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Build Train and Deploy Machine Learning Models using Amazon SageMaker
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This tutorial will guide you through the process of training and deploying a machine-learning model using Jupyter Notebook on AWS SageMaker. Our specific use case involves predicting flight delays, a common problem in the aviation industry.
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This video will show:
1. How to setup pipeline and its parameters
2. How to setup Data Engineering and Feature Processing Step
3. How to setup ML Model Training Phase and Evaluation Step
4. What needs to be done for ML Model Deployment Phase?
i. Create Model Step
ii. Batch Transformation Step
iii. Register Model Step
iv. Condition Step
Here are the steps we will cover in this video:
Data Loading: We will start by loading the flight data that will be used to train our model. This dataset will provide the necessary information for predicting flight delays accurately.
Visualization: We'll explore the dataset through visualizations, gaining insights into the various features and patterns that can help us understand flight delays better.
Data Transformation: Before training the model, we'll perform necessary data transformations, such as encoding categorical variables and scaling numerical features, to ensure optimal model performance.
Storing Features to S3: We'll store the transformed features in an S3 bucket, a scalable storage solution provided by AWS, which will serve as input for the training process.
Training a Linear Learner Model: Utilizing the features from the S3 bucket, we will train a linear learner model. This model, retrieved from Amazon Elastic Container Service (ECS), will be optimized for predicting flight delays based on the provided dataset.
Model Deployment: Once the training is complete, we will deploy the trained model on AWS SageMaker, making it available for real-time predictions. The deployed model will serve as a reliable tool for predicting flight delays in a production environment.
Real-Time Prediction: With the model successfully deployed, we'll demonstrate how to use it for real-time flight delay prediction. You will witness the power of machine learning as the model predicts delays based on the input data.
Finally, we'll conclude the tutorial by summarizing the key steps and emphasizing the importance of machine learning in accurately predicting flight delays. By following along with this tutorial, you'll gain practical knowledge of training and deploying ML models using Jupyter Notebook on AWS SageMaker, specifically within the context of flight delay prediction.
Amazon SageMaker is a machine learning (ML) service that provides purpose-built tools and optimized infrastructure to build, train, and deploy ML models for virtually any use case, regardless of your ML expertise. In this video, get an overview of Amazon SageMaker capabilities.
#sagemaker #aws #awssagemaker #machinelearning #machinelearningwithpython #machinelearningproject #machinelearningtutorialforbeginners #machinelearningtraining #machinelearningpython #cloudguru #amazonsagemaker
💥CloudWays COUPON CODE: BFCM4040
☝️☝️ USE THE EXCLUSIVE COUPON CODE ABOVE TO GET 40% OFF FOR 4 MONTHS💥along with 40 FREE migrations handled by our expert engineers (valid till 31st December, 2023).
💥Get $200 FREE Credits for signup. So, hurry up!💥
╔═╦╗╔╦╗╔═╦═╦╦╦╦╗╔═╗
║╚╣║║║╚╣╚╣╔╣╔╣║╚╣═╣
╠╗║╚╝║║╠╗║╚╣║║║║║═╣
╚═╩══╩═╩═╩═╩╝╚╩═╩═╝
This video will show:
1. How to setup pipeline and its parameters
2. How to setup Data Engineering and Feature Processing Step
3. How to setup ML Model Training Phase and Evaluation Step
4. What needs to be done for ML Model Deployment Phase?
i. Create Model Step
ii. Batch Transformation Step
iii. Register Model Step
iv. Condition Step
Here are the steps we will cover in this video:
Data Loading: We will start by loading the flight data that will be used to train our model. This dataset will provide the necessary information for predicting flight delays accurately.
Visualization: We'll explore the dataset through visualizations, gaining insights into the various features and patterns that can help us understand flight delays better.
Data Transformation: Before training the model, we'll perform necessary data transformations, such as encoding categorical variables and scaling numerical features, to ensure optimal model performance.
Storing Features to S3: We'll store the transformed features in an S3 bucket, a scalable storage solution provided by AWS, which will serve as input for the training process.
Training a Linear Learner Model: Utilizing the features from the S3 bucket, we will train a linear learner model. This model, retrieved from Amazon Elastic Container Service (ECS), will be optimized for predicting flight delays based on the provided dataset.
Model Deployment: Once the training is complete, we will deploy the trained model on AWS SageMaker, making it available for real-time predictions. The deployed model will serve as a reliable tool for predicting flight delays in a production environment.
Real-Time Prediction: With the model successfully deployed, we'll demonstrate how to use it for real-time flight delay prediction. You will witness the power of machine learning as the model predicts delays based on the input data.
Finally, we'll conclude the tutorial by summarizing the key steps and emphasizing the importance of machine learning in accurately predicting flight delays. By following along with this tutorial, you'll gain practical knowledge of training and deploying ML models using Jupyter Notebook on AWS SageMaker, specifically within the context of flight delay prediction.
Amazon SageMaker is a machine learning (ML) service that provides purpose-built tools and optimized infrastructure to build, train, and deploy ML models for virtually any use case, regardless of your ML expertise. In this video, get an overview of Amazon SageMaker capabilities.
#sagemaker #aws #awssagemaker #machinelearning #machinelearningwithpython #machinelearningproject #machinelearningtutorialforbeginners #machinelearningtraining #machinelearningpython #cloudguru #amazonsagemaker
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