Rust for Large Language Model Operations (LLMOps) from Coursera

What's inside

Syllabus

DevOps Concepts for MLOps

Do you aspire to be a Rust developer at the forefront of the AI revolution? This groundbreaking 4-week course is designed specifically to train you in Large Language Model Operations (LLMOps) using Rust. This course doesn't just scratch the surface; it takes a deep dive into how you can integrate Rust with sophisticated LLM frameworks like HuggingFace Transformers. We'll also explore how to effectively deploy these large models on cloud infrastructures such as AWS, all while incorporating DevOps methodologies tailored for LLMOps.

Rust Hugging Face Candle

This week, you will delve into the powerful combination of Rust with Candle, a minimalist ML framework, and explore how they can be used with Hugging Face's popular transformer models. You will apply these concepts by working on a series of hands-on labs that guide you through building, training, and deploying machine learning models using Rust, Candle, and Hugging Face. The assessment will challenge you to create a real-world application using these tools, demonstrating your ability to apply the techniques learned in complex scenarios.

Key LLMOps Technologies

This week, you will learn how to implement state-of-the-art natural language processing models in Rust using key LLMOps technologies like Rust Bert, tch-rs, and ONNX. You will apply these skills by converting a BERT model to ONNX and deploying it in a Rust application, demonstrating proficiency in operationalizing NLP pipelines.

Key Generative AI Technologies

This week, you will learn to utilize GenAI Systems to enhance your ability to write production software and solve problems.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches foundational knowledge of Large Language Model Operations (LLMOps)

Develops skills in integrating Rust with popular LLM frameworks like HuggingFace Transformers

Provides a comprehensive understanding of LLMOps technologies like Rust Bert, tch-rs, and ONNX

Includes hands-on labs for practical experience in building and deploying machine learning models

Prepares learners for a career as a Rust developer in the AI field

Requires prior knowledge of Rust and machine learning concepts

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Rust for Large Language Model Operations (LLMOps) with these activities:

Seek Guidance from Rust and LLMOps Experts

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Enhance your learning by connecting with experiencedRust and LLMOps professionals who can provide valuable advice and support.

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Attend industry events and meetups to network with potential mentors.
Reach out to professionals on LinkedIn or other platforms, expressing your interest in mentorship.

Organize a Study Group for Rust and LLMOps

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Foster collaboration and enhance your understanding by forming a study group with peers who share your interests in Rust and LLMOps.

Browse courses on LLMOps

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Identify a group of peers who are also interested in Rust and LLMOps.
Set regular meeting times and establish a study plan that covers relevant topics.
Take turns leading discussions, presenting concepts, and facilitating group activities.

Practice Hugging Face Transformers

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Reinforce your understanding of Hugging Face Transformers by working through practice problems.

Browse courses on Transformers

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Dive into the documentation and tutorials for Hugging Face Transformers to build a solid foundation.
Solve coding challenges and participate in online forums dedicated to Hugging Face Transformers to test and refine your skills.

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Attend LLMOps Workshops or Conferences

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Expand your knowledge and skills by attending workshops or conferences dedicated to LLMOps.

Browse courses on LLMOps

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Research and identify upcoming LLMOps workshops or conferences.
Register and attend the event, actively participating in sessions and engaging with speakers and attendees.

Write a Blog Post on LLMOps Best Practices

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Deepen your understanding of LLMOps best practices by writing a blog post that shares your insights and knowledge.

Browse courses on LLMOps

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Research and gather information on various LLMOps best practices.
Organize your thoughts and outline the key points you want to cover in your blog post.
Write the content, ensuring it is well-structured, informative, and engaging.
Proofread your blog post carefully before publishing it on a platform of your choice.

Build a Speech Recognition Model

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Solidify your understanding of LLMOps by building a speech recognition model using techniques learned in the course.

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Gather a dataset of speech recordings and transcribe them to create a labeled dataset.
Train a speech recognition model using the Hugging Face Transformers library.
Evaluate the performance of your model using standard metrics.

Contribute to Open Source LLMOps Projects

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Gain practical experience and contribute to the LLMOps community by volunteering on open source projects.

Browse courses on LLMOps

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Identify open source LLMOps projects that align with your interests and skills.
Join the project's community, introduce yourself, and express your willingness to contribute.
Review the project's documentation and codebase to familiarize yourself with its goals and workings.
Identify areas where you can make meaningful contributions, such as bug fixes, feature enhancements, or documentation improvements.
Submit your contributions for review and merge, actively engaging with the project's maintainers and community.

