help build a foundation for a Business Analyst who seeks to automate some tasks of a large-scale data labeling task by organizing, training, and evaluating human labelers.
Database Administrator
Database Administrator
This course may be useful for Database Administrators who require automatic data labeling and an experience to manage their end-to-end data labeling jobs, for machine learning datasets. Amazon SageMaker Ground Truth can help build a foundation for a Database Administrator who seeks to automate some tasks of a large-scale data labeling task by organizing, training, and evaluating human labelers.
IT Manager
IT Manager
This course may be useful for IT Managers who require automatic data labeling and an experience to manage their end-to-end data labeling jobs, for machine learning datasets. Amazon SageMaker Ground Truth can help build a foundation for an IT Manager who seeks to automate some tasks of a large-scale data labeling task by organizing, training, and evaluating human labelers.
Data Governance Specialist
Data Governance Specialist
This course may be useful for Data Governance Specialists who require automatic data labeling and an experience to manage their end-to-end data labeling jobs, for machine learning datasets. Amazon SageMaker Ground Truth can help build a foundation for a Data Governance Specialist who seeks to automate some tasks of a large-scale data labeling task by organizing, training, and evaluating human labelers.
Project Manager
Project Manager
This course may be useful for Project Managers who require automatic data labeling and an experience to manage their end-to-end data labeling jobs, for machine learning datasets. Amazon SageMaker Ground Truth can help build a foundation for a Project Manager who seeks to automate some tasks of a large-scale data labeling task by organizing, training, and evaluating human labelers.
For more career information including salaries, visit:
OpenCourser.com/course/3rgrv5/introduction
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Introduction to Amazon SageMaker Ground Truth.
Save
Comprehensive guide to deep learning, covering the latest advances in the field. It provides a deep understanding of the fundamental concepts of deep learning, as well as practical tips on how to build and train deep learning models.
Provides a comprehensive overview of deep learning for natural language processing, covering topics such as text classification, machine translation, and question answering. It great resource for anyone looking to learn more about this field.
Save
Provides a comprehensive overview of computer vision, covering topics such as image processing, feature extraction, and object recognition. It great resource for anyone looking to learn more about this field.
Provides a comprehensive overview of speech and language processing, covering topics such as speech recognition, natural language understanding, and machine translation. It great resource for anyone looking to learn more about this field.
Provides a comprehensive overview of pattern recognition and machine learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It great resource for anyone looking to learn more about this field.
Provides a comprehensive overview of machine learning using Python. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. It great resource for anyone looking to learn more about machine learning using Python.
Provides a comprehensive overview of machine learning using R. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. It great resource for anyone looking to learn more about machine learning using R.
Hands-on guide to data science, covering the entire data science pipeline from data collection to model deployment. It great resource for anyone looking to learn the basics of data science.
Practical guide to machine learning using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It provides a comprehensive overview of machine learning techniques and algorithms.
Provides a gentle introduction to machine learning using Python. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. It great resource for anyone looking to get started with machine learning.
Provides a very gentle introduction to machine learning. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. It great resource for anyone who is completely new to machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/3rgrv5/introduction
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Time
11 hours
Level
Introductory
Via
Pluralsight
Instructor
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Language
English
Good to know
Guides learners through the process of data labeling for machine learning data sets
Demonstrates how to use Amazon SageMaker Ground Truth for comprehensive data labeling
Appropriate for learners of varying levels of experience with data labeling
Instructors are recognized for expertise in the field of data labeling
Course materials focus on a niche topic, which may not be suitable for all
Could be more widely applicable if it covered a broader range of data labeling techniques
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