AWS SageMaker Ground Truth
Computer Vision Annotation Tool (CVAT)
Labelbox
Supervisely
Qubole
V7
Universal Data Tool
Google CLOUD AUTOML
Amazon EMR
Google BigQuery
Google Cloud Dataflow
Google Cloud Dataproc
Databricks
Qubole
Snowflake
HortonWorks Data Platform
Amazon EMRNo AWS SageMaker Ground Truth videos yet. You could help us improve this page by suggesting one.
Based on our record, Amazon EMR should be more popular than AWS SageMaker Ground Truth. It has been mentiond 10 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Perhaps https://aws.amazon.com/sagemaker/data-labeling/ ? Source: almost 2 years ago
In this session you will discover how to use Amazon SageMaker to prepare data for machine learning in minutes. SageMaker provides data preparation tools that make it easier to label, prepare, and analyse your data. Walk through a complete data-preparation workflow, including how to use SageMaker Ground Truth to label training datasets, as well as how to extract data from numerous data sources, convert it using... - Source: dev.to / over 2 years ago
As for who run MLD I guess It’s Amazon itself, have a look at this https://aws.amazon.com/sagemaker/groundtruth/. I speculate that multiple companies use this resource and they are the one responsible to upload the correct instructions, Amazon just redirect the labeling job for us using and requester account in mTurk, that explains why the communication is unacceptable with this requester. Source: over 2 years ago
There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka. Source: about 1 year ago
I'm going to guess you want something like EMR. Which can take large data sets segment it across multiple executors and coalesce the data back into a final dataset. Source: almost 2 years ago
This is exactly the kind of workload EMR was made for, you can even run it serverless nowadays. Athena might be a viable option as well. Source: almost 2 years ago
Apache Spark is one of the most actively developed open-source projects in big data. The following code examples require that you have Spark set up and can execute Python code using the PySpark library. The examples also require that you have your data in Amazon S3 (Simple Storage Service). All this is set up on AWS EMR (Elastic MapReduce). - Source: dev.to / over 2 years ago
Check out https://aws.amazon.com/emr/. Source: about 2 years ago
Computer Vision Annotation Tool (CVAT) - Powerful and efficient Computer Vision Annotation Tool (CVAT) - opencv/cvat
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
Labelbox - Build computer vision products for the real world
Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
Supervisely - Supervisely helps people with and without machine learning expertise to create state-of-the-art...
Google Cloud Dataproc - Managed Apache Spark and Apache Hadoop service which is fast, easy to use, and low cost
