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Updated in [February 21st, 2023]

Learners of this course will gain a comprehensive understanding of PyTorch and deep learning, and how to use them to create business value. They will learn about the most common use cases of AI in the industry, and how PyTorch's ecosystem and the commoditization of deep learning models can help them integrate them into their business. They will also learn why ensuring data quality is critical for the successful deployment of AI applications, and why getting the right data should be the top priority for any AI project. Learners will gain an understanding of the trade-offs involved in choosing the appropriate model for the task at hand, such as build vs. buy, black vs. white box, and the risk and cost of delivering wrong predictions. They will also learn about the inherent limitations of AI models, the mitigation of risks and vulnerabilities, and the challenge of data privacy. Finally, learners will gain an understanding of the Python programming language and its use in data science and machine learning.

[Applications]
Upon completion of LFS116x, participants should be able to identify the most suitable use cases for AI in their organization, understand the trade-offs involved in choosing the appropriate model for the task at hand, and be able to make informed decisions about the development and maintenance of AI projects. Additionally, participants should be aware of the inherent limitations of AI models, the mitigation of risks and vulnerabilities, and the challenge of data privacy.

[Career Paths]
1. AI Engineer: AI Engineers are responsible for developing and deploying AI applications. They use deep learning frameworks such as PyTorch to build and train models, and use them to automate and optimize processes. AI Engineers must have a strong understanding of the AI landscape, and be able to identify the most appropriate model for the task at hand. AI Engineers must also be aware of the risks and vulnerabilities associated with AI applications, and be able to mitigate them.

2. Data Scientist: Data Scientists are responsible for collecting, cleaning, and analyzing data to identify patterns and trends. They use a variety of tools and techniques to extract insights from data, and use them to inform decision-making. Data Scientists must have a strong understanding of data science principles and be able to identify the most appropriate model for the task at hand.

3. Machine Learning Engineer: Machine Learning Engineers are responsible for developing and deploying machine learning models. They use deep learning frameworks such as PyTorch to build and train models, and use them to automate and optimize processes. Machine Learning Engineers must have a strong understanding of the AI landscape, and be able to identify the most appropriate model for the task at hand. They must also be aware of the risks and vulnerabilities associated with AI applications, and be able to mitigate them.

4. AI Product Manager: AI Product Managers are responsible for managing the development and deployment of AI applications. They use deep learning frameworks such as PyTorch to build and train models, and use them to automate and optimize processes. AI Product Managers must have a strong understanding of the AI landscape, and be able to identify the most appropriate model for the task at hand. They must also be aware of the risks and vulnerabilities associated with AI applications, and be able to mitigate them.