PluralSight – Large Language Models (LLMs) for Data Professionals
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This learning path focuses on teaching data professionals the foundational knowledge they need to start incorporating Large Language Models (LLMs) into their workflows. The courses in this path will teach a variety of topics ranging from pre-trained LLMs to real-world applications of LLMs. Whether you’re just getting into LLMs or already experienced using them, this learning path will help elevate your knowledge to the next level.
Introduction to Large Language Models for Data Practitioners
By Russ Thomas
Intermediate
Jan 4, 2024
Description
Just when we had gotten used to tools like Alexa, Siri, and Cortana, applications such as ChatGPT suddenly reset the world’s expectations of what is possible with AI. Powering this massive technological leap in terms of natural language processing are large language models. In this course, Introduction to Large Language Models for Data Practitioners, you’ll be introduced as a data practitioner to what you need to know about large language models (LLMs) and how you can leverage them moving forward. First, you’ll explore the evolution of LLMs over the past 70 years, from the first conceptual neural network through advancements in machine learning, deep learning, and the development of various language model architectures, to the transformer-based LLMs now emerging for general use such as PaLM, Claude, LLaMA, and GPT. Next, you’ll discover what makes the transformer model so revolutionary by learning more about the internal workings of the model – defining concepts and terms critical to transformer-based LLMs such as parameters, encoding, decoding, attention, weights, training, and tuning. Finally, you’ll see where LLMs fit within the domain of primitive objects available to data practitioners to solve real-world problems, obtain confidence in understanding the power and limitations of these models, discuss ethical considerations and harmful biases, and demonstrate improving accuracy and relevancy through fine tuning and feedback vs. feedforward approaches in machine learning. When you’re finished with this course, you’ll have the knowledge necessary to determine where LLMs fit in your domain, the ability to conceptualize how LLMs work, and, more importantly, what capabilities and limitations this presents to you as you prepare to implement LLMs into your toolkit moving forward.
Build Solutions with Pre-trained LLMs
By Ria Cheruvu
Intermediate
Jan 10, 2024
Description
Large language models (LLMs) are changing the way we can interact with data, creating new interfaces for us to question and explore different forms of data, such as the internet, email, healthcare records, and more via a textual format. The field of LLMs continues to evolve rapidly, and it can be challenging to identify where to get started building solutions with LLMs and taking advantage of this revolutionary technology. In this course, Build Solutions with Pre-trained LLMs, you’ll gain the ability to implement different pre-trained language models with popular tools and frameworks, including how to customize (fine-tune), deploy, and build solutions using the models. First, you’ll explore what makes large language models big and efficient, gaining hands-on experience with popular pre-trained LLMs and using them to solve real-world problems. Then, you’ll dive into practical tools for working with pre-trained LLMs, including the HuggingFace, TensorFlow, and PyTorch libraries. Next, you’ll discover how to fine-tune pre-trained LLMs for specific tasks or domains, gaining the skills to effectively describe techniques for adapting model architectures and implementing fine-tuning. Finally, you’ll learn about the pitfalls and challenges of working with pre-trained LLMs and how to overcome them. When you’re finished with this course, you’ll have the skills and knowledge needed to implement pre-trained LLMs effectively, and confidently be able to build solutions using LLMs to solve real-world problems.
Advanced Text Generation and Analysis with LLMs
By Russ Thomas
Intermediate
Feb 8, 2024
Description
Moving beyond a hobby project with a large language model (LLM) to a real-world application often requires advanced techniques to ensure your solution is production-ready, and that it will remain contextually effective under scrutiny and scale. In this course, Advanced Text Generation and Analysis with LLMs, you’ll gain the ability to assess, monitor, and guide the text generation of your LLM. First, you’ll look at advanced techniques to ensure your LLM maintains relevancy and coherence within a domain, increasing its fluency while maintaining context. Then, you’ll explore additional techniques to increase your LLM’s ability to identify and generate intent, sentiment, and tone in both pure natural language processing environments as well as multimodal applications such as video, image, and audio AI applications. Next, you’ll see how to guide the output of your LLMs, researching prompt engineering – an especially critical concept with conversational models or question/answer applications – as well as demonstrating techniques to guide both a model’s diversity and style. Finally, you’ll look at the various methods researchers have created to assess and monitor the effectiveness of an LLM model, introducing concepts such as precision and recall as well as popular metrics such as BLEU and ROUGE. By the end of this course, you’ll be prepared to utilize large language models in a real-world setting, with increased confidence in your model’s ability to accurately perform within the context of its intended application.
