Video: AWS What’s Next, S1|E5. Upcoming launches and announcements covering AWS CodeArtifact, AWS Neptune, AWS Sagemaker Ground Truth and related product demos.

AWS What's Next, S1|E5

This 3 hours youtube video covers latest AWS services like CodeArtifact, Neptune and Sagemaker Ground Truth with demos.

  • AWS CodeArtifact: 00:19:23
    • AWS CodeArtifact Demo: 00:39:50
  • Amazon Neptune: 01:10:35
    • Amazon Neptune Demo: 01:25:57
  • Amazon SageMaker Ground Truth: 02:13:18
    • Amazon SageMaker Ground Truth Demo: 02:25:34

AWS What’s Next hosts Nick Walsh and Rob Zhu talk about upcoming launches and announcements from Amazon Web Services, including Richard Boyd, Senior Developer Advocate, AWS Developer Tools shares how AWS CodeArtifact offers Secure, scalable, cost effective artifact management for software development.

Karthik Bharathy, Principal Product Manager, Amazon Neptune demonstrates AWS DMS and how it is used to copy graph data from relational databases to Amazon Neptune.

Jonathan Buck, Software Dev Engineer, Ground Truth demonstrates how Amazon SageMaker Ground Truth is a fully managed data labeling service that makes it easy to build highly accurate training datasets for machine learning.

AWS CodeArtifact

AWS CodeArtifact is a fully managed artifact repository service that makes it easy for organizations of any size to securely store, publish, and share software packages used in their software development process.

CodeArtifact can be configured to automatically fetch software packages and dependencies from public artifact repositories so developers have access to the latest versions.

Amazon Neptune

Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets.

The core of Amazon Neptune is a purpose-built, high-performance graph database engine optimized for storing billions of relationships and querying the graph with milliseconds latency.

Amazon Neptune supports popular graph models Property Graph and W3C's RDF, and their respective query languages Apache TinkerPop Gremlin and SPARQL, allowing you to easily build queries that efficiently navigate highly connected datasets.

Neptune powers graph use cases such as recommendation engines, fraud detection, knowledge graphs, drug discovery, and network security.

Amazon Neptune is highly available, with read replicas, point-in-time recovery, continuous backup to Amazon S3, and replication across Availability Zones.


AWS Database Migration Service helps you migrate databases to AWS quickly and securely. The source database remains fully operational during the migration, minimizing downtime to applications that rely on the database.

The AWS Database Migration Service can migrate your data to and from most widely used commercial and open-source databases.

AWS Database Migration Service supports homogeneous migrations such as Oracle to Oracle, as well as heterogeneous migrations between different database platforms, such as Oracle or Microsoft SQL Server to Amazon Aurora.

With AWS Database Migration Service, you can continuously replicate your data with high availability and consolidate databases into a petabyte-scale data warehouse by streaming data to Amazon Redshift and Amazon S3.

AWS Sagemaker Ground Truth

Amazon SageMaker Ground Truth is a fully managed data labeling service that makes it easy to build highly accurate training datasets for machine learning.

Get started with labeling your data in minutes through the SageMaker Ground Truth console using custom or built-in data labeling workflows. These workflows support a variety of use cases including

  • 3D point clouds,
  • computer vision, and
  • natural language processing.

As part of the workflows, labelers have access to assistive labeling features such as

  • automatic 3D cuboid snapping,
  • removal of distortion in 2D images, and
  • auto-segment tools to reduce the time required to label datasets.

In addition, Ground Truth offers automatic data labeling which uses a machine learning model to label your data.

submitted by /u/reddit007user
[link] [comments]