{"id":97298,"date":"2024-09-03T15:46:48","date_gmt":"2024-09-03T10:16:48","guid":{"rendered":"https:\/\/www.whizlabs.com\/blog\/?p=97298"},"modified":"2024-09-03T15:46:48","modified_gmt":"2024-09-03T10:16:48","slug":"optimizing-machine-learning-models-on-aws","status":"publish","type":"post","link":"https:\/\/www.whizlabs.com\/blog\/optimizing-machine-learning-models-on-aws\/","title":{"rendered":"Deploying and Optimizing Machine Learning Models on AWS"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">This blog discusses how AWS ready-made ML services can help you tame the ML labyrinth with its ability to process large and complex data quickly and focus more on your core competencies. The blog also focuses on <\/span><a href=\"https:\/\/www.whizlabs.com\/aws-certified-machine-learning-specialty\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">AWS Certified Machine Learning Specialty certification<\/span><\/a><span style=\"font-weight: 400;\">, which equips developers like you with the skills to deploy and optimizing machine learning models on AWS and help organizations meet their business objectives. In addition, this certification provides a comprehensive understanding of ML principles and deployment techniques that you can use to harness the full ML potential.<\/span><\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_76 ez-toc-wrap-left counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #ea7e02;color:#ea7e02\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #ea7e02;color:#ea7e02\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.whizlabs.com\/blog\/optimizing-machine-learning-models-on-aws\/#Machine_Learning_Development_Life_Cycle_MLDC\" >Machine Learning Development Life Cycle (MLDC)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.whizlabs.com\/blog\/optimizing-machine-learning-models-on-aws\/#Define_Business_Goal_and_Frame_ML_Problem\" >Define Business Goal and Frame ML Problem<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.whizlabs.com\/blog\/optimizing-machine-learning-models-on-aws\/#Deploying_and_Optimizing_Machine_Learning_Models_on_AWS\" >Deploying and Optimizing Machine Learning Models on AWS<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.whizlabs.com\/blog\/optimizing-machine-learning-models-on-aws\/#Data_Processing_Optimization\" >Data Processing Optimization<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.whizlabs.com\/blog\/optimizing-machine-learning-models-on-aws\/#Model_Optimization\" >Model Optimization<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.whizlabs.com\/blog\/optimizing-machine-learning-models-on-aws\/#Deployment_Optimization\" >Deployment Optimization<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.whizlabs.com\/blog\/optimizing-machine-learning-models-on-aws\/#Performance_Optimization\" >Performance Optimization<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.whizlabs.com\/blog\/optimizing-machine-learning-models-on-aws\/#Security_and_Compliance_Optimization\" >Security and Compliance Optimization<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.whizlabs.com\/blog\/optimizing-machine-learning-models-on-aws\/#Monitoring_and_Maintenance_Optimization\" >Monitoring and Maintenance Optimization<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.whizlabs.com\/blog\/optimizing-machine-learning-models-on-aws\/#AI_Optimization\" >AI Optimization<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.whizlabs.com\/blog\/optimizing-machine-learning-models-on-aws\/#Conclusion\" >Conclusion<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"Machine_Learning_Development_Life_Cycle_MLDC\"><\/span><span style=\"font-weight: 400;\">Machine Learning Development Life Cycle (MLDC)<\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">To streamline the development and deployment of ML models, development teams follow the Machine Learning Development Life Cycle (MLDC) &#8211; a structured, step-by-step iterative framework. The AWS Certified Machine Learning \u2013 Specialty closely aligns with MLDC, and it covers key aspects of each stage of the MLDC. With the knowledge you gather from this course, you will be able to apply MLDC in practice and decide the AWS services apt for each stage.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Machine Learning Development Life Cycle<\/span><\/p>\n<p><img decoding=\"async\" class=\"alignnone wp-image-97313 size-large\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/09\/Machine-Learning-Development-Life-Cycle-1024x270.png\" alt=\"Machine Learning Development Life Cycle.webp\" width=\"1024\" height=\"270\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/09\/Machine-Learning-Development-Life-Cycle-1024x270.png 1024w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/09\/Machine-Learning-Development-Life-Cycle-300x79.png 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/09\/Machine-Learning-Development-Life-Cycle-768x203.png 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/09\/Machine-Learning-Development-Life-Cycle-1536x405.png 1536w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/09\/Machine-Learning-Development-Life-Cycle-2048x540.png 2048w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/09\/Machine-Learning-Development-Life-Cycle-150x40.png 150w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">The four domains of\u00a0 AWS Certified Machine Learning Specialty exam cover the following phases in the MLDC. See <\/span><a href=\"https:\/\/d1.awsstatic.com\/training-and-certification\/docs-ml\/AWS-Certified-Machine-Learning-Specialty_Exam-Guide.pdf\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">CheetSheet<\/span><\/a><span style=\"font-weight: 400;\"> for details.