AWS ML - keshavbaweja-git/guides GitHub Wiki

Ground Truth Plus

Helps customers create high quality training data sets.

No need to build labeling applications and manage labeling workforce.

Provides mutually agreed upon upfront SLA for label quality.

Provides a feedback option on labels through a review interface.

Inference Recommender

Automates load testing and optimizes model performance across machine learning (ML) instances

Reduces the time it takes to get ML models from development to production in a cost effective way

Provides recommendations for best price performant instance type and endpoint configuration for model deployment

Free to use, customer only pays for instance usage during testing

Training Compiler

Optimize training runs

Deep Learning Container, but at this time tested again Hugging Face NLP models

PyTorch or TensorFlow

Minimal code changes

Amazon Lex Automated Chatbot Designer

Extends Amazon Lex by analyzing thousands of lines of transcripts in a couple of hours to give you an automated initial bot design that includes common intents

Integrated with Amazon Connect Contact Lens

Amazon Textract AnalyzeID

API for Amazon Textract that will automatically extract relevant information from identification documents, such as driver’s licenses and passports, without the need for templates or configuration

Amazon Personalize

New recommenders optimized for retail and media & entertainment use cases

New recipe type called USER_SEGMENTATION which includes two new recipes, aws-item-affinity and aws-item-attribute, both of which can be used to identify users based on their preferences

Amazon Kendra - Kendra Experience Builder

Deploy a fully functional and customizable search experience in a few clicks, without any coding or ML experience.

Use an intuitive, visual workflow to build, customize, and launch a Kendra powered search application

Amazon Kendra - Kendra Search Analytics Dashboard

View quality and usability metrics associated with a Kendra powered search application

Custom Document Enrichment build a custom ingestion pipeline that can pre-process documents before they get indexed into Kendra