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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”