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Using PyTorch in the Cloud

PyTorch Playbook

Janani Ravi
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This course is no longer available. Find something similar by browsing:
Deep Learning Cloud Computing Distributed Training PyTorch GCP Machine Learning AWS

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Teaches how to deploy PyTorch models in cloud environments, including AWS, Azure, and GCP
Suitable for learners with a basic understanding of PyTorch and cloud computing
Instructors are experienced practitioners in the field of PyTorch and cloud computing
Requires learners to have access to cloud computing resources, which may incur costs

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Career center

Learners who complete Using PyTorch in the Cloud: PyTorch Playbook will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
The Machine Learning Engineer is responsible for designing, developing, deploying, and maintaining machine learning models. This course can help you become a Machine Learning Engineer by providing you with the skills and knowledge to use PyTorch, a popular deep learning framework, on each of the big three cloud providers: Amazon Web Services (AWS), Microsoft Azure, and the Google Cloud Platform (GCP).
See salaries and explore the career path for Machine Learning Engineer
Data Scientist
The Data Scientist is responsible for collecting, analyzing, and interpreting data to help businesses make better decisions. This course can help you become a Data Scientist by providing you with the skills and knowledge to use PyTorch to build deep learning models that can be used to analyze data and make predictions.
See salaries and explore the career path for Data Scientist
Deep Learning Engineer
The Deep Learning Engineer is responsible for designing, developing, and deploying deep learning models. This course can help you become a Deep Learning Engineer by providing you with the skills and knowledge to use PyTorch, a popular deep learning framework, on each of the big three cloud providers: Amazon Web Services (AWS), Microsoft Azure, and the Google Cloud Platform (GCP).
See salaries and explore the career path for Deep Learning Engineer
Cloud Architect
The Cloud Architect is responsible for designing and implementing cloud-based solutions. This course can help you become a Cloud Architect by providing you with the skills and knowledge to use PyTorch on each of the big three cloud providers: Amazon Web Services (AWS), Microsoft Azure, and the Google Cloud Platform (GCP).
See salaries and explore the career path for Cloud Architect
Software Engineer
The Software Engineer is responsible for designing, developing, and maintaining software applications. This course can help you become a Software Engineer by providing you with the skills and knowledge to use PyTorch, a popular deep learning framework, on each of the big three cloud providers: Amazon Web Services (AWS), Microsoft Azure, and the Google Cloud Platform (GCP).
See salaries and explore the career path for Software Engineer
Data Analyst
The Data Analyst is responsible for collecting, analyzing, and interpreting data to help businesses make better decisions. This course may be useful for you if you want to become a Data Analyst, as it will provide you with the skills and knowledge to use PyTorch to build deep learning models that can be used to analyze data and make predictions.
See salaries and explore the career path for Data Analyst
Business Intelligence Analyst
The Business Intelligence Analyst is responsible for collecting, analyzing, and interpreting data to help businesses make better decisions. This course may be useful for you if you want to become a Business Intelligence Analyst, as it will provide you with the skills and knowledge to use PyTorch to build deep learning models that can be used to analyze data and make predictions.
See salaries and explore the career path for Business Intelligence Analyst
Quantitative Analyst
The Quantitative Analyst is responsible for using mathematical and statistical models to analyze data and make predictions. This course may be useful for you if you want to become a Quantitative Analyst, as it will provide you with the skills and knowledge to use PyTorch to build deep learning models that can be used to analyze data and make predictions.
See salaries and explore the career path for Quantitative Analyst
Financial Analyst
The Financial Analyst is responsible for analyzing financial data to help businesses make better decisions. This course may be useful for you if you want to become a Financial Analyst, as it will provide you with the skills and knowledge to use PyTorch to build deep learning models that can be used to analyze financial data and make predictions.
See salaries and explore the career path for Financial Analyst
Investment Analyst
The Investment Analyst is responsible for analyzing investment data to help businesses make better decisions. This course may be useful for you if you want to become an Investment Analyst, as it will provide you with the skills and knowledge to use PyTorch to build deep learning models that can be used to analyze investment data and make predictions.
See salaries and explore the career path for Investment Analyst
Risk Analyst
The Risk Analyst is responsible for identifying and assessing risks to help businesses make better decisions. This course may be useful for you if you want to become a Risk Analyst, as it will provide you with the skills and knowledge to use PyTorch to build deep learning models that can be used to identify and assess risks.
See salaries and explore the career path for Risk Analyst
Operations Research Analyst
The Operations Research Analyst is responsible for using mathematical and statistical models to help businesses make better decisions. This course may be useful for you if you want to become an Operations Research Analyst, as it will provide you with the skills and knowledge to use PyTorch to build deep learning models that can be used to make better decisions.
See salaries and explore the career path for Operations Research Analyst
Management Consultant
The Management Consultant is responsible for helping businesses improve their performance. This course may be useful for you if you want to become a Management Consultant, as it will provide you with the skills and knowledge to use PyTorch to build deep learning models that can be used to analyze data and make recommendations for improvement.
See salaries and explore the career path for Management Consultant
Product Manager
The Product Manager is responsible for developing and managing products. This course may be useful for you if you want to become a Product Manager, as it will provide you with the skills and knowledge to use PyTorch to build deep learning models that can be used to analyze data and make decisions about product development.
See salaries and explore the career path for Product Manager
Marketing Manager
The Marketing Manager is responsible for developing and implementing marketing campaigns. This course may be useful for you if you want to become a Marketing Manager, as it will provide you with the skills and knowledge to use PyTorch to build deep learning models that can be used to analyze data and make decisions about marketing campaigns.
See salaries and explore the career path for Marketing Manager

Reading list

We've selected 50 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 Using PyTorch in the Cloud: PyTorch Playbook.
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Deep Learning
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Time
2 hours
Via
Pluralsight
Instructor
Janani Ravi
This course belongs to a collection called Building Deep Learning Solutions with PyTorch. It's comprised of eight courses:
  1. Foundations of PyTorch
  2. Building Your First PyTorch Solution
  3. Using PyTorch in the Cloud: PyTorch Playbook (viewing)
  4. Image Classification with PyTorch
  5. Style Transfer with PyTorch
  6. Natural Language Processing with PyTorch
  7. Expediting Deep Learning with Transfer Learning: PyTorch Playbook
  8. Deploying PyTorch Models in Production: PyTorch Playbook
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Teaches how to deploy PyTorch models in cloud environments, including AWS, Azure, and GCP
Suitable for learners with a basic understanding of PyTorch and cloud computing
Instructors are experienced practitioners in the field of PyTorch and cloud computing
Requires learners to have access to cloud computing resources, which may incur costs
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