clean dead

Author: robmarkcole

.mlc_config.json

@@ -3,6 +3,7 @@
         200,
         403,
         429,
+        500,
         503,
         999
     ]

README.md

@@ -115,12 +115,11 @@ If you are happy to live with some lock-in, these are good options:
 
 ## Framework agnostic model serving
 * The [Triton Inference Server](https://github.com/triton-inference-server/server) provides an optimized cloud and edge inferencing solution. Read [CAPE Analytics Uses Computer Vision to Put Geospatial Data and Risk Information in Hands of Property Insurance Companies](https://blogs.nvidia.com/blog/2021/05/21/cape-analytics-computer-vision/)
-* [RedisAI](https://oss.redis.com/redisai/) is a Redis module for executing Deep Learning/Machine Learning models and managing their data
+* [RedisAI](https://github.com/RedisAI/RedisAI) is a Redis module for executing Deep Learning/Machine Learning models and managing their data
 
 ## Using lambda functions - i.e. serverless
 Using lambda functions allows inference without having to configure or manage the underlying infrastructure
 * On AWS either use regular lambdas from AWS or [SageMaker Serverless Inference](https://docs.aws.amazon.com/sagemaker/latest/dg/serverless-endpoints.html)
-* [Object detection inference with AWS Lambda and IceVision (PyTorch)](https://laurenzstrothmann.com/object-detection-inference-aws-lambda-icevision) with [repo](https://github.com/2649/laurenzstrothmann)
 * [Deploying PyTorch on AWS Lambda](https://segments.ai/blog/pytorch-on-lambda)
 * [Example deployment behind an API Gateway Proxy](https://github.com/philschmid/cdk-samples/tree/master/sagemaker-serverless-huggingface-endpoint)
 
@@ -151,7 +150,7 @@ Once your model is deployed you will want to monitor for data errors, broken pip
 # AWS
 * Host your data on [S3](https://aws.amazon.com/s3/) and metadata in a db such as [postgres](https://aws.amazon.com/rds/postgresql/)
 * For batch processing use [Batch](https://aws.amazon.com/batch/). GPU instances are available for [batch deep learning](https://aws.amazon.com/blogs/compute/deep-learning-on-aws-batch/) inferencing.
-* If processing can be performed in 15 minutes or less, serverless [Lambda](https://aws.amazon.com/lambda/) functions are an attractive option owing to their ability to scale. Note that lambda may not be a particularly quick solution for deep learning applications, since you do not have the option to batch inference on a GPU. Creating a docker container with all the required dependencies can be a challenge. To get started read [Using container images to run PyTorch models in AWS Lambda](https://aws.amazon.com/blogs/machine-learning/using-container-images-to-run-pytorch-models-in-aws-lambda/) and for an image classification example [checkout this repo](https://github.com/aws-samples/aws-lambda-docker-serverless-inference). Also read [Processing satellite imagery with serverless architecture](https://aws.amazon.com/blogs/compute/processing-satellite-imagery-with-serverless-architecture/) which discusses queuing & lambda. Sagemaker also supports server less inference, see  [SageMaker Serverless Inference](https://docs.aws.amazon.com/sagemaker/latest/dg/serverless-endpoints.html). For managing a serverless infrastructure composed of multiple lambda functions use [AWS SAM](https://docs.aws.amazon.com/serverless-application-model/index.html) and read [How to continuously deploy a FastAPI to AWS Lambda with AWS SAM](https://iwpnd.pw/articles/2020-01/deploy-fastapi-to-aws-lambda)
+* If processing can be performed in 15 minutes or less, serverless [Lambda](https://aws.amazon.com/lambda/) functions are an attractive option owing to their ability to scale. Note that lambda may not be a particularly quick solution for deep learning applications, since you do not have the option to batch inference on a GPU. Creating a docker container with all the required dependencies can be a challenge. To get started read [Using container images to run PyTorch models in AWS Lambda](https://aws.amazon.com/blogs/machine-learning/using-container-images-to-run-pytorch-models-in-aws-lambda/) and for an image classification example [checkout this repo](https://github.com/aws-samples/aws-lambda-docker-serverless-inference). Also read [Processing satellite imagery with serverless architecture](https://aws.amazon.com/blogs/compute/processing-satellite-imagery-with-serverless-architecture/) which discusses queuing & lambda. Sagemaker also supports server less inference, see  [SageMaker Serverless Inference](https://docs.aws.amazon.com/sagemaker/latest/dg/serverless-endpoints.html). For managing a serverless infrastructure composed of multiple lambda functions use [AWS SAM](https://docs.aws.amazon.com/serverless-application-model/index.html)
 * [Sagemaker](https://aws.amazon.com/sagemaker/) is an ecosystem of ML tools accessed via a hosted Jupyter environment & API. Read [Build GAN with PyTorch and Amazon SageMaker](https://aws.amazon.com/blogs/machine-learning/build-gan-with-pytorch-and-amazon-sagemaker/), [Run computer vision inference on large videos with Amazon SageMaker asynchronous endpoints](https://aws.amazon.com/blogs/machine-learning/run-computer-vision-inference-on-large-videos-with-amazon-sagemaker-asynchronous-endpoints/), [Use Amazon SageMaker to Build, Train, and Deploy ML Models Using Geospatial Data](https://aws.amazon.com/blogs/aws/preview-use-amazon-sagemaker-to-build-train-and-deploy-ml-models-using-geospatial-data/)
 * [SageMaker Studio Lab](https://studiolab.sagemaker.aws/) competes with Google colab being free to use with no credit card or AWS account required
 * [Deep learning AMIs](https://aws.amazon.com/machine-learning/amis/) are EC2 instances with deep learning frameworks preinstalled. They do require more setup from the user than Sagemaker but in return allow access to the underlying hardware, which makes debugging issues more straightforward. There is a [good guide to setting up your AMI instance on the Keras blog](https://blog.keras.io/running-jupyter-notebooks-on-gpu-on-aws-a-starter-guide.html). Read [Deploying the SpaceNet 6 Baseline on AWS](https://medium.com/the-downlinq/deploying-the-spacenet-6-baseline-on-aws-c811ad82da1)
@@ -194,7 +193,7 @@ Once your model is deployed you will want to monitor for data errors, broken pip
 * For containerised apps use [Cloud Run](https://cloud.google.com/run) (AWS App Runner equivalent but can scale to zero)
 
