From Recipe to Chef - Become an LLM Engineer from Coursera

A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course.

In this course, you will embark on an exciting culinary adventure of Large Language Models (LLMs), from the foundational ingredients to the final deployment of your own LLM-powered app. Through each module, you'll gain hands-on experience in model training, fine-tuning, and deployment, equipping you with the skills to become a proficient LLM engineer. By the end, you’ll understand how LLMs are created, optimized, and evaluated, and how they’re applied to real-world problems.

Your learning journey will start with understanding the core principles behind LLMs, like data tokenization, training mechanisms, and the nuances of prompt engineering. As you dive deeper, you'll explore different architectures and learn how to fine-tune LLMs to suit specific needs, using techniques like transfer learning and low-rank adaptation. From there, you’ll get hands-on with deploying LLMs into production environments and building interactive applications using tools like Gradio, Streamlit, and LangChain.

Whether you’re new to AI or looking to refine your skills, this course will walk you through the process of designing and developing LLM-powered solutions. By the end, you’ll not only have built a fully functional LLM app, but you will also be ready to enter the booming field of LLM engineering with the skills and confidence to make an impact.

By the end of the course, you will be able to understand the fundamentals of LLMs, create and fine-tune your own models, evaluate their effectiveness, deploy them in real-world applications, and monitor and improve their performance over time. Additionally, you’ll have developed a strong portfolio of LLM projects to showcase your expertise.

What's inside

Syllabus

What's Cooking? Intro to LLMs

In this module, we will introduce you to the fascinating world of LLMs, tracing their evolution from rudimentary algorithms to advanced, sophisticated systems. You’ll also discover how LLMs function differently from traditional AI, and get a taste of the most popular models in use today. By the end, you'll be prepared to dive deeper into the mechanics of LLMs.

Career center

Learners who complete From Recipe to Chef - Become an LLM Engineer will develop knowledge and skills that may be useful to these careers:

LLM Engineer

The LLM Engineer role is at the forefront of developing, deploying, and maintaining large language models. This professional is responsible for the entire lifecycle of LLMs, from crafting initial prompts to fine-tuning models for specific applications and ensuring their optimal performance in production environments. The "From Recipe to Chef - Become an LLM Engineer" course directly targets this path, providing learners with hands-on experience in model training, fine-tuning, and deployment. You will gain a deep understanding of LLM fundamentals, how to evaluate their effectiveness, and build fully functional LLM-powered applications using tools like Gradio, Streamlit, and LangChain, cultivating a strong portfolio of projects essential for this booming field.

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Natural Language Processing Engineer

A Natural Language Processing Engineer specializes in developing systems that allow computers to understand, interpret, and generate human language. This role involves working with textual data, building algorithms for tasks such as sentiment analysis, translation, and text summarization, with large language models being a core component today. The "From Recipe to Chef - Become an LLM Engineer" course is exceptionally well-suited for a Natural Language Processing Engineer, as it comprehensively covers the foundational ingredients and deployment of LLMs. You will learn data tokenization, prompt engineering, and how to fine-tune models to suit specific linguistic needs, preparing you to tackle complex language-related problems and create sophisticated NLP-powered applications.

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Machine Learning Engineer

A Machine Learning Engineer focuses on designing, building, and maintaining machine learning systems. This involves everything from data preparation and model selection to deployment and monitoring. The "From Recipe to Chef - Become an LLM Engineer" course offers a specialized and highly relevant path for aspiring Machine Learning Engineers, particularly those interested in natural language processing. You will develop proficiency in core ML engineering practices such as understanding data, model training basics, and fine-tuning using techniques like transfer learning. The course's emphasis on deploying LLMs into production environments and monitoring their performance provides crucial skills directly applicable to the broader field of machine learning engineering.

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Artificial Intelligence Engineer

An Artificial Intelligence Engineer designs and implements AI solutions across various domains, focusing on building intelligent systems that can learn, reason, and make decisions. This role involves selecting appropriate AI techniques, developing algorithms, and integrating AI into broader software systems. The "From Recipe to Chef - Become an LLM Engineer" course equips aspiring Artificial Intelligence Engineers with deep expertise in one of AI's most impactful areas: large language models. Learners will understand the core principles behind LLMs, explore different architectures, and gain hands-on experience in building and deploying LLM-powered applications, positioning them to innovate and solve real-world problems with advanced AI.

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Prompt Engineer

A Prompt Engineer specializes in crafting, refining, and optimizing inputs for large language models to elicit the most accurate, relevant, and desirable outputs. This role requires a deep understanding of how LLMs process information and respond to various instructions. The "From Recipe to Chef - Become an LLM Engineer" course dedicates a significant module to prompt engineering, explicitly teaching different prompting styles, how to craft the best prompts for specific tasks, and strategies to evaluate their effectiveness. This direct focus on prompt engineering, combined with an understanding of LLM mechanics and evaluation, provides an unparalleled foundation for anyone pursuing a career as a Prompt Engineer.

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Machine Learning Operations Engineer

A Machine Learning Operations Engineer is crucial for integrating machine learning models into production systems, ensuring their reliable, scalable, and efficient operation. This involves managing the full lifecycle from development to deployment and continuous monitoring. The "From Recipe to Chef - Become an LLM Engineer" course significantly aids an aspiring Machine Learning Operations Engineer by covering critical aspects like deploying LLMs into production environments and monitoring their performance over time. You will learn about wrapping models in APIs, choosing hosting platforms, scaling, and counteracting model drift, providing essential skills for maintaining robust and responsive LLM-powered applications in the real world.

