AWS EC2 python ML teach me training and deployment
Budget: $30 – $250 CAD
1. I do not provide you my AWS account . you develop all on your AWS account
2. Teach me ML python project end to end development on EC2 (not sagemaker) AWS with autoscaling production
2. Any python package installed by pip can be used for this ML project (not limited to scikit or deep learning)
3. Build with Automatic scaling a customized model training for python code ML model provided by me or you can use your code if I like it
4. Build with Automatic scaling a customized inference
5. Automatic scaling, load balancing to handle 1234 requests/sec, requests done by python from any computer on web
6. many EC2 AWS instances Distributed training with Hyperparameters search
7. possibly many EC2 AWS instances Distributed Preprocessing
8. many EC2 AWS instances Distributed Inference with autoscaling
9. requests implemented by you developed python for predictions may be done from any computer on web up to 1234 requests per second , everybody with credentials in web can create requests (not ssh or any remote connection to AWS web site)
10. no GUI design, but all done by python SDK - only python code used
11. ML will be provided or you can use your ML model if I like it
12. If you need Docker use Docker, but better if you know how do to this without Docker, since docker makes calculations to be slow
YOU PROVIDE PYTHON CODE AND
VIDEO of yours or our online meeting
WITH EXPLANATIONS STEP BY STEP HOW TO DO
---------------------------------------------------------------
PROJECT ACCEPTANCE : I CAN DO BY MYSELF on my AWS account
----------------------------------------------------------------
TIME MAX 1 WEEK
Testing : I use your python code which hosted on my AWS cloud project created by me using your instructions
I provide to John, Peter, Ana, Paul.. etc, only one the same web link and only python code for 1) request implementation for test data
2) receiving response and comparing with prediction calculated locally
3) Requests are sent by each person in parallel: 1234 requests per second combined for all of them (not each one 1243), for requests generation you can use data from train data set. Make sure there are no dropped requests ( requests without response )
4) Requests consists of data sample and unique Request ID. Machine learning model code receives data and Request ID. Then returns prediction and the same Request ID concatenated with date time of processing. Response: id and prediction and date time
Many requests are sent asynchronously in batches or continuously. When predictions are received: unique ID is used to match prediction and request
Id to confirm it is the same request what was sent for prediction
2. Teach me ML python project end to end development on EC2 (not sagemaker) AWS with autoscaling production
2. Any python package installed by pip can be used for this ML project (not limited to scikit or deep learning)
3. Build with Automatic scaling a customized model training for python code ML model provided by me or you can use your code if I like it
4. Build with Automatic scaling a customized inference
5. Automatic scaling, load balancing to handle 1234 requests/sec, requests done by python from any computer on web
6. many EC2 AWS instances Distributed training with Hyperparameters search
7. possibly many EC2 AWS instances Distributed Preprocessing
8. many EC2 AWS instances Distributed Inference with autoscaling
9. requests implemented by you developed python for predictions may be done from any computer on web up to 1234 requests per second , everybody with credentials in web can create requests (not ssh or any remote connection to AWS web site)
10. no GUI design, but all done by python SDK - only python code used
11. ML will be provided or you can use your ML model if I like it
12. If you need Docker use Docker, but better if you know how do to this without Docker, since docker makes calculations to be slow
YOU PROVIDE PYTHON CODE AND
VIDEO of yours or our online meeting
WITH EXPLANATIONS STEP BY STEP HOW TO DO
---------------------------------------------------------------
PROJECT ACCEPTANCE : I CAN DO BY MYSELF on my AWS account
----------------------------------------------------------------
TIME MAX 1 WEEK
Testing : I use your python code which hosted on my AWS cloud project created by me using your instructions
I provide to John, Peter, Ana, Paul.. etc, only one the same web link and only python code for 1) request implementation for test data
2) receiving response and comparing with prediction calculated locally
3) Requests are sent by each person in parallel: 1234 requests per second combined for all of them (not each one 1243), for requests generation you can use data from train data set. Make sure there are no dropped requests ( requests without response )
4) Requests consists of data sample and unique Request ID. Machine learning model code receives data and Request ID. Then returns prediction and the same Request ID concatenated with date time of processing. Response: id and prediction and date time
Many requests are sent asynchronously in batches or continuously. When predictions are received: unique ID is used to match prediction and request
Id to confirm it is the same request what was sent for prediction