AMAZON ML platform services in Artificial Intelligence Platform

SageMaker is available through Amazon's ML services platform. It allows developers and data scientists to swiftly create, train, and deploy machine learning models. Amazon SageMaker is a fully managed service that handles the complete machine learning workflow, including data labeling and preparation, algorithm selection, model training, tuning, and optimization for deployment, prediction, and action. The models are ready for manufacturing in a fraction of the time and at a fraction of the cost.

Demo

Amazon SageMaker demo can be requested by connecting with support.

Pricing

• Amazon SageMaker - Amazon SageMaker is free to try
• Amazon SageMaker Studio Notebooks ml.m5d.24xlarge with 96 vCPU and 192 GiB Memory is priced at $4.896 per Hour
• RStudio on SageMaker ml.m5d.24xlarge with 96 vCPU and 384 GiB Memory is priced at $6.509 per Hour
• Amazon SageMaker Hosting: Real-Time Inference ml.m4.16xlarge with 64 vCPU and 256 GiB Memory is priced at $3.84 per Hour
• Amazon SageMaker Data Wrangler ml.m5.4xlarge with 16 vCPU and 64 GiB Memory is priced at $0.922 per Hour
• Amazon SageMaker Processing ml.m4.16xlarge with 64 vCPU and 256 GiB Memory is priced at $3.84 per Hour
• Amazon SageMaker Training ml.m4.16xlarge with 64 vCPU and 256 GiB Memory is priced at $3.84 per Hour
• Amazon SageMaker Asynchronous Inference ml.m4.16xlarge with 64 vCPU and 256 GiB is priced at $3.84 per Hour

Features

Amazon SageMaker Ground Truth builds highly accurate training datasets for machine learning quickly. SageMaker Ground Truth offers easy access to public and private human labelers and provides them with built-in workflows and interfaces for common labeling tasks. Additionally, SageMaker Ground Truth can lower your labeling costs by up to 70% using automatic labeling, which works by training Ground Truth from data labeled by humans so that the service learns to label data independently.

SageMaker Ground Truth uses a machine learning model to automatically label raw data to produce high-quality training datasets quickly at a fraction of the cost of manual labeling. Data is only routed to humans if the active learning model cannot confidently label it. The human-labeled data is then used to train the model to improve its capabilities. Less data is then sent to humans in the next round of labeling, lowering your costs.

Amazon SageMaker Ground Truth helps build high-quality and accurate training datasets quickly. Machine-generated labels provide consistent results with a confidence score for each label so that it can easily understand how certain the service is that the label is correct. Human-labeled results are automatically scored against criteria it provides to help ensure that more data is sent to high-quality labelers and low-quality labelers are deemphasized.