Antje Barth

Antje Barth

San Francisco Bay Area
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Publications

  • Generative AI on AWS

    O'Reilly Media

    Companies today are moving rapidly to integrate generative AI into their products and services. But there's a great deal of hype (and misunderstanding) about the impact and promise of this technology. With this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology.

    You'll learn the generative AI project life cycle…

    Companies today are moving rapidly to integrate generative AI into their products and services. But there's a great deal of hype (and misunderstanding) about the impact and promise of this technology. With this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology.

    You'll learn the generative AI project life cycle including use case definition, model selection, model fine-tuning, retrieval-augmented generation, reinforcement learning from human feedback, and model quantization, optimization, and deployment. And you'll explore different types of models including large language models (LLMs) and multimodal models such as Stable Diffusion for generating images and Flamingo/IDEFICS for answering questions about images.

    - Apply generative AI to your business use cases
    - Determine which generative AI models are best suited to your task
    - Perform prompt engineering and in-context learning
    - Fine-tune generative AI models on your datasets with low-rank adaptation (LoRA)
    - Align generative AI models to human values with reinforcement learning from human feedback (RLHF)
    - Augment your model with retrieval-augmented generation (RAG)
    - Explore libraries such as LangChain and ReAct to develop agents and actions
    - Build generative AI applications with Amazon Bedrock

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  • Generative AI with Large Language Models

    DeepLearning.AI, Coursera

    In Generative AI with Large Language Models (LLMs), you’ll learn the fundamentals of how generative AI works, and how to deploy it in real-world applications.

    By taking this course, you'll learn to:
    - Deeply understand generative AI, describing the key steps in a typical LLM-based generative AI lifecycle, from data gathering and model selection, to performance evaluation and deployment
    - Describe in detail the transformer architecture that powers LLMs, how they’re trained, and how…

    In Generative AI with Large Language Models (LLMs), you’ll learn the fundamentals of how generative AI works, and how to deploy it in real-world applications.

    By taking this course, you'll learn to:
    - Deeply understand generative AI, describing the key steps in a typical LLM-based generative AI lifecycle, from data gathering and model selection, to performance evaluation and deployment
    - Describe in detail the transformer architecture that powers LLMs, how they’re trained, and how fine-tuning enables LLMs to be adapted to a variety of specific use cases
    - Use empirical scaling laws to optimize the model's objective function across dataset size, compute budget, and inference requirements
    - Apply state-of-the art training, tuning, inference, tools, and deployment methods to maximize the performance of models within the specific constraints of your project
    - Discuss the challenges and opportunities that generative AI creates for businesses after hearing stories from industry researchers and practitioners

    Developers who have a good foundational understanding of how LLMs work, as well the best practices behind training and deploying them, will be able to make good decisions for their companies and more quickly build working prototypes. This course will support learners in building practical intuition about how to best utilize this exciting new technology.

    This is an intermediate course, so you should have some experience coding in Python to get the most out of it. You should also be familiar with the basics of machine learning, such as supervised and unsupervised learning, loss functions, and splitting data into training, validation, and test sets. If you have taken the Machine Learning Specialization or Deep Learning Specialization from DeepLearning.AI, you’ll be ready to take this course and dive deeper into the fundamentals of generative AI.

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  • Practical Data Science

    DeepLearning.AI, Coursera

    Development environments might not have the exact requirements as production environments. Moving data science and machine learning projects from idea to production requires state-of-the-art skills. You need to architect and implement your projects for scale and operational efficiency. Data science is an interdisciplinary field that combines domain knowledge with mathematics, statistics, data visualization, and programming skills.

    The Practical Data Science Specialization brings…

    Development environments might not have the exact requirements as production environments. Moving data science and machine learning projects from idea to production requires state-of-the-art skills. You need to architect and implement your projects for scale and operational efficiency. Data science is an interdisciplinary field that combines domain knowledge with mathematics, statistics, data visualization, and programming skills.

    The Practical Data Science Specialization brings together these disciplines using purpose-built ML tools in the AWS cloud. It helps you develop the practical skills to effectively deploy your data science projects and overcome challenges at each step of the ML workflow using Amazon SageMaker.

    This Specialization is designed for data-focused developers, scientists, and analysts familiar with the Python and SQL programming languages who want to learn how to build, train, and deploy scalable, end-to-end ML pipelines - both automated and human-in-the-loop - in the AWS cloud.

    Each of the 10 weeks features a comprehensive lab developed specifically for this Specialization that provides hands-on experience with state-of-the-art algorithms for natural language processing (NLP) and natural language understanding (NLU), including BERT and FastText using Amazon SageMaker.

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  • Data Science on AWS

    O'Reilly Media

    With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level up your skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth…

    With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level up your skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance.

    - Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more
    - Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot
    - Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment
    - Tie everything together into a repeatable machine learning operations pipeline
    - Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka
    - Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more

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