Amazon SageMaker Pipelines, the first purpose-built continuous integration and continuous delivery (CI/CD) service for machine learning (ML), is now integrated with SageMaker's automatic model tuning capability. Customers can add a model tuning step (TuningStep) in their SageMaker Pipelines which will automatically invoke a hyperparameter tuning job. The hyperparameter tuning finds the best version of a model by running many training jobs on the dataset using the algorithm and the ranges of hyperparameters specified by the customer. They can then register the best version of the model into the model registry using the RegisterModel step.

In less than a year, the American grocery store has gone from an age-old, in-person shopping institution to a destination at the forefront of a technological transformation. Grocery giant Kroger, for instance, covered 98 percent of households in its delivery areas in 2020 by investing in a large digital and delivery presence. Furthermore, according to a recent global study, over 50 percent of respondents are not planning to re-integrate in-store shopping into their routine for "a long time"--underlining the need for innovative solutions. As the world traverses a slow path to recovery amid COVID-19, how we purchase food and staple goods may never look quite the same, thanks to new technologies and consumer habits. Though automats and self-service technologies have existed since the 1930s, the first proper self-checkout platform was introduced in 1992 by Dr.

Artificial Intelligence (AI) has been redefining society in ways we have never anticipated. Technology is clinging to us in every walk of our lives, right from unlocking our smartphones to our day-to-day activities, online shopping, intelligent car dashboards, autonomous robots and so on. Though the concept of AI was first talked about in the early 1950s, forming a basis for many computer learning and complex decision-making processes, it is only of late, where processing huge amounts of data is required, that this field of technology is picking up pace. What is in the AI basket? AI is not a technology, rather it is a science or field of study.

There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.

In this post, you learn how to create a MLOps project to automate the deployment of an Amazon SageMaker endpoint with multiple production variants for A/B testing. You also deploy a general purpose API and testing infrastructure that includes a multi-armed bandit experiment framework. This testing infrastructure will automatically optimize traffic to the best-performing model over time based on user feedback. Amazon SageMaker MLOps projects are a new capability recently released with Amazon SageMaker Pipelines, the first purpose-built, easy-to-use, continuous integration and continuous delivery (CI/CD) service for ML. The MLOps project template provisions the initial setup required for a complete end-to-end MLOps system, including model building, training, and deployment, and can be customized to support your own organizations requirements.

Amazon SageMaker helps data scientists and developers prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML. SageMaker accelerates innovation within your organization by providing purpose-built tools for every step of ML development, including labeling, data preparation, feature engineering, statistical bias detection, AutoML, training, tuning, hosting, explainability, monitoring, and workflow automation. You can use a variety of techniques to deploy new ML models to production, so choosing the right strategy is an important decision. You must weigh the options in terms of the impact of change on the system and on the end users. In this post, we show you how to deploy using a shadow deployment strategy.

The frustration and anguish of trying and failing to piece together Ikea furniture may seem like an exercise in humiliation for you, but know this: The particleboard nightmare may one day lead to robots that aren't so stupid. In recent years, roboticists have been finding that building Ikea furniture is actually a great way to teach robots how to handle the chaos of the real world. One group of researchers coded a simulator in which virtual robot arms used trial and error to put chairs together. Others managed to get a different set of robot arms to construct Ikea chairs in the real world, though it took them 20 minutes. And now, a helpful robot can assist a human in assembling an Ikea bookcase by predicting what part they'll want next and handing it over.

The global health crisis accelerated the adoption of omnichannel shopping and fulfillment. Consumers spent $861.12 billion online with US merchants in 2020, up an incredible 44% compared to the previous year, which marks the highest annual growth in U.S. e-commerce in at least two decades. To keep up pace with this shift and more effectively sell, businesses have substantially moved investments to online infrastructures, such as e-commerce platforms, inventory management, product recommendations and chatbots and delivery. On one hand, setting up e-commerce sites and/or optimizing online stores means increased sales and market penetration; on the other, these benefits are potentially outweighed by the increased costs as retailers essentially shift a part of their business to logistics and fulfillment. As businesses make the transition to online retailers, they will have to focus on these four key customer areas to ensure profitability: fraud, delivery theft, returns and customer service.

Hugging Face is the technology startup, with an active open-source community, that drove the worldwide adoption of transformer-based models thanks to its eponymous Transformers library. Earlier this year, Hugging Face and AWS collaborated to enable you to train and deploy over 10,000 pre-trained models on Amazon SageMaker. For more information on training Hugging Face models at scale on SageMaker, refer to AWS and Hugging Face collaborate to simplify and accelerate adoption of Natural Language Processing models and the sample notebooks. In this post, we discuss different methods to create a SageMaker endpoint for a Hugging Face model. If you're unfamiliar with transformer-based models and their place in the natural language processing (NLP) landscape, here is an overview.

Proper inventory management is a top concern for retailers. Out-of-stock items and inefficient replacement strategies can result in lost sales, reduced customer satisfaction, and lower loyalty levels. In response to these challenges, companies like Walmart and Kellogg's are harnessing the power of artificial intelligence to improve real-time product substitutions and predict shortages weeks in advance. Artificial intelligence in the food and beverage market is expected to reach $29.94 billion by 2026, growing at a CAGR of over 45.77% during the forecast period, reported Research and Markets. This growth is largely attributed to consumer's increasing demand for fast, affordable, and easily accessible food options.