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Table 1. MLDC to Exam domain mapping<\/span><\/p>\n<p><img decoding=\"async\" class=\"alignnone wp-image-97310 size-large\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/09\/MLDC-to-Exam-domain-mapping-1024x694.webp\" alt=\"MLDC to Exam domain mapping\" width=\"1024\" height=\"694\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/09\/MLDC-to-Exam-domain-mapping-1024x694.webp 1024w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/09\/MLDC-to-Exam-domain-mapping-300x203.webp 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/09\/MLDC-to-Exam-domain-mapping-768x520.webp 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/09\/MLDC-to-Exam-domain-mapping-1536x1040.webp 1536w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/09\/MLDC-to-Exam-domain-mapping-2048x1387.webp 2048w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/09\/MLDC-to-Exam-domain-mapping-150x102.webp 150w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Define_Business_Goal_and_Frame_ML_Problem\"><\/span><span style=\"font-weight: 400;\">Define Business Goal and Frame ML Problem<\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Optimizing ML models starts with a clearly defined business goal, which in turn translates into a well-framed ML problem. The business goal is the overarching objective that the ML solution is designed to achieve, and it represents a real-world problem or opportunity the organization aims to achieve. Review your goal to determine if ML is the right solution. If yes, accordingly determine the ML model, data needed, the evaluation metric, and the deployment strategy.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Problem framing describes the ML task that will help achieve the business goal. The problem defines the ideal outcome and the model\u2019s goal, output, and success metrics. Here\u2019s an example of a business goal and its ML problem.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Take the <\/span><b>Whizlabs AWS Certified Machine Learning Specialty course<\/b><span style=\"font-weight: 400;\"> to gain the expert knowledge required for identifying the business problem and identifying the appropriate ML approach.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Example &#8211; Business Goal and ML Problem Framing<\/span><\/p>\n<p><img decoding=\"async\" class=\"alignnone wp-image-97303 size-large\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/09\/ML-Programming-1024x353.webp\" alt=\"Business Goal and ML Problem Framing\" width=\"1024\" height=\"353\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/09\/ML-Programming-1024x353.webp 1024w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/09\/ML-Programming-300x103.webp 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/09\/ML-Programming-768x265.webp 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/09\/ML-Programming-1536x529.webp 1536w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/09\/ML-Programming-2048x706.webp 2048w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/09\/ML-Programming-150x52.webp 150w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Deploying_and_Optimizing_Machine_Learning_Models_on_AWS\"><\/span><span style=\"font-weight: 400;\">Deploying and Optimizing Machine Learning Models on AWS<\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">An optimized ML deployment ensures that models perform at their best while using resources efficiently and providing scalability and reliability in production environments. To ensure a model is optimized in production, you must consider several key factors. For example, data quality, deployment platform, model performance, deployment strategies, and others\u00a0 So, there is not a single approach to improving models. Hence, AWS offers a comprehensive suite of services, allowing you to select the right solution for a specific use case. The<\/span><a href=\"https:\/\/www.whizlabs.com\/blog\/aws-certified-machine-learning-specialty-certification-preparation\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\"> AWS Certified Machine Learning Specialty course<\/span><\/a><span style=\"font-weight: 400;\"> will help you learn how to identify the appropriate AWS services to implement ML solutions.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In general, we can sum up model optimization in the following seven categories.\u00a0 Let\u2019s review the AWS services that contribute to these categories.