Category: Advanced (300)

Spam emails, also known as junk mail, are sent to a large number of users at once and often contain scams, phishing content, or cryptic messages. Spam emails are sometimes sent manually by a human, but most often they are sent using a bot. Examples of spam emails include fake ads, chain emails, and impersonation […]

With cloud computing, as compute power and data became more available, machine learning (ML) is now making an impact across every industry and is a core part of every business and industry. Amazon SageMaker Studio is the first fully integrated ML development environment (IDE) with a web-based visual interface. You can perform all ML development […]

Predictive maintenance is critical in automotive industries because it can avoid out-of-the-blue mechanical failures and reactive maintenance activities that disrupt operations. By predicting vehicle failures and scheduling maintenance and repairs, you’ll reduce downtime, improve safety, and boost productivity levels. What if we could apply deep learning techniques to common areas that drive vehicle failures, unplanned […]

Cost of poor quality is top of mind for manufacturers. Quality defects increase scrap and rework costs, decrease throughput, and can impact customers and company reputation. Quality inspection on the production line is crucial for maintaining quality standards. In many cases, human visual inspection is used to assess the quality and detect defects, which can […]

Generative AI technology is improving rapidly, and it’s now possible to generate text and images based on text input. Stable Diffusion is a text-to-image model that empowers you to create photorealistic applications. You can easily generate images from text using Stable Diffusion models through Amazon SageMaker JumpStart. The following are examples of input texts and […]

This blog post is co-written with Marat Adayev and Dmitrii Evstiukhin from Provectus. When machine learning (ML) models are deployed into production and employed to drive business decisions, the challenge often lies in the operation and management of multiple models. Machine Learning Operations (MLOps) provides the technical solution to this issue, assisting organizations in managing, […]

Machine learning (ML) administrators play a critical role in maintaining the security and integrity of ML workloads. Their primary focus is to ensure that users operate with the utmost security, adhering to the principle of least privilege. However, accommodating the diverse needs of different user personas and creating appropriate permission policies can sometimes impede agility. […]

Amazon SageMaker Data Wrangler is a single visual interface that reduces the time required to prepare data and perform feature engineering from weeks to minutes with the ability to select and clean data, create features, and automate data preparation in machine learning (ML) workflows without writing any code. SageMaker Data Wrangler supports Snowflake, a popular […]

For data scientists, moving machine learning (ML) models from proof of concept to production often presents a significant challenge. One of the main challenges can be deploying a well-performing, locally trained model to the cloud for inference and use in other applications. It can be cumbersome to manage the process, but with the right tool, […]

Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). Studio provides a single web-based visual interface where you can perform all ML development steps required to prepare data, as well as build, train, and deploy models. Lifecycle configurations are shell scripts triggered by Studio lifecycle events, such as starting […]