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Golang Data Science Books. There have even been a couple books written about the topic. Go Machine Learning Projects (2018) – this book uses gonum and gorgonia in the examples Machine Learning with Go (2017). Stackoverflow “Go really can be that much faster than python” Reasons to use Golang for Data Science.
Currently, we are working hard on the second edition of Building LLMs for Production, and we would love to know how your reading journey with the book has been. Super excited to read your reviews for the book! Perfectlord is looking for a few college students from India for the Amazon ML Challenge. Meme of the week!
Many of you asked for an electronic version of our new book, so after working out the kinks, we are finally excited to release the electronic version of “Building LLMs for Production.” We’ve heard many feedback from you guys wanting to have both the e-book and book for different occasions. We listened.
Using Python # Load a model model = YOLO("yolo11n.pt") # Predict with the model results = model("[link] First, we load the YOLO11 object detection model. We must note 2 key points: The Python approach gives us more flexibility to integrate the model into larger projects and customize the outputs programmatically. Here, yolo11n.pt
This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting for vendors who see the opportunity to create buzz around a new category of enterprise software. What does a modern technology stack for streamlined ML processes look like?
Creating scalable and efficient machine learning (ML) pipelines is crucial for streamlining the development, deployment, and management of ML models. Configuration files (YAML and JSON) allow ML practitioners to specify undifferentiated code for orchestrating training pipelines using declarative syntax.
The following is an extract from Andrew McMahon’s book , Machine Learning Engineering with Python, Second Edition. Secondly, to be a successful ML engineer in the real world, you cannot just understand the technology; you must understand the business. First of all, the ultimate goal of your work is to generate value.
years along a dozen graduates and experts in the field… our new and first book: Building LLMs for Production! So we’ve gathered everything we worked on and with in this 470-page book all about LLMs and how to work with them. Jerry Liu , Co-founder and CEO of LlamaIndex “This book will help you or your company get the most out of LLMs.
Multiple programming language support – The GitHub repository provides the observability solution in both Python and Node.js With a strong background in AI/ML, Ishan specializes in building Generative AI solutions that drive business value. However, some components may incur additional usage-based costs.
If you’ve enjoyed the list of courses at Gen AI 360, wait for this… Today, I am super excited to finally announce that we at towards_AI have released our first book: Building LLMs for Production. This 470-page book is all about LLMs and how to work with them. Good morning, fellow learners. Get your copy now!
For AWS and Outerbounds customers, the goal is to build a differentiated machine learning and artificial intelligence (ML/AI) system and reliably improve it over time. Second, open source Metaflow provides the necessary software infrastructure to build production-grade ML/AI systems in a developer-friendly manner.
ML for Big Data with PySpark on AWS, Asynchronous Programming in Python, and the Top Industries for AI Harnessing Machine Learning on Big Data with PySpark on AWS In this brief tutorial, you’ll learn some basics on how to use Spark on AWS for machine learning, MLlib, and more. You Can Now Watch the Generative AI Summit On-Demand Here!
Take advantage of the current deal offered by Amazon (depending on location) to get our recent book, “Building LLMs for Production,” with 30% off right now! Get the book now at 30% off! Featured Community post from the Discord Arwmoffat just released Manifest, a tool that lets you write a Python function and have an LLM execute it.
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His book, Deep Learning Illustrated , is a #1 bestseller and has been translated into six languages. He also operates his own Python and data science consultancy and corporate training business. He also teaches AI and ML courses at Cornell, NY and Queens University, CA. She is also an experienced instructor and lecturer.
Source: Author Introduction Machine learning (ML) models, like other software, are constantly changing and evolving. Version control systems (VCS) play a key role in this area by offering a structured method to track changes made to models and handle versions of data and code used in these ML projects.
If you want to check it out, we also have our book, Building LLMs for Production, available on the O’Reilly learning platform. We recently partnered with O’Reilly to make our book available on their learning platform. If you are enjoying our latest book, Building LLMs for Production, could you take a moment to drop an honest review?
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