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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

Amazon Sagemaker Tool for MLOps

Mobarak Inuwa
Mobarak Inuwa Last Updated : 13 Nov, 2024
6 min read

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

Introduction

Learning its tools is one way to start having a hands-on understanding of MLOps. Understanding the tools makes the concept more practical and easy to grasp. MLOps finds its importance in place of model deployment. In this article, we will briefly see what MLOps is before seeing the Sagemaker tool that can make MLOps easy to implement and manage. These tool help to improve the overall deployment of machine-learning projects while still saving time and cost. People still finding the concept unclear could understand better by understanding the tools that can be used, and here we see the Amazon Sagemaker.

What is MLOps?

The term ‘’MLOps” comes from two words “machine learning” and the practice of DevOps in software development. It is an emerging field that is rapidly gaining momentum amongst Data Scientists, ML Engineers, and AI enthusiasts.

MLOps is a set of procedures that machine learning (ML) practitioners adhere to in order to speed up the deployment of ML models in actual projects and improve the overall integration of various project pipeline processes. It is a set of practices that aims to deploy maintainable machine learning models in production with reliability and efficiency.

The Continuous Delivery Foundation SIG MLOps presents a differentiation of the ML models management from traditional software engineering by suggesting the following MLOps strengths:

  • MLOps aims to unify the release cycle for machine learning and software application releases.
  • MLOps enables automated testing of machine learning artifacts (e.g. data validation, ML model testing, and ML model integration testing)
  • MLOps enables the application of agile principles to machine learning projects.
  • MLOps enables supporting machine learning models and datasets to build these models as first-class citizens within CI/CD systems.
  • MLOps reduces technical debt across machine learning models.
  • MLOps must be a language-, framework-, platform-, and infrastructure-agnostic practice.

MLOps systems are expected to be robust by having the above features. The model artifacts need to be built so that they contain all the information needed for data preprocessing. A model artifact is a collection of files produced by a training job required for model deployment. In AWS Sagemaker API, they are the output that results from training a model and typically consist of trained parameters, a model definition that describes how to compute inferences, and other metadata.

After building model artifacts, they have to be able to track the code that builds them, the data they were trained and tested on, and how they are related. With this ability, production and running them such that frequently delivering apps to customers through automation in the stages of app development becomes possible. ML apps can be continuously deployed, integrated, and delivered through CI/CD process efficiently.

MLOps Tools

The type of tool to employ depends on the nature of the project. We will see a number of them and some of their features so that it can be understood how they best fit in. There is nothing too catchy about the term tools used here. It is just a means of implementing MLOps. Here we see these tools as professional platform management. Seeing the features of the tools will explain how they can be used in any part of the ML lifecycle. Below are some of the most common in the field.

Amazon SageMaker

Amazon SageMaker is a cloud-based machine-learning platform that has been around for half a decade and launched in November 2017. It creates, trains, and deploys machine-learning models in the cloud. Amazon SageMaker is a fully managed machine learning platform making it easy for data scientists and developers to build and train machine learning models quickly and then deploy them into production. It makes it easier to get started with ML workflow by providing features like Amazon SageMaker JumpStart, which contains built-in algorithms and pre-built machine learning (ML) solutions that can be deployed with just a few clicks.

MLOps
Source: Rhinestone-SageMaker-Studio-Page-2-v2 Src: aws

It contains hundreds of built-in algorithms and pre-trained models from model hubs, including TensorFlow Hub, PyTorch Hub, HuggingFace, and MxNet GluonCV. Carrying out common machine learning tasks becomes very easy.

All these features make implementing MLOps very easy and fast. Amazon SageMaker provides MLOps to help developers automate and standardize processes throughout the ML lifecycle. This increases productivity by training, testing, troubleshooting, deploying, and governing ML models.

