Tag: faang

Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. It helps data scientists and developers prepare, build, train, and deploy high-quality ML models quickly by bringing together a broad set of capabilities purpose-built for ML. In 2021, AWS announced the integration of NVIDIA Triton Inference Server in SageMaker. You …

Read more “Serve multiple models with Amazon SageMaker and Triton Inference Server”

Amazon SageMaker is a fully managed service that provides developers and data scientists the ability to quickly build, train, and deploy machine learning (ML) models. With SageMaker, you can deploy your ML models on hosted endpoints and get inference results in real time. You can easily view the performance metrics for your endpoints in Amazon …

Read more “Model Hosting Patterns in SageMaker: Best practices in testing and updating models on SageMaker”

Organizations are increasingly using machine learning pipelines to streamline and scale their ML workflows. However, managing these pipelines can be challenging when an organization has multiple ML projects and pipelines at different stages of development. To solve this, we need a way to build upon DevOps concepts and apply them to this ML-specific problem. In …

Read more “Best Practices for managing Vertex Pipelines code”

Two months after their debut sweeping MLPerf inference benchmarks, NVIDIA H100 Tensor Core GPUs set world records across enterprise AI workloads in the industry group’s latest tests of AI training. Together, the results show H100 is the best choice for users who demand utmost performance when creating and deploying advanced AI models. MLPerf is the …

Read more “NVIDIA Hopper, Ampere GPUs Sweep Benchmarks in AI Training”

Posted by Shunyu Yao, Student Researcher, and Yuan Cao, Research Scientist, Google Research, Brain Team Recent advances have expanded the applicability of language models (LM) to downstream tasks. On one hand, existing language models that are properly prompted, via chain-of-thought, demonstrate emergent capabilities that carry out self-conditioned reasoning traces to derive answers from questions, excelling …

Read more “ReAct: Synergizing Reasoning and Acting in Language Models”

The COVID-19 global pandemic has accelerated the need to verify and onboard users online across several industries, such as financial services, insurance, and healthcare. When it comes to user experience it is crucial to provide a frictionless transaction while maintaining a high standard for identity verification.  The question is, how do you verify real people …

Read more ““ID + Selfie” – Improving digital identity verification using AWS”

Amazon SageMaker is a fully-managed service that provides every developer and data scientist with the ability to quickly build, train, and deploy machine learning (ML) models at scale. ML is realized in inference. SageMaker offers four Inference options: Real-Time Inference Serverless Inference Asynchronous Inference Batch Transform These four options can be broadly classified into Online …

Read more “Getting started with deploying real-time models on Amazon SageMaker”

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, and is composed of tumors with significant molecular heterogeneity resulting from differences in intrinsic oncogenic signaling pathways [1]. Enabling precision medicine, anticipating patient preferences, detecting disease, and improving care quality for NSCLC patients are important topics among healthcare and life sciences (HCLS) …

Read more “Predict lung cancer survival status using multimodal data on Amazon SageMaker JumpStart”