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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Introduction to AWS SageMaker for Beginner

A
Ankita Last Updated : 29 Feb, 2024
6 min read

Introduction

Data scientists need to create, train and deploy a large number of models as they work. In most environments, they face a lot of difficulties scaling up or down the necessary processes and resources. AWS has created a simple but efficient service called AWS Sagemaker Tutorial to care for this particular problem. In this article, we will cover the salient features of AWS Sage Maker which make it a cost-efficient and efficient tool for all data scientists.

This article was published as a part of the Data Science Blogathon.

Table of contents

AWS in Short

Amazon offers a number of services and on-demand cloud platforms where you can create, deploy as well as monitor applications. Within the cloud platform, a number of effective tools and services such as AWS SageMaker are available which are extremely nifty and useful to practice as well as experienced data scientists.

AWS Sagemaker Tutorial and its Uses

aws sage maker

Amazon has utilized real-world experiences to build a machine learning platform that can help users seamlessly create, deploy and manage ML models. The AWS SageMaker is basically a production-ready environment that hosts all the user-created models and allows the user to scale up or down based on their requirements. This on-demand ML platform comes hand in hand with a number of benefits that are useful for users. Let us discuss what these advantages or benefits are.

Advantages of Using AWS SageMaker

  • Productivity: It allows the user to deploy and manage efficiently thereby reducing the number of delays in working and increasing productivity.
  • Scalability: AWS SageMaker is highly scalable and allows users to scale up or down as per requirements. It also promotes faster model training.
  • Storage: Working with ML models can get storage-intensive pretty quickly. However, AWS SageMaker allows you suitable storage to help with this problem. Now, you can store all necessary ML models and components in one place.
  • Cost: AWS SageMakers reduces the costs of building and deploying ML models by up to 70%.
  • Time Efficient: It helps to create and manage Ec2 compute instances in a time-efficient manner.
  • Continuous Deployment: AWS Sagemaker will analyze the raw data and create, deploy and train a model automatically with open and absolute visibility.
  • Reduces Labeling Tasks: It helps to reduce the overall time which is required for the various data labeling tasks.

Machine Learning possibilities with AWS SageMaker

aws sage maker


ML is made easier using AWS SageMaker. Here, let us discuss how ML is implemented using AWS Sagemaker Tutorial and how can we create, test, tune and deploy an end to end model using this tool.

How to Build?

AWS SageMaker has a compilation of top 10 widely used ML algos ready at your dashboard for builds and training purposes. You can also choose your specific server size and notebook instance. You may also choose to optimize your chosen algorithm using K-means, Linear/Logistic regressions. You also have the option of using the Jupyter notebook interface to customize instances.

Testing and Tuning

To test and tune you first need to set up the required libraries which need to be imported. Then define a few environment variables that need to be managed so that the model can be trained. Then tune and train the model. It has unbuilt hyperparameter tuning which uses a combination of various algorithm parameters. It uses the S3 bucket to store and transfer data as it’s in-house of AWS and also secure and safe.

To deploy docker containers, AWS Sagemaker uses ECR because it is highly scalable. The training data is stored in Amazon S3 but the training algorithm is stored in ECR. It also sets up a cluster by itself to ingest data, train, and store it in the AWS S3 buckets. For doing predictions over an entire dataset, you should use AWS Sagemaker Batch Transform but for limited data, you should go for AWS Sagemaker Tutorial Hosting services.

Deploy and Finalize

When you’re done tuning your model, it will now be ready for deployment. SageMaker endpoints are in charge of real-time predictions and deployment of your model. The predictions help to create insights into whether the business goals are achieved by the ML model you’ve created and deployed. Once this is done, you can evaluate and rate your ML model for future reference and improvements.

Steps on How to Train a Model With AWS Sagemaker?

Let us discuss how to train a model in AWS SageMaker based on ML compute instances

  • First you need to create a training job which may comprise of S3 bucket, ML instance, and inference code image.
  • Your input data for the model should be accessible within the specific S3 bucket. After creating the training jobs, ML compute instances are launched.
  • Now, AWS Sage Maker trains the model using codes and datasets. It also stores the output and artifacts in AWS S3 buckets.
  • In case of failure of the training code, the helper code launches and performs the remaining tasks.

Various Companies Which are Using SageMaker Service

ProQuest, Tinder, Comcast Corp, and more companies regularly make use of AWS Sage Maker service. These companies mainly leverage this service to cut down on operational costs while maintaining standard quality. More than 800 companies regularly use AWS SageMaker amongst which popular usage includes the creation of recommendation systems for users which is widely in demand due to its user-centric nature. The majority of AWS Sage Maker users are situated in the US and UK which contributes to most of its market share. However, more countries are joining in as this relatively newer service is gaining popularity amongst data scientists.

Some statements from the big companies are as follows:

  • Intuit uses Sagemaker to accelerate its AI by deploying the algorithms on the platform. They create their algorithm and solve complex problems for customers dynamically.
  • GE Healthcare uses Sagemaker to improve its patient care. The scalability feature helps them to integrate with other AWS features as required. This opens up new opportunities for better healthcare and universal patient care.
  • ADP Inc uses Sagemaker to identify the workforce patterns and then predict the outcomes intelligently before they occur. Employee turnover is a big issue in many organizations and with Sagemaker we have reduced our model deployment timeline from 2 weeks to 1 day.

Full list of companies and testimonials: Click Here

Conclusion

Coding Deploying and maintaining Machine Learning Models have become a much easier task. It helps to increase your overall productivity by taking care of most parts of a model deployment by itself. It is both a scalable and also cost and time-efficient solution for an organization. The continuo deployment features ensure that the model will be always up and also can be updated during runtime with smooth enrollment and bugs can be removed in early stages before full deployment. AWS SageMaker is a one-stop solution to build, test, tune and then deploy your models and let the AWS service deal with it all of the major parts.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

Frequently Asked Questions

Q1. What is AWS SageMaker and how does it benefit data scientists?

A. AWS SageMaker is a machine learning platform by Amazon that enables users to seamlessly create, deploy, and manage machine learning models. It offers benefits such as increased productivity, scalability, efficient storage, cost reduction, and time efficiency for data scientists, making the model creation and deployment process smoother and more streamlined.

Q2. What are some key features of AWS SageMaker for machine learning tasks?

AWS SageMaker provides a range of features including a selection of pre-built machine learning algorithms, customizable server sizes and notebook instances, hyperparameter tuning, integration with Amazon S3 and ECR for data storage and management, real-time predictions through SageMaker endpoints, and continuous deployment capabilities. These features contribute to its effectiveness in handling various machine learning tasks.

Q3. Which companies are using AWS SageMaker and how are they benefiting from it?

Several companies, including ProQuest, Tinder, Comcast Corp, Intuit, GE Healthcare, and ADP Inc, leverage AWS SageMaker to improve operational efficiency, accelerate AI development, enhance patient care, predict workforce patterns, and reduce model deployment timelines. These companies utilize SageMaker’s scalability, cost-effectiveness, and advanced features to address various business challenges and deliver innovative solutions.

AWSaws sagemakerAWS SageMaker UsesblogathonCloud platformsmachine learning
A
Ankita

Hello Reader, I am a tech writer and data enthusiast. I hope my articles will help you in any way possible and I look forward to keeping on writing with interesting topics for you to read. Feel free to connect with me on LinkedIn or via Gmail. I am a 22 graduate and currently will join Deloitte as a Data Analyst

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