 # Microsoft Azure
-* [Azure Orbital](https://azure.microsoft.com/en-us/services/orbital/) -> Satellite ground station and scheduling services for fast downlinking of data
+* [Azure Orbital](https://azure.microsoft.com/en-us/products/orbital/) -> Satellite ground station and scheduling services for fast downlinking of data
 * [ShipDetection](https://github.com/microsoft/ShipDetection) -> use the Azure Custom Vision service to train an object detection model that can detect and locate ships in a satellite image
 * [SwimmingPoolDetection](https://github.com/retkowsky/SwimmingPoolDetection) -> Swimming pool detection with Azure Custom Vision
 * [Geospatial analysis with Azure Synapse Analytics](https://docs.microsoft.com/en-us/azure/architecture/industries/aerospace/geospatial-processing-analytics) and [repo](https://github.com/Azure/Azure-Orbital-Analytics-Samples)
@@ -238,12 +237,9 @@ Processing on board a satellite allows less data to be downlinked. e.g. super-re
 * [Intel to place movidius in orbit to filter images of clouds at source - Oct 2020](https://techcrunch.com/2020/10/20/intel-is-providing-the-smarts-for-the-first-satellite-with-local-ai-processing-on-board/) - Getting rid of these images before they’re even transmitted means that the satellite can actually realize a bandwidth savings of up to 30%
 * [WorldFloods](https://watchers.news/2021/07/11/worldfloods-ai-pioneered-at-oxford-for-global-flood-mapping-launches-into-space/) will pioneer the detection of global flood events from space, launched on June 30, 2021. [This paper](https://arxiv.org/pdf/1910.03019.pdf) describes the model which is run on Intel Movidius Myriad2 hardware capable of processing a 12 MP image in less than a minute
 * [How AI and machine learning can support spacecraft docking](https://towardsdatascience.com/deep-learning-in-space-964566f09dcd) with [repo](https://github.com/nevers/space-dl) uwing Yolov3
-* [exo-space](https://www.exo-space.com/) -> startup with plans to release an AI hardware addon for satellites
 * [Sony’s Spresense microcontroller board is going to space](https://developer.sony.com/posts/the-spresense-microcontroller-board-launched-in-space/) -> vision applications include cloud detection, [more details here](https://www.hackster.io/dhruvsheth_/to-space-and-beyond-with-edgeimpulse-and-sony-s-spresense-d87a70)
 * [Palantir Edge AI in Space](https://blog.palantir.com/edge-ai-in-space-93d793433a1e) -> using NVIDIA Jetson for ship/aircraft/cloud detection & land cover segmentation
 * [Spiral Blue](https://spiralblue.space/) -> startup building edge computers to run AI analytics on-board satellites
 * [RaVAEn](https://github.com/spaceml-org/RaVAEn) -> a lightweight, unsupervised approach for change detection in satellite data based on Variational Auto-Encoders (VAEs) with the specific purpose of on-board deployment. It flags changed areas to prioritise for downlink, shortening the response time
 * [AWS successfully runs AWS compute and machine learning services on an orbiting satellite in a first-of-its kind space experiment](https://aws.amazon.com/blogs/publicsector/aws-successfully-runs-aws-compute-machine-learning-services-orbiting-satellite-first-space-experiment/)
 * [An Overview of Model Compression Techniques for Deep Learning in Space](https://medium.com/gsi-technology/an-overview-of-model-compression-techniques-for-deep-learning-in-space-3fd8d4ce84e5)
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