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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

Automate ML Development With Amazon Sagemaker

S
Shiva Last Updated : 03 Jun, 2022
5 min read

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

Introduction on Amazon Sagemaker

Amazon Sagemaker is arguably the most powerful, feature-rich, and fully managed machine learning service developed by Amazon. From creating your own labeled datasets to deploying and monitoring the models on production, Sagemaker is equipped to do everything. It can also provide an integrated Jupyter notebook instance for easy access to your data for exploration and analysis, so you don’t have to fiddle around with server configuration. Sagemaker supports bring-your-own-algorithms and frameworks, which offer flexible distributed training options that adjust to your specific workflows. Pipelines can be created for any machine learning workflow that you can think of which can get quite complex.

Here are some of the important features provided by Amazon Sagemaker.

1. SageMaker Studio:

An integrated ML environment using which you can train, build, deploy, and analyze your models all at the same place.

2. SageMaker Ground Truth

Very high-quality training datasets using workers to create labeled datasets as you like.

3. SageMaker Studio Lab

A free service that gives access to computing resources in the Jupyter environment.

4. SageMaker Data Wrangler

Using Data Wrangler you can import, analyze, prepare, and featurize data in SageMaker Studio. You can integrate it into your ML workflows to simplify data pre-processing or feature engineering using little coding or sometimes no coding. You also have the facility to add your own Python scripts and transformations to customize your data preparation workflow.

5. SageMaker Autopilot

People without ML knowledge can easily build classification and regression models.

Directly jumping into Sagemaker and working with it can be overwhelming especially for people who don’t have good machine learning knowledge and things can really get out of hand really quickly. This is the case where Sagemaker Autopilot can really help people and businesses.

Let us look at how easy it is to use Amazon Sagemaker to develop a Machine Learning model even for a beginner.

Getting Started with Sagemaker Autopilot

  1. Enter the Experiment name and the S3 location where your dataset is present.
Getting started with Sagemaker Autopilot| Amazon Sagemaker

2. Enter the Experiment name and the S3 location where your dataset is present.

(Note that URL must be an s3:// formatted URL where Amazon SageMaker has the write permissions and it must be in the current AWS Region. Also, the file must be in CSV or Parquet format and contain at least 500 rows.)

Getting started with Sagemaker Autopilot 2

 

Getting started with Sagemaker AutopilotM 3| Amazon Sagemaker

3. Next, Tick the toggle switch to on if you wish to provide a manifest file instead of the dataset directly. A manifest file includes metadata about input data along with the location of the data and which attributes from the dataset to be used etc.

Getting started with Sagemaker Autopilot 4

4. Select the target column and enter the output bucket location.

Getting started with Sagemaker Autopilot 5
Getting started with Sagemaker Autopilot 6 Amazon Sagemaker

5. There are a few Advanced options you can set like the Machine learning problem type (Regression, Classification) and choosing how to run your experiment (only generate the candidates or create the model also for immediate deployment).  You can also choose to auto-deploy the model after it is generated.

6. After these steps, you can click on create experiment button which puts the Sagemaker Autopilot to work and generates the

Getting started with Sagemaker Autopilot 7| Amazon Sagemaker
Getting started with Sagemaker Autopilot 8| Amazon Sagemaker

Notebooks Generated

If you remember the S3 bucket output location that we have provided while creating the experiment, it is here the Amazon Sagemaker creates 3 files. They are

  1. data exploration notebook
  2. candidate definition notebook
  3. model insights report

The data exploration notebook describes what Autopilot has learned about the data that you provided to it.

The Candidate definition notebook has information about all the candidates for the model generation. A candidate is nothing but a combination of preprocessing step, algorithm, and hyperparameter ranges. Each candidate has the potential to be the final model based on the metrics that the candidate generates.

Finally,  the model insights report provides the model insights and charts only the best model candidate. This includes understanding false positives/false negatives, tradeoffs between true positives and false positives, and tradeoffs between precision and recall.

Autopilot Algorithm Support

These are 3 types of  algorithms that Amazon Sagemaker Autopilot supports:

  • Linear Learner Algorithm: It consists of a set of supervised learning algorithms used for solving either classification or regression problems.
  • XGBoost Algorithm: It is a supervised learning algorithm that tries to accurately predict a target feature by combining estimates from a set of simpler and weaker models.
  • Deep Learning Algorithm : It  is a multilayer perceptron and feedforward artificial neural network. It can easily handle data that is not linearly separable.

It is not mandatory for you to provide an algorithm type to the Autopilot. Autopilot intelligently understands the problem and automatically chooses the correct algorithm based on the given dataset.

Autopilot Candidate Metrics

Amazon SageMaker Autopilot produces metrics that measure the predictive quality of machine learning model candidates. Here are some of them based on the problem type:

  • Regression:- Mean Absolute Error, Mean Squared Error, R2, Root Mean Square Error.
  • Binary classification:- Accuracy, AUC, F1, LogLoss, Precision, Recall, BalancedAccuracy.
  • Multiclass classification:- Accuracy, F1macro, LogLoss, RecallMacro, PrecisionMacro.

Autopilot Model Deployment

There are three ways to deploy the models generated by the Sagemaker Autopilot. 

  • Automatically:- During the creation of the autopilot experiment, if you select the auto-deploy option, the Sagemaker will automatically deploy the best model from the experiment to an endpoint so that you can start interacting with the model right away. this is recommended for beginners.
  • Manually:- You can choose not to auto-deploy your model for a number of reasons such as, you would wish to look at the steps and make changes to the preprocessing steps or the model itself and sometimes you would want to deploy the model in a completely different environment or cloud provider. In this case, you only need the model artifacts which are stored in the output  S3 location.
  • API calls:- Alternately, You can also deploy your model through API calls. In fact, you can create the entire experiment itself using the AWS SDK library. This is very useful if you wish to create AutoML jobs without logging into the AWS console.

Conclusion

So, to quickly recap, we have used the Amazon AWS Sagemaker Autopilot to create an autopilot experiment that will automatically generate the best ML model for the uploaded dataset. Then we have looked into what is the purpose of each notebook generated by Autopilot. We also discussed which algorithms are supported and what are the different ways to deploy a model generated by autopilot.

Autopilot can help any business to solve problems through machine learning without the need for the businesses having to hire ML experts which can save a lot of money. Even if the company already has a bunch of MLexperts, it speeds up the entire model development process reducing the time to market.

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

Amazon machine learningAmazon Sagemakerblogathonmachine learningML Development
S
Shiva

I am a Machine learning engineer working at TCS. I am extremely motivated and interested in solving machine Learning related problems in the real world. My aim or goal is to disrupt the software industry with world class AI product which helps businesses and people

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