Glad you enjoyed it!

Yeah you can do that. Hosting models for inference only. I’ll keep in mind for other videos.

Sounds like a plan! :)

@@mikegchambers Yes pleasee.

@@kscolina so I’ve been working on a demo project. Can I confirm what is wanted in terms of input and output handlers? Are we talking pipeline model with data pre-processing?

​@@mikegchambers That I am not sure of yet. By the way, I raised a question in a separate reply. :)

Absolutely. You can use notebooks as you would normally, and use the SageMaker SDK to train jobs at scale, and use many more tools like AutoML, Data Wrangler, etc etc. So much power, yet in a familiar interface.

@@mikegchambers Do you mind doing video on AutoML, data wrangler, etc. I swear you can explain ML to my grandmother lol

@@Kmysiak1 on the way!

Hey. I hear your frustration, and I have some thoughts here. So this method is all about running the code at scale, and specifically not running it inside the notebook itself. In other words we are using the notebook, not for ML code, but to orchestrate other infrastructure to run our ML code. So, when I comes to debugging, you would want to do list debugging earlier in in the process, with the ML code (probably in a notebook somewhere) and once your happy with it, we move to this method described here. As for debugging the ‘at-scale’ production deployment, there are ways to do this, that I didn’t cover in this video but I think I should in a future video. I hope that helps put things into perspective. I appreciate you raising that point, and I’ll see how I can clarify for the future.

@@mikegchambers Thank you for the response :) When you say "in a notebook somewhere" are you talking about somewhere in AWS Sagemaker? I would like to use Sagemaker both for development (running on smaller datasets, check that model is correctly setup, monitor convergence etc.) and then later maybe for large scale training. Where do I turn for the former?

@@hejden yes absolutely. So you can spin up a notebook in SageMaker Notebooks, SageMaker Studio, or even SageMaker Studio Labs (for free) and run the ML code in the notebook ’locally’ (to the notebook server). When you’re happy you can ‘migrate’ the code into prod scale as shown here. It’s basically the setup I run through here. I show how the ML code works in the notebook, then get it working in the container using SageMaker. Maybe what I could clarify, is that in this video I use a the same notebook to explore the solution and then get it working in SageMaker managed containers. There is no need to have both these steps in the same notebook, and in many real world scenarios you probably wouldn’t. Steps: - Get your ML code working. Could be done on your own machine, or on a notebook server like SagaMaker Notebooks, Studio, or Studio Labs. This code should include methods to load data, and to serialise and deserialise the model for storage. - Transfer the code into a .py file with the necessary function hooks that SageMaker will be looking for, for the lifecycle of the ML tasks. (Load data, save model, etc). - Create some SageMaker code to orchestrate getting your .py file into a managed SageMaker container. This code can also run anywhere you have access to AWS SDKs, so your own machine, and EC2 instance, a SageMaker Notebook or Studio (but probably not Studio Labs at this time.) - Run your orchestration code and SageMaker will handle the rest. As a preference, I run all code in SageMaker when I can. I don’t like local development and dealing with dependencies etc. it sounds like this is you’re preference too. Make sense? (I’m typing this on my phone, fingers crossed there are not too many typos!)