<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data processing optimization<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Model Optimization<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Performance Optimization<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Deployment Optimization<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Security and Compliance Optimization<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Monitoring and Maintenance Optimization<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AI Optimization<\/span><\/li>\n<\/ol>\n<h3><span class=\"ez-toc-section\" id=\"Data_Processing_Optimization\"><\/span><span style=\"font-weight: 400;\">Data Processing Optimization<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">An ML model is only as good as the data you feed it. Optimizing data processing is crucial for the performance and accuracy of the ML models. Data goes through several steps before it\u2019s ready for ML consumption.<\/span><\/p>\n<p><b>Data Collection and Ingestion:<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"https:\/\/www.whizlabs.com\/blog\/what-is-aws-kinesis\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">AWS Kinesis<\/span><\/a><span style=\"font-weight: 400;\">: capture and process streaming data in real-time. The three components of Kinesis are:<\/span>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Kinesis Data Streams: enable real-time data ingestion<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Kinesis Data Firehose: load data to AWS data stores<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Kinesis Data Analytics: perform real-time analytics<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<ul>\n<li aria-level=\"1\"><span style=\"font-weight: 400;\">Amazon Managed Streaming for Apache Kafka (Amazon MSK): use it for real-time data streaming,\u00a0 to maintain compatibility with your Kafka-based applications.<\/span><\/li>\n<li aria-level=\"1\"><span style=\"font-weight: 400;\">AWS Glue: use it for batch processing of data lakes or data warehouses and for automated extract, load, and transform (ELT) operations.\u00a0<\/span><\/li>\n<\/ul>\n<p><b>Data Storage<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Amazon S3: store structured and unstructured data and use it as a data ingestion destination as it integrates seamlessly with other Amazon Web Services machine learning services.<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Amazon Redshift: store and analyze very large datasets in a petabyte-scale data warehouse that supports a relational database management system (RDBMS).<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Amazon DynamoDB: store non-relational (NoSQL) data with quicker real-time access, real-time inference, and low-latency response time.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Amazon Elastic File System (Amazon EFS): use EFS as a common training data source for ML workloads and applications that run on multiple EC2 instances.<\/span><\/li>\n<\/ul>\n<p><b>Data Transformation<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AWS Glue: automate data extraction, transformation, and loading and integrate data with a serverless approach<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Amazon Elastic MapReduce (Amazon EMR): transform large data sets in a distributed computing environment\u00a0 and get support for Hadoop Distributed File System (HDFC)<\/span><\/li>\n<\/ul>\n<p><b>Data Query and Analysis<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Amazon Athena: query data stored in S3 using SQL without managing any servers.\u00a0<\/span><\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"Model_Optimization\"><\/span><span style=\"font-weight: 400;\">Model Optimization<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Amazon offers the <\/span><a href=\"https:\/\/aws.amazon.com\/sagemaker\/\" target=\"_blank\" rel=\"nofollow noopener\"><span style=\"font-weight: 400;\">AWS SageMaker<\/span><\/a><span style=\"font-weight: 400;\"> platform\u2014a low-code all-in-one umbrella of features and tools for building, training, and deploying machine-learning models:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">SageMaker pre-trained models and built-in algorithms: jumpstart your model building with ready-to-use built-in algorithms that support both supervised and unsupervised learning.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">SageMaker Studio: take advantage of a fully managed integrated development environment (IDE) that decouples code, compute, and storage.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">SageMaker Autopilot: leverages an AutoML solution that automatically creates a model from a given tabular dataset and target column name and displays it in a leaderboard.<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">SageMaker Hyperparameter Tuning: automate the process of finding the best hyperparameter combination for your model.<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"Deployment_Optimization\"><\/span><span style=\"font-weight: 400;\">Deployment Optimization<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Deployment optimization ensures that your ML model is successfully deployed in the production environment. Here are some of the optimization services:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AWS Lambda: runs serverless computing for deploying models without managing servers, which requires low-latency, on-demand inference.