Automated Machine Learning with Amazon SageMaker Autopilot

Introduction

In recent years, data science has witnessed significant advancements, with machine learning algorithms playing a pivotal role in extracting valuable insights from vast data. However, developing effective machine learning models can be complex and time-consuming, requiring expertise in data preprocessing, feature engineering, model selection, and hyperparameter tuning. To address these challenges, Amazon Web Services (AWS) introduced Amazon SageMaker Autopilot, a revolutionary tool that automates the machine learning workflow. In this blog, we will explore the capabilities and benefits of SageMaker Autopilot and delve into a hands-on lab to witness its power.

Amazon SageMaker Autopilot

Amazon SageMaker Autopilot is a fully managed service that automates the end-to-end process of developing machine learning models. From data preprocessing to model selection and hyperparameter tuning, Autopilot takes care of it all.

The core idea behind Amazon SageMaker Autopilot is to enable data scientists, developers, and business analysts to build high-quality ML models without extensive manual intervention.

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Key Features of Amazon SageMaker Autopilot

Automated Data Preprocessing: Amazon SageMaker Autopilot streamlines the data preprocessing phase, handling missing values, encoding categorical features, and performing feature scaling automatically. This saves time and reduces the risk of errors in data preparation.
Model Selection: Amazon SageMaker Autopilot employs a range of algorithms, from traditional linear models to advanced deep learning architectures, to identify the most suitable model for the given dataset. It iteratively tests various algorithms, selects the best performers, and optimizes them further.
Hyperparameter Tuning: Fine-tuning the hyperparameters of a machine learning model is critical to achieving optimal performance. Amazon SageMaker Autopilot uses Bayesian optimization to efficiently search for the best hyperparameters, leading to improved model accuracy.
Automatic Model Documentation: Understanding and documenting the model’s decisions are essential for transparency and compliance. Amazon SageMaker Autopilot generates comprehensive model reports, explaining the underlying model logic and decision-making process.

Benefits of Amazon SageMaker Autopilot

Time and Cost Efficiency: Amazon SageMaker Autopilot significantly reduces the time and effort required to build a machine learning model. Data scientists can focus on interpreting results and extracting insights instead of dealing with repetitive tasks.
Ease of Use: Amazon SageMaker Autopilot requires no prior knowledge of machine learning or coding expertise. Its user-friendly interface allows individuals from various domains to leverage the power of ML without a steep learning curve.
Scalability: AWS’s infrastructure enables Amazon SageMaker Autopilot to handle large datasets and complex ML problems, making it suitable for various applications.

Steps to Create a Machine Learning Model in Amazon SageMaker Autopilot

Login to your AWS account
Go to Amazon SageMaker and Click on studio. Then create a domain using the quick start.
Once the domain is created, then under launch, click on studio.

step3

4. Go to File, then go to a new file and then click on Notebook.

step4

5. For setting up the environment, we need to select an image as data science, Kernel as Python 3, and instance type. For now, we have chosen ml.t3.medium.

step5

First, we need to extract the sample data from Amazon SageMaker Autopilot. We will use below code:

%%sh
apt-get install -y unzip
wget https://sagemaker-sample-data-us-west-2.s3-us-west-2.amazonaws.com/autopilot/direct_marketing/bank-additional.zip
unzip -o bank-additional.zip

%%sh

apt-get install -y unzip

wget https://sagemaker-sample-data-us-west-2.s3-us-west-2.amazonaws.com/autopilot/direct_marketing/bank-additional.zip

unzip -o bank-additional.zip

7. Now load the dataset

import pandas as pd
data = pd.read_csv('./bank-additional/bank-additional-full.csv')
data[:10]

import pandas as pd

data = pd.read_csv('./bank-additional/bank-additional-full.csv')

data[:10]

8. Upload dataset into Amazon S3 bucket

import sagemaker
prefix = 'sagemaker/tutorial-autopilot/input'
sess   = sagemaker.Session()
uri = sess.upload_data(path="./bank-additional/bank-additional-full.csv", key_prefix=prefix)
print(uri)

import sagemaker

prefix = 'sagemaker/tutorial-autopilot/input'

sess = sagemaker.Session()

uri = sess.upload_data(path="./bank-additional/bank-additional-full.csv", key_prefix=prefix)

print(uri)

9. For creating experiment go to AutoML and click on create experiment:

step9

step9b

step9c

step9d

10. After filling in the above details, click on Create an experiment, and Amazon SageMaker Autopilot automatically performs data preprocessing, model selection, and hyperparameter tuning.

11. Once training is completed, we can see the different type of models created and their accuracy. Amazon SageMaker Autopilot will also suggest the best model.

step11

12. After completion of the experiment, we can choose the best model and deploy the model to the Amazon SageMaker endpoint.

step12

Conclusion

Amazon SageMaker Autopilot represents a significant leap forward in automated machine learning. By automating the end-to-end ML process, Amazon SageMaker Autopilot empowers data scientists and developers to focus on higher-value tasks. It enhances the accessibility of machine learning for a broader audience. Its time and cost efficiency, ease of use, and scalability make it a compelling choice for organizations seeking to harness the potential of AI and machine learning. Embrace Amazon SageMaker Autopilot today and embark on a data science journey that drives innovation and unlocks unprecedented insights from your data.

Drop a query if you have any questions regarding Amazon SageMaker Autopilot and we will get back to you quickly.

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FAQs

1. What types of machine learning problems can be solved using Amazon SageMaker Autopilot?

ANS: – Amazon SageMaker Autopilot is designed to handle both classification and regression problems. Amazon SageMaker Autopilot can automatically select the appropriate algorithms and hyperparameters to build accurate machine learning models, whether you have a dataset for predicting categories or continuous numerical values. It supports many algorithms, from traditional ones like linear regression and logistic regression to more complex models like XGBoost, Random Forest, and deep learning architectures.

2. Can I customize the machine learning pipeline generated by Amazon SageMaker Autopilot?

ANS: – While Amazon SageMaker Autopilot is primarily designed for automation, it provides some degree of customization flexibility. For instance, you can specify constraints for feature engineering, such as excluding certain features or applying specific data transformations. Additionally, you can set certain hyperparameter ranges to guide the hyperparameter tuning process. However, the true power of Amazon SageMaker Autopilot lies in its ability to automate most of the machine learning workflow, so extensive customization is limited.

3. How does Amazon SageMaker Autopilot handle imbalanced datasets in classification problems?

ANS: – Imbalanced datasets, where the number of samples in different classes is significantly uneven, can pose challenges for machine learning models. Amazon SageMaker Autopilot addresses this issue by employing class weights and synthetic data generation techniques. Class weights give higher importance to underrepresented classes, helping the model learn from them effectively. Synthetic data generation involves creating additional samples for minority classes, further balancing the dataset. By automatically applying these techniques, Amazon SageMaker Autopilot enhances the model’s ability to handle imbalanced data and produce reliable predictions for all classes.

WRITTEN BY Hridya Hari

Hridya Hari works as a Research Associate - Data and AIoT at CloudThat. She is a data science aspirant who is also passionate about cloud technologies. Her expertise also includes Exploratory Data Analysis.