Walmart is expanding its drone delivery operations to some 4 million households in six states, as the big-box retailer looks to add speedy delivery to compete in the nascent space with the likes of Amazon.com Walmart said Tuesday that it will be able to deliver more than 1 million packages by drone a year in as little as 30 minutes to households in parts of Arizona, Arkansas, Florida, Texas, Utah and Virginia. The service will have a $3.99 delivery fee per order and can deliver up to 10 pounds at a time.
Khang Pham is a Software Engineer with 12 years of experience in Machine Learning and Big Data. Since 2019, he has helped hundreds of engineers get jobs at big tech companies like Google, Meta (Facebook), Amazon, Apple, LinkedIn, Twitter and Microsoft. He created Machine Learning System Design (https://rebrand.ly/mlsd_launch) in 2021 and his course became the number #1 Machine Learning course on educative.
This book is great for someone with a tech and Python background who wants to grow their career in ML or someone working in the machine learning domain aspiring to better understand the full ML lifecycle. Even if you are new to Python, the theories in the book are worth learning and the Python examples are complete and easy to run. David does a really great job of starting simple in the first section of the book with an explanation of AI and machine learning and different types of ML. From there, he goes into use cases of ML across different sectors. I enjoyed the labs in this portion of the book as a good tech refresher; [...]this section is comprehensive and gives you hands-on experience with automation and integrating many of the technologies you would need in your enterprise ML Platform. I felt this part of the book provides solid guidance covering all the key areas you need to understand to build an ML platform with examples and labs in each area. Overall, I was impressed with the writing throughout the book and the way it shows you the full picture from learning the basics to advanced topics in ML with accompanying labs. For anyone interested in becoming a machine learning solutions architect or looking to build skills for ML projects, this book is a must-read.
Most developers who grapple with big data are data engineers, data scientists, or machine learning engineers. This book is aimed at those professionals who are looking to use Spark to scale their applications to handle massive amounts of data. In particular, data engineers will learn how to use Spark's Structured APIs to perform complex data exploration and analysis on both batch and streaming data; use Spark SQL for interactive queries; use Spark's built-in and external data sources to read, refine, and write data in different file formats as part of their extract, transform, and load (ETL) tasks; and build reliable data lakes with Spark and the open source Delta Lake table format. For data scientists and machine learning engineers, Spark's MLlib library offers many common algorithms to build distributed machine learning models. We will cover how to build pipelines with MLlib, best practices for distributed machine learning, how to use Spark to scale single-node models, and how to manage and deploy these models using the open source library MLflow.
Done right, Machine Learning (ML) can be a major differentiator for your startup. While ML can get quite complex, you actually don't need a team of expensive Data Scientists and ML Engineers to gain real value from ML – like improving customer acquisition or providing personalized recommendations for customers. Then check out our upcoming Twitch training series, Let's ship it – with AWS! ML Edition to learn how to easily get started with ML. This live, interactive training with AWS Machine Learning experts Aaron Hunter and Fred Graichen will begin on June 2nd and run through July 21st, every Thursday from 4pm – 5:30pm PT. Each session will feature a hands-on ML use case, and we'll be answering your questions live.
Subtitle creation on video content poses challenges no matter how big or small the organization. To address those challenges, Amazon Transcribe has a helpful feature that enables subtitle creation directly within the service. There is no machine learning (ML) or code writing required to get started. This post walks you through setting up a no-code workflow for creating video subtitles using Amazon Transcribe within your Amazon Web Services account. The terms subtitles and closed captions are commonly used interchangeably, and both refer to spoken text displayed on the screen.
Educated at St Pauls School, London and Cambridge University, José Luis Bermúdez is Professor of Philosophy at Texas A&M University, where he has also served as Dean of Liberal Arts and Associate Provost for Strategic Planning. Since his first book, The Paradox of Self-Consciousness (MIT Press 1998) he has been working on interdiscipinary aspects of self-representation and self-consciousness, most recently in Understanding "I": Language and Thought (OUP, 2017) and The Bodily Self: Selected Essays (MIT Press, 2018). He also works on rationality and reasoning, where he has published Decision Theory and Rationality (OUP, 2009). He is currently writing a book of framing and rationality, and also preparing the third edition of his textbook Cognitive Science: An Introduction to the Science of the Mind, both for Cambridge University Press. His work has appeared in seven languages and he is one of the 100 most cited philosophers on Google scholar.
AWS customers are relying on Infrastructure as Code (IaC) to design, develop, and manage their cloud infrastructure. IaC ensures that customer infrastructure and services are consistent, scalable, and reproducible, while being able to follow best practices in the area of development operations (DevOps). One possible approach to manage AWS infrastructure and services with IaC is Terraform, which allows developers to organize their infrastructure in reusable code modules. This aspect is increasingly gaining importance in the area of machine learning (ML). Developing and managing ML pipelines, including training and inference with Terraform as IaC, lets you easily scale for multiple ML use cases or Regions without having to develop the infrastructure from scratch.
OppFi Inc., a 10-year-old fintech platform based in Chicago, targets U.S. households with an average of $50,000 in annual income that need extra cash for car repairs, medical bills, student loans and other expenses. Todd Schwartz, the company's chief executive, said its customers are employed and have bank accounts but are otherwise "locked out of mainstream financial services." The Morning Download delivers daily insights and news on business technology from the CIO Journal team. OppFi, which made its public-market debut last summer, uses an AI model, real-time data analytics and a proprietary scoring algorithm to automate the underwriting process. It generates a credit score by analyzing a loan applicant's online shopping habits, income and employment information, among other data sources.
Rajiv Malhotra was trained initially as a Physicist, and then as a Computer Scientist specializing in AI in the 1970s. After a successful corporate career in the US, he became an entrepreneur and founded and ran several IT companies in 20 countries. Since the early 1990s, as the founder of his non-profit Infinity Foundation (Princeton, USA), he has been researching civilizations and their engagement with technology from a historical, social sciences and mind sciences perspective. He has authored several best-selling books. Infinity Foundation has also published a 14-volume series on the History of Indian Science & Technology.