**Introduction to Live Sessions: A New Experiment**

We are experimenting with a new type of live session that will allow us to walk through code step-by-step and help you understand how a specific task is done. This live session will be a continuation of our previous deployment session, where we discussed productionization and deployment of machine learning and deep learning models. In this session, we'll be deploying an ML model using API on Amazon Web Services (AWS), one of the most popular cloud services.

**Two-Part Session**

The two-part session will consist of building an API around a machine learning model and then deploying it into production. The first part will focus on how to build an API around a Python machine learning model, using an Ax scalar model as a sample model. We can do the same thing for any machine learning model built in Python or scikit-learn. In this section, we'll take a simple cyclical load model, which is built using Python, and amplify it.

**Hands-On Session**

This live session will be hands-on, with the goal of providing you with practical experience on how to build an API around a machine learning model and deploy it into production. We'll try to do as much as possible in two hours, but we might overshoot by 15-20 minutes if needed. The session is scheduled for 24th March, from 10 a.m. to 12 p.m., and will be accessible through the desktop app.

**Experimentation**

This live session is an experiment, as we have not tried out hands-on sessions before. We're trying it out on a Windows box, which should be familiar to most of our students who use Windows operating systems. We'll do our best not to mess it up, but we hope that this will also help you get some hands-on exposure on how to build an API around a machine learning model and deploy it into production.

**Introduction Session**

We had an introduction session a few weeks back on various choices for productionization and deployment of machine learning models. This is a continuation session where we're actually deploying it on the real world. We'll take a simple problem, a Python model, and amplify it. In this section, we'll deploy it on a real cloud instance using AWS.

**Slack Channels**

For any questions or suggestions, please mention them below in the comment section of our Slack channels, specifically the "miscellaneous Channel" (Channel 4). We'll try to accommodate as many suggestions as possible in this two-hour session. Thank you, and we look forward to seeing you on 24th March from 10 a.m. to 12 p.m.