Explore Advanced LLMOps Techniques

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Expand your knowledge of LLMOps by following guided tutorials that cover advanced techniques and best practices.

Browse courses on LLMOps

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Identify reliable online resources or training platforms that offer guided tutorials on advanced LLMOps topics.
Follow the tutorials step-by-step, experimenting with the code and techniques provided.
Seek support from online communities or forums if you encounter any challenges.

Career center

Learners who complete Rust for Large Language Model Operations (LLMOps) will develop knowledge and skills that may be useful to these careers:

Generative AI Research Engineer

Generative AI Research Engineers design, develop, and maintain generative AI algorithms and applications. This course may be useful for helping you to build a foundation in Rust, a programming language that is well-suited for developing high-performance generative AI applications.

See salaries and explore the career path for Generative AI Research Engineer

Data Science Researcher

Data Science Researchers design, develop, and maintain data science algorithms and applications. This course may be useful for helping you to build a foundation in Rust, a programming language that is well-suited for developing high-performance data science applications.

See salaries and explore the career path for Data Science Researcher

Generative AI Engineer

Generative AI Engineers design, develop, and maintain generative AI applications. This course may be useful for helping you to build a foundation in Rust, a programming language that is well-suited for developing high-performance generative AI applications.

See salaries and explore the career path for Generative AI Engineer

Software Research Engineer

Software Research Engineers design, develop, and maintain software algorithms and applications. This course may be useful for helping you to build a foundation in Rust, a programming language that is well-suited for developing high-performance software applications.

See salaries and explore the career path for Software Research Engineer

Cloud Research Engineer

Cloud Research Engineers design, develop, and maintain cloud computing algorithms and applications. This course may be useful for helping you to build a foundation in Rust, a programming language that is well-suited for developing high-performance cloud computing applications.

See salaries and explore the career path for Cloud Research Engineer

NLP Research Engineer

NLP Research Engineers design, develop, and maintain natural language processing algorithms and applications. This course may be useful for helping you to build a foundation in Rust, a programming language that is well-suited for developing high-performance NLP applications.

See salaries and explore the career path for NLP Research Engineer

Machine Learning Researcher

Machine Learning Researchers design, develop, and maintain machine learning algorithms and applications. This course may be useful for helping you to build a foundation in Rust, a programming language that is well-suited for developing high-performance machine learning applications.

See salaries and explore the career path for Machine Learning Researcher

AI Researcher

AI Researchers design, develop, and maintain AI algorithms and applications. This course may be useful for helping you to build a foundation in Rust, a programming language that is well-suited for developing high-performance AI applications.

See salaries and explore the career path for AI Researcher

NLP Engineer

NLP Engineers design, develop, and maintain natural language processing applications. This course may be useful for helping you to build a foundation in Rust, a programming language that is well-suited for developing high-performance NLP applications.

See salaries and explore the career path for NLP Engineer

AI Research Scientist

AI Research Scientists design, develop, and maintain AI algorithms and applications. This course may be useful for helping you to build a foundation in Rust, a programming language that is well-suited for developing high-performance AI applications.

See salaries and explore the career path for AI Research Scientist

Machine Learning Engineer

Machine Learning Engineers build, deploy, and maintain machine learning models. This course may be useful for helping you to build a foundation in Rust, a programming language that is well-suited for developing high-performance machine learning applications.

See salaries and explore the career path for Machine Learning Engineer

Software Engineer

Software Engineers design, develop, and maintain software applications. This course may be useful for helping you to build a foundation in Rust, a programming language that is well-suited for developing high-performance software applications.

See salaries and explore the career path for Software Engineer

DevOps Engineer

DevOps Engineers bridge the gap between development and operations teams. This course may be useful for helping you to build a foundation in Rust, a programming language that is well-suited for developing high-performance DevOps applications.

See salaries and explore the career path for DevOps Engineer

Cloud Engineer

Cloud Engineers design, build, and maintain cloud computing infrastructure. This course may be useful for helping you to build a foundation in Rust, a programming language that is well-suited for developing high-performance cloud computing applications.

See salaries and explore the career path for Cloud Engineer

Data Scientist

Data Scientists use data to solve business problems. This course may be useful for helping you to build a foundation in Rust, a programming language that is well-suited for developing high-performance data science applications.

See salaries and explore the career path for Data Scientist

Rust for Large Language Model Operations (LLMOps)

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