http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
1 online resource (1 video file (3 hr., 5 min.)) : sound, color.
The engineering domain is one of the fastest growing areas in the field of machine learning. Machine learning powers advanced and seamless features such as user recommendations, predictions, image and speech recognition, medical diagnosis, and even fun applications like creating art based on user input. All this is powered by a plethora of systems that require well-trained engineers to design, implement, and use. But technical know-how is only part of the equation. In addition to the wide variety of technologies ML engineers must learn (including TensorFlow, PyTorch, AWS, Azure, BigQuery and many others), they have to deal with challenges like lack of data or data that's poorly labeled, fit, or collected to begin with. Join some of the best minds working in the field to learn how to tackle the challenges of ingesting, labeling, and applying data to the correctly identified machine learning problems. Whether you're a new ML engineer or a seasoned pro, you'll gain tips and insights that will help you design systems that allow for advanced analytics, predictions, and diagnoses. What you'll learn and how you can apply it Learn how to work with LLMs to achieve optimized results Discover how to design data and ML systems for trust and scalability Understand the challenges and opportunities in designing for industry This recording of a live event is for you because... You're a data engineer, ML engineer, or data scientist. You want to effectively approach the data lifecycle from ingestion to labeling to solving problems with machine learning. Recommended follow-up: Read Designing Machine Learning Systems (book) Read Fundamentals of Data Engineering (book) Read Machine Learning Design Patterns (book).
Shingai Manjengwa, Tim Long, Chip Huyen, Danny Farah, Devin Singh, Mohamed Hibat-Allah, Patricia Thaine, Parinaz Sobhani, presenters.