MLOps
Source: Amazon Jumpstart home screen

Apart from Amazone Sagemaker Jumpstart, a few other features available to facilitate MLOps include;

SageMaker Studio; It provides an integrated machine-learning environment for building, training, deploying, and analyzing models. SageMaker Studio provides all the tools to facilitate a good workflow, from data preparation to experimentation to production, making implementing MLOps convenient.

SageMaker Ground Truth Plus; is a feature that helps to provide the service of data labeling so that it can be ready to use in any project as a completed product. It uses an expert workforce to deliver quality, saving time and reducing costs. All this comes without having to build labeling applications and manage the workforce.

SageMaker Model Building Pipelines; is a tool for building machine learning pipelines benefitting from direct SageMaker integration. This integration helps create a pipeline and set up SageMaker Projects for orchestration. This helps to improve MLOps activity in the development process.

SageMaker Debugger; This is a powerful tool for ensuring the control of bugs. It does not only detect bugs but also sends details by profiling them from the training job, making the ML models highly robust. These debuggers send alerts when bugs or anomalies are found. It can identify the root cause and take action against the problems using metrics and tensors. Inspecting training parameters in the training process becomes very easy.

SageMaker Debugger

SageMaker Model Monitor; Monitoring is another important feature on the list. Monitoring and analyzing models in production to detect unwanted scenarios such as data drift and deviations in model quality in production becomes easier. It provides the ability to set up continuous monitoring in real-time or batches or on-schedule monitoring for asynchronous batch transform jobs. It is possible to set alerts that notify when there is a deviation in the model quality. It functions like the debugger, except it doesn’t send alerts on model and data, not an error.

This also saves time by reducing the damages caused by anomalies or drifts in the production process. Retraining is managed and reduced. The monitoring could be set for monitoring data, model, Bias Drift for Models in Production, and monitoring feature attribution drift for models in production.

Model Deployment and Monitoring for Drift

Preprocessing; This deals with processing such as feature engineering, data validation, model evaluation and interpretation, and evaluation of models. It provides a simplified, well-managed experience for running data processing workloads. such as feature engineering, data validation, model evaluation, and model interpretation. It also provides a way of post-monitoring by using APIs during the experimentation phase and after the code is deployed. This is a very good feature for MLOps.

MLOps
Processing

Many other tools provided by Amazon Sagemaker are not covered here in this article. I believe these are the most effective ones in the MLOps discussion.

With the benefits of ML, many businesses are considering getting into the technology. This has caused a hike in the need for effective ways of delivering this industrial requirement. The appropriate tool can assist these organizations in managing everything from data preparation to deploying at a meager cost.

Conclusion

Managing a complete lifecycle on a scale using MLOps is challenging. Solving machine learning business-like problems requires a DevOps mentality of software development which brings about the technique known as MLOps. MLOps becomes easily achievable with sophistication, such as the Amazon Sagemaker. No doubts Amazon SageMaker is a good MLOps platform for many reasons. With edge-cutting features, it provides a very large range of ways to develop models that perform very well in meeting MLOps standards. Big projects can easily be done with the platform helping teams with details and alerts from data to model anomalies and even reporting bugs. One thing that Sagemaker has dealt with is saving time and cost.

Key Takeaways;

  • Understanding the tools of MLOps could make the concept more practical and easy to grasp.
  • MLOps is a set of procedures that machine learning (ML) practitioners adhere to to speed up ML models’ deployment in actual projects and improve the overall integration of various project pipeline processes.
  • The type of MLOps tool to employ depends on the nature of the project. Seeing a number of them and some of their features could make it understandable how they best fit in.
  • Amazon SageMaker is a fully managed machine learning platform making it easy for data scientists and developers to quickly build and train machine learning models and then deploy them into production.

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

Mobarak Inuwa
Mobarak Inuwa

I am an AI Engineer with a deep passion for research, and solving complex problems. I provide AI solutions leveraging Large Language Models (LLMs), GenAI, Transformer Models, and Stable Diffusion.

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