The Next Live Session: Continuing with Amazon SageMaker

This is an announcement about our next live session on the coming Sunday, which is the 29th of August, between 7 to 9 p.m. Again, this live session is accessible to all of our AI course and rural students, as well as our diploma students. It is the second part in a series of live sessions that we're conducting related to Amazon SageMaker.

Just like the previous live session, we will use AirMeet for all of our discussion and also the chat and interactivity during the live session itself. As usual, you will get an invitation to AirMeet via your registered email addresses again in the previous week in the previous live session. In the last live session, we understood the basic architecture of SageMaker, what are the various components when to use them, what are the trade-offs, pros and cons of using SageMaker versus an on-premise system versus something like a cube flow. We have tried to discuss that fairly rigorously and also started this end-to-end example where we take a real-world classical machine learning problem where we use boosting techniques like GBDT and solve it end-to-end using SageMaker.

We only covered one part of it, so in this week's live session, we'll continue where we left off in the previous discussion and try to complete the end-to-end everything from data labeling and cleaning to productionization serving and monitoring of machine learning models for a classical machine learning system where we use techniques like boosting especially a library like XGBoost. Once we finish that, we'll also start with one more example wherein we take a deep learning problem again solve it end-to-end from data labeling, data cleaning all the way to model serving productionization and monitoring.

In this series of live sessions, we wanted to conduct them as a series of small case studies or small examples or small hands-on sessions where we take multiple problems, some in classical machine learning, some in deep learning, some in IoT related machine learning problems and solve them end-to-end from data cleaning and labeling to model productionization serving and monitoring. This way, you will learn how to use a very powerful toolkit or a platform like SageMaker for a wide spectrum of problems. Realistically speaking, this might take us three or four live sessions, but that's okay because we want to do it rigorously and thoroughly so that at the end of it, you can start using SageMaker right away and build end-to-end machine learning systems comfortably on it.

So, see you on the coming Sunday again. Please note the time; it is 7 p.m. to 9 p.m. via AirMeet.

"WEBVTTKind: captionsLanguage: enhi friends this is an announcement about our next live session on the coming sunday which is the 29th of august between 7 to 9 p.m again this live session is accessible to all of our ai course and rural students and also our diploma students and this live session is the second part in a series of live sessions that we're conducting related to amazon sage maker as usual just like the previous live session we will use air meet for all of our discussion and also the chat and interactivity during the live session itself again just like the last week you will get an invitation to airmeet via your registered email addresses again in the previous week in the previous live session we understood the basic architecture of sagemaker what are the various components when to use when not to use what are the trade-offs pros and cons of using sagemaker versus an on-premise system versus something like a cube flow right we have tried to discuss that fairly rigorously and we also started this end-to-end example where we take a real world classical machine learning problem where we use boosting techniques like gbdt and solve it end-to-end using sagemaker we only covered one part of it we could not complete the whole example so in this week's live session we'll continue where we left off in the previous in the previous discussion and we'll try and complete the end to end everything from data labeling and cleaning to productionization serving and monitoring of machine learning models for a classical machine learning system where we use techniques like boosting especially a library like xgboost and we'll also once we finish that we'll also start with one more example wherein we take a deep learning problem again solve it end to end from data labeling data cleaning all the way to model serving productionization and monitoring again in this series of live sessions we wanted to conduct them as a series of small case studies are small examples or small hands-on sessions where we take multiple problems some problems in classical machine learning some in deep learning some in iot related machine learning problems and solve them end to end from data cleaning and labeling to model productionization serving and monitoring this way you will learn how to use a very powerful toolkit or a platform like sagemaker for a wide spectrum of problems and realistically speaking this might take us three or four live sessions but that's okay because we want to do it rigorously and thoroughly so that at the end of it you can start using sagemaker right away and build end-to-end machine learning systems comfortably on it okay so see you on the coming sunday again please note the time it is 7 pm to 9 pm via air meet see youhi friends this is an announcement about our next live session on the coming sunday which is the 29th of august between 7 to 9 p.m again this live session is accessible to all of our ai course and rural students and also our diploma students and this live session is the second part in a series of live sessions that we're conducting related to amazon sage maker as usual just like the previous live session we will use air meet for all of our discussion and also the chat and interactivity during the live session itself again just like the last week you will get an invitation to airmeet via your registered email addresses again in the previous week in the previous live session we understood the basic architecture of sagemaker what are the various components when to use when not to use what are the trade-offs pros and cons of using sagemaker versus an on-premise system versus something like a cube flow right we have tried to discuss that fairly rigorously and we also started this end-to-end example where we take a real world classical machine learning problem where we use boosting techniques like gbdt and solve it end-to-end using sagemaker we only covered one part of it we could not complete the whole example so in this week's live session we'll continue where we left off in the previous in the previous discussion and we'll try and complete the end to end everything from data labeling and cleaning to productionization serving and monitoring of machine learning models for a classical machine learning system where we use techniques like boosting especially a library like xgboost and we'll also once we finish that we'll also start with one more example wherein we take a deep learning problem again solve it end to end from data labeling data cleaning all the way to model serving productionization and monitoring again in this series of live sessions we wanted to conduct them as a series of small case studies are small examples or small hands-on sessions where we take multiple problems some problems in classical machine learning some in deep learning some in iot related machine learning problems and solve them end to end from data cleaning and labeling to model productionization serving and monitoring this way you will learn how to use a very powerful toolkit or a platform like sagemaker for a wide spectrum of problems and realistically speaking this might take us three or four live sessions but that's okay because we want to do it rigorously and thoroughly so that at the end of it you can start using sagemaker right away and build end-to-end machine learning systems comfortably on it okay so see you on the coming sunday again please note the time it is 7 pm to 9 pm via air meet see you\n"