Tech Horizon with Anand Vemula

  Fine-Tuning LLMs: A Developer’s Guide to Custom AI Models Link to Book -  Fine-Tuning LLMs: A Developer’s Guide to Custom AI Models Fine-Tuning LLMs: A Developer’s Guide to Custom AI Models" serves as a comprehensive resource for developers looking to adapt pre-trained large language models (LLMs) for specific applications. The book begins by tracing the historical evolution of LLMs, detailing their transition from traditional natural language processing (NLP) models to the sophisticated architectures used today. It emphasizes the importance of fine-tuning, which involves training a pre-existing model on a smaller, domain-specific dataset to enhance its performance on targeted tasks.The guide outlines various fine-tuning methodologies, including supervised, unsupervised, and instruction-based approaches. Each method is discussed in detail, highlighting its implications for different applications. A structured seven-stage pipeline for LLM fine-tuning is introduced, covering e...

  Hands-On AI: Building ML Models with Python Link to Book -  Hands-On AI: Building ML Models with Python This book explores advanced and cutting-edge topics in machine learning, artificial intelligence, and deep learning, providing both theoretical insights and practical implementations. It is designed for professionals and practitioners who seek to master modern AI techniques and apply them to real-world challenges. The first part of the book covers foundational topics such as optimizing classical machine learning algorithms, deep learning architectures like neural networks, and advanced unsupervised learning methods. Key chapters focus on building scalable data pipelines, feature engineering, and leveraging tools like Dask for data processing. The book delves into cutting-edge models, including vision transformers, natural language processing (NLP) with transformers, and reinforcement learning for real-world applications. The second part focuses on generative AI, particul...

  AI-Powered Development: Building and Scaling Machine Learning Applications Link to Book -  AI-Powered Development: Building and Scaling Machine Learning Applications AI-Powered Development: Building and Scaling Machine Learning Applications" is a practical guide for building and deploying real-world machine learning applications. Author emphasizes a hands-on approach, demonstrating key concepts through code examples, illustrations, and interviews with industry experts. The book covers the entire ML application lifecycle, starting with defining the problem and collecting data, then moving on to model training, evaluation, and deployment. It delves into crucial aspects like data preprocessing, feature engineering, and model selection, offering best practices for each stage. Furthermore, it addresses the challenges of scaling ML applications, including infrastructure considerations and monitoring strategies.

  Hands-On AI: Building ML Models with Python Link to Book -  Hands-On AI: Building ML Models with Python Hands-On AI: Building ML Models with Python" provides a comprehensive guide to understanding and applying machine learning (ML) using Python. The book covers the fundamental concepts, mathematical foundations, and the essential tools necessary for building successful ML models. It begins with an introduction to machine learning, explaining the basics and setting up the Python environment for AI development. The book then delves into data preparation and feature engineering, exploring techniques for data cleaning, wrangling, and visualization, all of which are crucial for effective model training. The book also addresses core machine learning algorithms, including supervised and unsupervised learning, regression models, classification models, and ensemble methods. Advanced topics such as deep learning, natural language processing (NLP), reinforcement learning, and time se...

  AI for Business: Transforming Operations with Machine Learning Link to Book -  AI for Business: Transforming Operations with Machine Learning AI for Business: Transforming Operations with Machine Learning explores the transformative power of artificial intelligence (AI) in reshaping business operations across industries. The book delves into how AI, particularly machine learning (ML), can be harnessed to drive innovation, enhance productivity, and create competitive advantages in a rapidly evolving business landscape. Starting with the foundational understanding of AI and ML, the book provides business leaders with key concepts and practical insights into how AI can be leveraged to improve decision-making, optimize processes, and unlock new growth opportunities. It emphasizes the importance of data as the core currency in AI-driven businesses, with chapters dedicated to data collection, cleaning, labeling, and ethical considerations. The book also dives into real-world app...

  Cloud AI Mastery: Deploying Models at Scale with AWS, Azure, and Google Cloud Link to Book -  Cloud AI Mastery: Deploying Models at Scale with AWS, Azure, and Google Cloud Cloud AI Mastery: Deploying Models at Scale with AWS, Azure, and Google Cloud" is a comprehensive guide for data scientists, machine learning engineers, and developers seeking to leverage the power of cloud computing for building, training, deploying, and managing AI models at scale. The book begins by establishing a strong foundation in cloud computing principles and core machine learning concepts, including supervised, unsupervised, and reinforcement learning, as well as neural network architectures. The core of the book dives into the AI/ML offerings of the three major cloud providers: AWS, Azure, and Google Cloud. For AWS, the book explores Amazon SageMaker in detail, covering model building, training, hyperparameter tuning, and deployment strategies like real-time inference and batch transform. It al...

  Machine Learning at Scale: Efficient AI Solutions with Big Data Link to Book -  Machine Learning at Scale: Efficient AI Solutions with Big Data Machine Learning at Scale: Efficient AI Solutions with Big Data" explores the challenges and techniques of building and deploying machine learning systems capable of handling massive datasets and complex models. It begins by establishing the foundations of scalable ML, covering the evolution from Big Data to AI-first, modern data engineering practices like data lakes and feature stores, and efficient algorithms including distributed training and federated learning. The book then transitions to practical implementation, detailing how to scale data preparation and feature engineering, optimize large model training and evaluation using techniques like AutoML and model compression, and implement MLOps for streamlined deployment and monitoring. It addresses crucial aspects of operationalizing ML, including CI/CD pipelines, model serving ...