I wonder if it's possible to run SageMaker Inference or Batch Transform job directly for a video input (.mp4 or another format)?
If no could you please advice the best practice that might be used for pre-processing?
Asynchronous inference could be a good option for this use case. There is a blog published by AWS that talks about how you can do this.
https://aws.amazon.com/blogs/machine-learning/run-computer-vision-inference-on-large-videos-with-amazon-sagemaker-asynchronous-endpoints/
Related
I am a student studying Sage Maker Neo.
I am working on this tutorial.
Training and Serving with TensorFlow on Amazon SageMaker
https://github.com/aws/amazon-sagemaker-examples/blob/master/aws_sagemaker_studio/frameworks/tensorflow_mnist/tensorflow_mnist.ipynb
What I'm curious about is that the inference speed is similar when using the c5 instance and when using the p2 instance.
Please let me know what I am missing.
The tutorial doesn't have anything to do with Neo. You can try out Neo using this example - https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker_neo_compilation_jobs/pytorch_torchvision/pytorch_torchvision_neo.ipynb.
Also, SageMaker Inference Recommender is a good tool to standardize the testing of model performance - https://docs.aws.amazon.com/sagemaker/latest/dg/inference-recommender.html
Couldn't find the right place to ask this, so doing it here.
Does Model Monitor support monitoring Batch Transform jobs, or only endpoints? The documentation seems to only reference endpoints...
We just launched the support.
Here are the sample notebook:
https://github.com/aws/amazon-sagemaker-examples/tree/main/sagemaker_model_monitor/model_monitor_batch_transform
Here is the what's new post:
https://aws.amazon.com/about-aws/whats-new/2022/10/amazon-sagemaker-model-monitor-batch-transform-jobs/
I am reading article titles that suggest h2o.ai integrates its ML in/with snowflake.
https://www.h2o.ai/resources/solution-brief/integration-of-h2o-driverless-ai-with-snowflake/
If I wanted to export a POJO learner like a gbm and have it run in snowflake, is there a clean way to do that? I didn't see any clear directions in the (several) articles I found.
How does that integrate with ML-ops?
One way to integrate models built in H2o.ai is to integrate through Snowflake External Functions.
This is documented at https://docs.h2o.ai/driverless-ai/latest-stable/docs/userguide/snowflake-integration.html
H2o.ai also have (or will have shortly) support to deploy models into Snowflake Java UDFs and it is described in https://www.h2o.ai/blog/h2o-integrates-with-snowflake-snowpark-java-udfs-how-to-better-leverage-the-snowflake-data-marketplace-and-deploy-in-database/
I am trying out Amazon Sagemaker, I haven't figured out how we can have Continuous training.
For example if i have a CSV file in s3 and I want to train each time the CSV file is updated.
I know we can go again to the notebook and re-run the whole notebook to make this happen.
But i am looking for an automated way, with some python scripts or using a lambda function with s3 events etc
You can use boto3 sdk for python to start training on lambda then you need to trigger the lambda when csv is update.
http://boto3.readthedocs.io/en/latest/reference/services/sagemaker.html
Example python code
https://docs.aws.amazon.com/sagemaker/latest/dg/ex1-train-model-create-training-job.html
Addition: You dont need to use lambda you just start/cronjob the python script any kind of instance which has python and aws sdk in it.
There are a couple examples for how to accomplish this in the aws-samples GitHub.
The serverless-sagemaker-orchestration example sounds most similar to the use case you are describing. This example walks you through how to continuously train a SageMaker linear regression model for housing price predictions on new CSV data that is added daily to a S3 bucket using the built-in LinearLearner algorithm, orchestrated with Amazon CloudWatch Events, AWS Step Functions, and AWS Lambda.
There is also the similar aws-sagemaker-build example but it might be more difficult to follow currently if you are looking for detailed instructions.
Hope this helps!
I have recently started learning zeppelin. I know we can use angular and PostgreSQL e.t.c within it using interpreter. I have gone through its tutorial as well. But it is not as descriptive as I thought. I have many doubts which I am asking to you and which may help other beginners as well.
1> How we can create API for the zeppelin (if possible)?: As most of the client side apps uses API, is it possible to create API in zeppelin ? and in which language we can create API. If possible I am thinking to create API in java or node.js(JS).
2> Is it possible to integrate zeppelin graphs in any UI(angular or html ?)?
3> How we can deploy zeppelin based application in production environment ?
If you have any good tutorial source please attach it.
If I have asked unrelated questions please point out. I will change it.
Thanks in advance for provide help and giving you precious time!
Apache Zeppelin has wide and well described API [1]. You can use any language to work with API.
Yes [2]. You can embed the paragraph result to your website.
You can use binary package or built from source [3].
[4] contains a lot of code in setup section.
--
http://zeppelin.apache.org/docs/0.8.0/usage/rest_api/notebook.html
http://zeppelin.apache.org/docs/0.8.0/usage/other_features/publishing_paragraphs.html
http://zeppelin.apache.org/docs/0.8.0/quickstart/install.html
http://zeppelin.apache.org/docs/0.8.0/