Optimize LLMs for Specific Business Needs
By Daryle Serrant
Intermediate
Jan 19, 2024
Description
Since the release of ChatGPT, Dolly, PaLM, and other large language models (LLMs), an increasing number of companies are seeking to leverage these technologies to address business-specific or industry-specific problems. A challenge many organizations face is optimizing LLMs to adequately meet unique demands while also ensuring ethical compliance. In this course, Optimize LLMs for Specific Business Needs, you will gain the ability to tailor LLMs to accomplish specific business challenges and objectives. First, you’ll explore the role of LLMs in addressing business opportunities and learn how they can be tailored to meet industry requirements. Next, you’ll discover the intricacies involved in adapting, fine-tuning, and optimizing models for specific use cases, including methods for integrating domain-specific knowledge into LLMs and several techniques for optimizing computation and memory utilization of these models in production environments. Finally, you’ll learn testing and validation strategies for LLMs in business applications and how to continuously refine LLMs based on real world-usage. When you’re finished with this course, you’ll have the skills and knowledge of LLMs needed to effectively integrate these tools into your organization.
LLMs in Action: Real-world Applications
By Amber Israelsen
Intermediate
Dec 1, 2023
Description
By now, you’ve probably used large language models (LLMs) like ChatGPT, Bing, or Bard to write emails, generate code, and maybe even do some creative writing. But beyond those obvious examples, what are some other practical use cases for LLMs? In this course, LLMs in Action: Real-world Applications, you’ll gain an understanding of how LLMs are being used today across a variety of industries. First, you’ll explore applications of LLMs in healthcare, the financial sector, e-commerce and retail, and media and entertainment. Next, you’ll discover innovative and novel ways that LLMs are being used, and how cross-industry adoption can facilitate creative problem-solving. Finally, you’ll explore recent success stories and the impacts of this transformative technology. When you’re finished with this course, you’ll have the skills and knowledge of LLMs needed to apply them in a practical way across a variety of fields.
Ensure the Ethical Use of LLMs in Data Projects
By Kesha Williams
Intermediate
Jan 8, 2024
Description
In the rapidly evolving field of AI, the ethical use of Large Language Models (LLMs) in data projects presents a critical challenge. With the increasing reliance on LLMs for decision-making and content generation, there’s a growing concern about biases and their societal impacts. In this course, Ensure the Ethical Use of LLMs in Data Projects, you will learn to implement LLMs responsibly and ethically in various data-driven projects. First, you’ll explore the ethical dimensions and inherent biases of LLMs. This includes understanding how biases manifest in LLM-generated content and decisions and the potential societal impacts of these biases. Next, you’ll discover practical strategies for detecting and mitigating biases in LLMs. Finally, you’ll learn how to effectively engage with stakeholders and communicate the ethical considerations of LLMs. When you’re finished with this course, you’ll have the skills and knowledge of ethical LLM implementation needed to ensure responsible and fair use of these powerful tools in your data projects.
Scale and Deploy LLMs in Production Environments
By Abhishek Kumar
Intermediate
Mar 28, 2024
Description
Despite all the promise and potential of Large Language Models (LLMs), leveraging these LLMs in a production environment is complex and challenging. In this course, Scale and Deploy LLMs in Production Environments, you’ll gain the ability to create and customize blueprints for deploying LLMs in a scalable, cost-effective, and secure way that aligns with your enterprise goals. First, you’ll explore different approaches to LLM deployment in production and customize the deployment blueprint based on your use case and enterprise needs. Next, you’ll discover considerations related to monitoring and maintaining LLMs in production. Finally, you’ll learn how to integrate LLMs into your enterprise ecosystem safely and securely. When you’re finished with this course, you’ll have the skills and knowledge of constructing LLM deployment blueprints needed to use them in a production environment that is scalable, secure, and responsible.
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