Harnessing LLMs: Strategy, Fine-Tuning & Evaluation

In the age of artificial intelligence (AI), it is essential to learn how to apply the power of large language models (LLMs) for building various production-ready applications. In this hands-on-course, learners will gain the necessary skills for building and responsibly deploying a conversational AI application. Following the demo provided in this course, learners will learn how to develop a FAQ chatbot using HuggingFace, Python, and Gradio. Core concepts from applying prompt engineering to extract the most value from LLMs to infrastructure, monitoring, and security considerations for real-world deployment will be covered. Important ethical considerations such as mitigating bias, ensuring transparency, and maintaining user trust will also be covered to help learners understand the best practices in developing a responsible and ethical AI system. By the end, learners will have developed familiarity with both the technical and human aspects of building impactful LLM applications. The learners can design, develop, and deploy production-ready applications powered by Large Language Models. This course is designed for individuals with a basic understanding of programming and application development concepts. It is suitable for developers, data scientists, AI enthusiasts, and anyone interested in using LLMs to build practical applications. you need basic concepts, software tools, and an internet-connected computer.

This course demystifies the concept of "LLM fine-tuning" and its critical applications in the business world. In the context of rapidly evolving AI technologies, understanding how to fine-tune Large Language Models (LLMs) is essential for businesses to stay competitive. The course covers foundational concepts, the background of LLMs, current uses in various industries, and a glimpse into future possibilities. Through real-life examples, learners will see how fine-tuning LLMs can lead to more efficient, personalized, and innovative business solutions. Main Outcome and Takeaways: 1. Review and apply different LLMs and tools to fine-tune a model for business-specific tasks for making better use of AI in your own business growth. 2. Comprehend LLM Fundamentals: Understand the basics of LLMs and the significance of fine-tuning. (Knowledge) 3. Analyze Business Applications: Evaluate how LLM fine-tuning is applied in different business scenarios. (Analysis) Develop Fine-Tuning Strategies: Create strategies for fine-tuning LLMs to meet specific business needs. (Application) Forecast Future Trends: Anticipate and plan for future developments in LLM technology in business contexts. (Evaluation)

This course offers a deep dive into Large Language Models (LLMs), exploring their capabilities, applications, challenges, and future potential in the business landscape. Through a blend of theoretical insights and practical examples, learners can review and acquire concepts related to LLMs and their transformative impact on various industries. Today, major organizations use related LLM technologies such as Customer Support Chatbots, Marketing Content Generation, and Software Development tools. The impact of these technologies combined with real-time Data Analytics has transformed a wide range of industries, from airlines, advertising agencies, legal firms, and health care, just to name a few. Overall, Generative AI is changing the landscape faster than ever. As we dive into the lessons of this course, we will frame our discussions through several cases for marketing, software programming, content creation, and analytics. This course provides a 360° overview of the current abilities of state-of-the-art LLMs for businesses. Moreover, through this course, you will learn to identify some of the trends behind LLMs such as Neural Networks, Transformers, source models, and APIs. This program is designed for: 1- Entrepreneurs, business executives, and employees in general, will be able to evaluate how LLMs could affect their business. 2- Non-technical participants who can detect areas in which LLMs can improve their productivity. 3- Technical participants who will generate ideas to apply their skills and use LLMs to their advantage. 4- Learners who will have an opportunity to foresee what areas of business are likely to be impacted by LLMs. Learners should ideally possess the following prerequisite skills: Basic knowledge about business and startups; Curiosity for the field of AI; and General interest in machine learning and AI-related technologies.

This course addresses evaluating Large Language Models (LLMs), starting with foundational evaluation methods, exploring advanced techniques with Vertex AI's tools like Automatic Metrics and AutoSxS, and forecasting the evolution of generative AI evaluation. This course is ideal for AI Product Managers looking to optimize LLM applications, Data Scientists interested in advanced AI model evaluation techniques, AI Ethicists and Policy Makers focused on responsible AI deployment, and Academic Researchers studying the impact of generative AI across various domains. A basic understanding of artificial intelligence, machine learning concepts, and familiarity with natural language processing (NLP) is recommended. Prior experience with Google Cloud Vertex AI is beneficial but not required. It covers practical applications, integrating human judgment with automatic methods, and prepares learners for future trends in AI evaluation across various media, including text, images, and audio. This comprehensive approach ensures you are equipped to assess LLMs effectively, enhancing business strategies and innovation.

Did you know that mastering Generative AI (GenAI) and selecting the right models can significantly enhance your projects and organization? Learn how to leverage advanced AI technologies to make informed decisions and optimize your workflows. This short course empowers professionals to enhance their strategies using GenAI technologies. By completing this course, you'll be able to explain various GenAI models and their applications, evaluate these models, and integrate them into your operational systems effectively. By the end of this course, you will be able to: 1. Distinguish between GenAI models and their applications. 2. Use evaluation criteria to select suitable GenAI models for specific uses cases. 3. Make informed decisions about integrating Generative AI models into their operational systems. There are many different GenAi models available and it can be challenging to find the one that meets your needs. This course is unique because you will be empowered to evaluate models based on your particular needs and criteria to make an effective decision. To be successful in this course, you should have experience with AI technologies, such as machine learning or neural networks, and a foundational understanding of integrating these technologies into business or technical operations. Familiarity with model evaluation, data handling, and computational resource management is also recommended.

There are literally thousands of Large Language Models or LLMs available out there that can be used for a plethora of purposes. Hugging Face is the de-facto hub for language models, offering a huge collection where you can find and use almost any model you need. Choosing the right model can be an arduous task given models come in various shapes, sizes and configurations and each model is specialized at something different. So, when you approach Hugging Face in search of the right Model for your requirement, you have to know the art of this matchmaking. In this course, we will learn how to navigate through the Hugging Face Hub for Models, matching their configurations to your needs. We will understand key characteristics of Models (LLMs), such as Size, Computational Requirements, Specializations, Licensing and so on. We will look into various families of Models and their specializations, performance and variants. We will also learn how to use various models from Hugging Face and Evaluate them based on your requirements. This course is designed for professionals deeply involved in the field of AI and machine learning, including Data Scientists, Machine Learning Engineers, AI Engineers, LLM RAG Application Developers, Software Developers, and IT Engineers. It targets individuals who are actively building or plan to build applications leveraging Large Language Models (LLMs) and seek to enhance their ability to select and utilize the most appropriate models for their specific needs. Participants should have a strong foundation in Python programming and a basic understanding of Large Language Models (LLMs) and their programmatic use, as the course will build on these concepts with practical coding exercises and advanced topics like model selection, comparison, and evaluation. By the end of this course, learners will have achieved four key objectives. They will master navigating the Hugging Face ecosystem, gaining proficiency in finding and understanding various models. They will also learn to effectively use these models, comparing them based on multiple factors and practical considerations. Additionally, the course will guide participants in testing and evaluating different models, enabling them to score and assess the results based on specific parameters. Ultimately, learners will be equipped to select the most suitable model for a given task, ensuring optimal performance in their applications.