Category: Amazon Machine Learning

Amazon Fraud Detector is a fully managed service that makes it easy to identify potentially fraudulent online activities, such as the creation of fake accounts or online payment fraud. Unlike general-purpose machine learning (ML) packages, Amazon Fraud Detector is designed specifically to detect fraud. Amazon Fraud Detector combines your data, the latest in ML science, […]

This post was authored by Simon Rolfes. Simon played 288 Bundesliga games as a central midfielder, scored 41 goals, and won 26 caps for Germany. Currently, he serves as Sporting Director at Bayer 04 Leverkusen, where he oversees and develops the pro player roster, the scouting department, and the club’s youth development. Simon also writes […]

Many universities like transcribing their recorded class lectures and later creating captions out of these transcriptions. Amazon Transcribe is a fully-managed automatic speech recognition service (ASR) that makes it easy to add speech-to-text capabilities to voice-enabled applications. Transcribe assists in increasing accessibility and improving content engagement and learning outcomes by connecting with both auditory and […]

AWS DeepRacer is the fastest way to get rolling with machine learning (ML) through a global racing league, cloud-based 3D racing simulator, and fully autonomous 1/18th scale race car driven by reinforcement learning. Starting today, the model action space is fully customizable yet simplified with new dynamic graphics so developers have greater control and can […]

May 2024: This post was reviewed and updated for accuracy. You can use various tools to define and run machine learning (ML) pipelines or DAGs (Directed Acyclic Graphs). Some popular options include AWS Step Functions, Apache Airflow, KubeFlow Pipelines (KFP), TensorFlow Extended (TFX), Argo, Luigi, and Amazon SageMaker Pipelines. All these tools help you compose […]

Running machine learning (ML) models at the edge can be a powerful enhancement for Internet of Things (IoT) solutions that must perform inference without a constant connection back to the cloud. Although there are numerous ways to train ML models for countless applications, effectively optimizing and deploying these models for IoT devices can present many […]

Data scientists can spend up to 80% of their time preparing data for machine learning (ML) projects. This preparation process is largely undifferentiated and tedious work, and can involve multiple programming APIs and custom libraries. Announced at AWS re:Invent 2020, Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for […]

February 9, 2024: Amazon Kinesis Data Firehose has been renamed to Amazon Data Firehose. Read the AWS What’s New post to learn more. The Intel Olympic Technology Group (OTG), a division within Intel focused on bringing cutting-edge technology to Olympic athletes, collaborated with AWS Machine Learning Professional Services (MLPS) to build a smart coaching software […]

This is a guest post from Mikael Graindorge, Sales Operations Leader at Thermo Fisher Scientific. In the life sciences industry, data is growing in abundance and is getting increasingly complex, which makes it challenging to use traditional analytics methodologies. At Thermo Fisher Scientific, our mission is to make the world healthier, cleaner, and safer, and […]

The last few years have seen the rise of transformer deep learning architectures to build natural language processing (NLP) model families. The adaptations of the transformer architecture in models such as BERT, RoBERTa, T5, GPT-2, and DistilBERT outperform previous NLP models on a wide range of tasks, such as text classification, question answering, summarization, and […]