Using Sagemaker Pipelines get ML models approved and deploy
This course will take you from little or no AWS Sagemaker Pipeline experience top very confident
Description
In this course there are 4 exercises of 3 different types of XGBoost models which are regression, binary classification, and multi class classification. The first two exercises you will get those two models approved for production. Then in the other two videos you will do the same on deploy and make predictions as well. In this video we will cover the 5 necessary pipeline steps and also get more in depth into more machine learning. Not to mention we will cover cross validation in depth and become more confident in getting models approved for production and a better understanding of MLOps. Also the workflow structure as well and learn features of Sagemaker Studio. Do not worry about having slight MLOps knowledge and not being an expert in Machine Learning or Amazon Sagemaker we will cover all of that including monitoring models as well. I will also have 4 quiz questions down below that will not be too easy or to difficult more of making sure that you watched the videos and did the exercises in the videos. But most importantly have fun learning. Don't forget that MLOps is very important in every Data Science project used in every industry because it addresses a common problem of model drift. If you have taken my other course I suggest you take this one as well because this is more of a sequel to the other one. Also if you are taking this course I suggest you take my other course to show you how to deploy various Sagemaker Models on AWS. But the other course does not include MLOps and Sagemaker Pipelines like this one does.
What You Will Learn!
- How to get models approved for Production
- ML workflows
- CI/CD
- Sagemaker Pipelines
- XGBoost
- Default Model Monitor
- Registering a model
- Deployment of a Model on AWS
- Sagemaker Studio
- Hyperparameter Tuning Job
- MLOps
- Processing Job
- Detect Data Drift
- Evaluate Models
- Make predictions of Deployed Models
- Machine Learning Parameters of various XGBoost Models
Who Should Attend!
- Intermediate Python and Machine Learning knowledge who want to learn MLOps with Sagemaker Pipelines
- Someone already in the Industry who wants to learn Sagemaker Pipelines
- Someone who knows Sagemaker deployement of basic model but doesnt know MLOps with Sagemaker Pipelines and wants to learn to get production worthy models deployed