"WEBVTTKind: captionsLanguage: enhi folks the next live session that we will have for students of Applied a course is a hands on live session so a hands on live session is one where we will walk through code so where we will walk through code and go step-by-step to help you understand how a specific task is done right this is a new type of live sessions that we are experimenting with right now right so this live session will be a continuation of our deployment session so if you remember we had a live session on production ization and deployment right so we had an earlier we had an earlier live session on introduction to production ization and deployment of machine learning and deep learning models so this is an extension to that introduction session where in this session we'll actually deploy an ml model using AP ice on Amazon Web Services which is one of the most popular cloud services right so this live session will consists of two parts literally the first part will be how to actually build an API how to basically build an API around a machine learning model that you've trained so we'll take machine learning models built in Python and we will use an ax scalar model as a sample model but we can do the same thing for any machine learning model built in by three right it could be an XG boost model a scikit-learn model whatever it is but we'll start with a simple cyclic load model which is built using Python and we will amplify it if I can call that so right so we'll build an API around the whole Python model or the Python or scikit-learn machine learning model that's a first part the second part here is once you have built it right we will show you how to deploy how to deploy this model how to deploy this model using API sub course so we are using the you are deploying the model using API on one of the most popular Club services called AWS and we will see how to do it using something called as ec2 boxes okay ec2 is is Elastic Compute cloud it is it is basically a service on Amazon Web Services which rents computers to you at at some fixed price per arc right we will we will show you so the two parts to it right first part is how to build any API around a Python machine learning model the second part is how to deploy it how to deploy this API based model on a tablet using ec2 and we try to do as much hands-on as possible and this live session is on 24th of March 2019 which is which is the next Sunday which is the which is the coming Sunday and we'll do it in a regular time from 10 a.m. to 12 p.m. we might overshoot a little if this takes more time than we expect we will try and finish everything in two hours but we might overshoot it by 15-20 minutes so please come prepared to sit through the discussion for 10 15 more minutes extra if we need it right and this is accessible to all of the registered students through the desktop app that we have and again by the way this is the first experiment to be honest this is an experiment that we are conducting this is the first hands-on session that we had we are trying out because still of we have not tried out hands-on sessions we have tried out more informaiton sessions - laughs right a hands-on session we're trying we're trying to do this the first time and we also do it on a Windows box so we'll try and do it on a Windows box as most of our students have Windows operating system and not Mac and Linux ok we'll try again these all stuff again this is a big experiment for us we've never done this before so we'll try our best not to mess it up but let's hope so let's it's an experiment for all of us ok and I hope this will also help you get some hands-on exposure on how to actually build an API around a machine learning model and actually deploy it into production right so this is a contribution to the introductory session remember we had an introduction session a few weeks back on various choices we have for production ization and deployment of machine learning models this is a continuation session where we're actually deploying it on the real world so we will take a we'll take a simple problem we'll take a Python model and amplify it and then deploy it on a real cloud instance ok hope you see you there hope to see you on 24th March on the desktop app from 10 a.m. to 12 p.m. and we'll use our slack channels so on our slack we have the miscellaneous Channel right so we'll use miss Lina channel 4 for all of the live Q&A just the way we have been doing in the last few sessions and any points that you want us to cover right any any suggestions you have any suggestions you have please mention please mention those suggestions below this video just under this in the comment section we'll try and accommodate as many suggestions as we can in this two-hour session thank you folks see you soonhi folks the next live session that we will have for students of Applied a course is a hands on live session so a hands on live session is one where we will walk through code so where we will walk through code and go step-by-step to help you understand how a specific task is done right this is a new type of live sessions that we are experimenting with right now right so this live session will be a continuation of our deployment session so if you remember we had a live session on production ization and deployment right so we had an earlier we had an earlier live session on introduction to production ization and deployment of machine learning and deep learning models so this is an extension to that introduction session where in this session we'll actually deploy an ml model using AP ice on Amazon Web Services which is one of the most popular cloud services right so this live session will consists of two parts literally the first part will be how to actually build an API how to basically build an API around a machine learning model that you've trained so we'll take machine learning models built in Python and we will use an ax scalar model as a sample model but we can do the same thing for any machine learning model built in by three right it could be an XG boost model a scikit-learn model whatever it is but we'll start with a simple cyclic load model which is built using Python and we will amplify it if I can call that so right so we'll build an API around the whole Python model or the Python or scikit-learn machine learning model that's a first part the second part here is once you have built it right we will show you how to deploy how to deploy this model how to deploy this model using API sub course so we are using the you are deploying the model using API on one of the most popular Club services called AWS and we will see how to do it using something called as ec2 boxes okay ec2 is is Elastic Compute cloud it is it is basically a service on Amazon Web Services which rents computers to you at at some fixed price per arc right we will we will show you so the two parts to it right first part is how to build any API around a Python machine learning model the second part is how to deploy it how to deploy this API based model on a tablet using ec2 and we try to do as much hands-on as possible and this live session is on 24th of March 2019 which is which is the next Sunday which is the which is the coming Sunday and we'll do it in a regular time from 10 a.m. to 12 p.m. we might overshoot a little if this takes more time than we expect we will try and finish everything in two hours but we might overshoot it by 15-20 minutes so please come prepared to sit through the discussion for 10 15 more minutes extra if we need it right and this is accessible to all of the registered students through the desktop app that we have and again by the way this is the first experiment to be honest this is an experiment that we are conducting this is the first hands-on session that we had we are trying out because still of we have not tried out hands-on sessions we have tried out more informaiton sessions - laughs right a hands-on session we're trying we're trying to do this the first time and we also do it on a Windows box so we'll try and do it on a Windows box as most of our students have Windows operating system and not Mac and Linux ok we'll try again these all stuff again this is a big experiment for us we've never done this before so we'll try our best not to mess it up but let's hope so let's it's an experiment for all of us ok and I hope this will also help you get some hands-on exposure on how to actually build an API around a machine learning model and actually deploy it into production right so this is a contribution to the introductory session remember we had an introduction session a few weeks back on various choices we have for production ization and deployment of machine learning models this is a continuation session where we're actually deploying it on the real world so we will take a we'll take a simple problem we'll take a Python model and amplify it and then deploy it on a real cloud instance ok hope you see you there hope to see you on 24th March on the desktop app from 10 a.m. to 12 p.m. and we'll use our slack channels so on our slack we have the miscellaneous Channel right so we'll use miss Lina channel 4 for all of the live Q&A just the way we have been doing in the last few sessions and any points that you want us to cover right any any suggestions you have any suggestions you have please mention please mention those suggestions below this video just under this in the comment section we'll try and accommodate as many suggestions as we can in this two-hour session thank you folks see you soon\n"