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Software Engineer with Artificial Intelligence Focus

A Software Engineer with Artificial Intelligence Focus designs, develops, and maintains software applications that integrate AI capabilities. This role blends traditional software development principles with specialized knowledge of AI systems, particularly in building intelligent features and user experiences. The "From Recipe to Chef - Become an LLM Engineer" course offers a robust pathway for a Software Engineer with Artificial Intelligence Focus by teaching how to build fully functional LLM-powered applications. Learners gain hands-on experience with tools like Gradio, Streamlit, and LangChain for creating interactive applications, enabling them to confidently integrate sophisticated LLM functionalities into diverse software solutions.

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Data Scientist specializing in Natural Language Processing

A Data Scientist specializing in Natural Language Processing applies statistical methods, machine learning, and computational linguistics to analyze and extract insights from textual data. This role often involves developing models for text classification, entity recognition, and information retrieval. The "From Recipe to Chef - Become an LLM Engineer" course may be helpful for a Data Scientist specializing in Natural Language Processing. While less focused on statistical analysis, it provides a deep understanding of LLM mechanics, data preparation including tokenization, and critically, how to evaluate LLM effectiveness and address biases. This knowledge empowers data scientists to better leverage, interpret, and troubleshoot large language models in their analytical work. This role typically requires an advanced degree.

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Solutions Architect specializing in Artificial Intelligence

A Solutions Architect specializing in Artificial Intelligence designs comprehensive technical solutions that leverage AI technologies to meet business needs. This involves understanding client requirements, selecting appropriate AI models and platforms, and outlining the system architecture for deployment and integration. The "From Recipe to Chef - Become an LLM Engineer" course provides valuable insights for a Solutions Architect specializing in Artificial Intelligence by covering the entire LLM lifecycle. You will learn about LLM fundamentals, deployment strategies, and building LLM-powered apps, enabling you to make informed decisions about model selection, optimization, and scalable real-world application of large language models within complex enterprise architectures.

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Artificial Intelligence Research Scientist

An Artificial Intelligence Research Scientist explores new frontiers in AI, developing novel algorithms, models, and theories to advance the field. This role typically involves conducting experiments, publishing findings, and contributing to cutting-edge AI technologies. The "From Recipe to Chef - Become an LLM Engineer" course may be useful for an Artificial Intelligence Research Scientist. While it is more focused on practical engineering, understanding the hands-on process of LLM training, fine-tuning, evaluation, and real-world deployment can provide valuable practical context for theoretical research. This course helps bridge the gap between abstract concepts and applied challenges, especially concerning model effectiveness and addressing biases. This role typically requires an advanced degree.

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Technical Product Manager for Artificial Intelligence Products

A Technical Product Manager for Artificial Intelligence Products guides the strategy, roadmap, and feature definition for AI-powered products. This role requires a blend of business acumen, technical understanding of AI, and strong communication skills to bridge the gap between development teams and market needs. The "From Recipe to Chef - Become an LLM Engineer" course may be helpful for a Technical Product Manager for Artificial Intelligence Products. By gaining a robust understanding of how LLMs are created, optimized, evaluated, and deployed, product managers can make informed decisions about product capabilities, assess technical feasibility, anticipate challenges like model drift, and effectively communicate the value and limitations of LLM-powered solutions.

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Data Engineer specializing in Machine Learning Data

A Data Engineer specializing in Machine Learning Data builds and maintains the infrastructure and pipelines for collecting, processing, and transforming large datasets required to train and operate machine learning models. This role ensures data quality, accessibility, and readiness for ML workflows. The "From Recipe to Chef - Become an LLM Engineer" course may be useful for a Data Engineer specializing in Machine Learning Data. The module "Ingredients Matter – Understanding Data" specifically covers tokenization, diverse datasets, and the impact of biases, providing critical context for preparing high-quality data. Understanding how data is consumed by LLMs helps these engineers design more effective and efficient data pipelines, directly impacting model performance and fairness.

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Cloud Engineer specializing in Machine Learning Infrastructure

A Cloud Engineer specializing in Machine Learning Infrastructure designs, implements, and manages the cloud-based platforms and services that support the training, deployment, and scaling of machine learning models. This role ensures the underlying compute, storage, and networking resources are optimized for ML workloads. The "From Recipe to Chef - Become an LLM Engineer" course may be helpful for a Cloud Engineer specializing in Machine Learning Infrastructure. While not focused on cloud specifics, it provides crucial insights into the hardware required for training LLMs at scale and the considerations for hosting and scaling models for wide-reaching use. This understanding helps them design robust, efficient, and cost-effective cloud solutions for LLM development and deployment.

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User Experience Designer for Artificial Intelligence Applications

A User Experience Designer for Artificial Intelligence Applications focuses on creating intuitive, effective, and ethically sound user interfaces and experiences for products powered by AI. This involves understanding how users interact with intelligent systems and designing interfaces that effectively communicate AI capabilities and limitations. The "From Recipe to Chef - Become an LLM Engineer" course may be useful for a User Experience Designer for Artificial Intelligence Applications. Understanding LLM fundamentals, prompt engineering for desired outputs, and how to detect common model errors like hallucinations provides critical insight. This knowledge helps designers create more robust user experiences, manage user expectations, and build interactive applications using tools like Gradio or Streamlit.

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Technical Writer for Artificial Intelligence Documentation

A Technical Writer for Artificial Intelligence Documentation creates clear, concise, and accurate documentation for AI products, APIs, and internal systems. This role translates complex technical concepts into understandable guides for developers, users, and stakeholders. The "From Recipe to Chef - Become an LLM Engineer" course may be useful for a Technical Writer for Artificial Intelligence Documentation. The comprehensive journey from LLM fundamentals to deployment and monitoring, including deep dives into topics like data tokenization, prompt engineering, and evaluation metrics, provides a solid foundation. This detailed understanding enables writers to articulate the intricate workings and applications of LLMs with precision and clarity, ensuring high-quality and informative documentation.

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