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Amazon API Gateway: manage APIs for serving models, enabling scalable and secure access to ML predictions via RESTful APIs.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AWS Fargate: run containerized ML models without managing server infrastructure.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AWS IoT Greengrass: deploy ML models on edge devices such as IoT devices.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AWS Step Functions: create an ML process as a series of steps that make up a workflow and let Step Functions orchestrate the workflows in order.<\/span><\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"Performance_Optimization\"><\/span><span style=\"font-weight: 400;\">Performance Optimization<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">You can improve the efficiency and effectiveness of ML models using the right services and tools. Here are some of the optimization services:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AWS Elastic Inference: accelerate inference for deep learning models by attaching GPU-powered inference acceleration to Amazon EC2 instances.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AWS SageMaker Batch Transform: run batch inference on large datasets in the form of CSV or JSON files.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AWS SageMaker Multi-Model Endpoints: deploy multiple models on a single endpoint, optimizing resource usage.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">SageMaker Endpoints: <\/span><span style=\"font-weight: 400;\">make real-time inference using REST API and enable auto-scaling to handle varying loads.<\/span><\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"Security_and_Compliance_Optimization\"><\/span><span style=\"font-weight: 400;\">Security and Compliance Optimization<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Security and compliance optimization ensures your ML environment is secure from threats and resulting financial losses, and compliant with fraud prevention regulations. Here are some of the optimization services:\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"https:\/\/www.whizlabs.com\/blog\/aws-identity-and-access-management\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">AWS Identity Access Management (AWS IAM)<\/span><\/a><span style=\"font-weight: 400;\">: control access to ML resources using fine-grained access control, least privilege principle, role-based access control, and temporary security credentials. Use a single interface for managing access policies across all AWS services used in ML workflows<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"https:\/\/www.whizlabs.com\/blog\/aws-kms\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">AWS Key Management Service (AWS KMS<\/span><\/a><span style=\"font-weight: 400;\">): manage encryption keys to protect sensitive data in ML workflows. Encrypt training data, model artifacts, and logs in services such as Amazon S3, Amazon RDS, and AWS SageMaker.<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">SageMaker security features:\u00a0 safeguard your ML environment from external threats by running training jobs and deploying models in an isolated network environment (Amazon Virtual Private Cloud \u2013 VPC). Integrate your model with CloudTrail and CloudWatch to detect unusual activities.<\/span><\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"Monitoring_and_Maintenance_Optimization\"><\/span><span style=\"font-weight: 400;\">Monitoring and Maintenance Optimization<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Monitoring and maintenance optimization will enable the ML model to run smoothly and effectively after it\u2019s deployed. Here are the services you can use for optimization:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AWS\u00a0 SageMaker Debugger: get insights during training to identify and resolve issues.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">\u00a0Amazon CloudWatch: monitor models and receive logs metrics from models and infrastructure, showing performance and operational health.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Amazon SageMaker Model Monitor: check monitoring reports on deployed models for data drift and performance degradation, with features to automatically retrain models if necessary.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AWS CloudTrail: track API calls and user activities across AWS services to get visibility into resource usage and changes<\/span><\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"AI_Optimization\"><\/span><span style=\"font-weight: 400;\">AI Optimization<\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Amazon AI services add extra value to AWS ML deployments, providing advanced functionalities and ease of integration that complement the core ML capabilities. These AI services enhance the overall experience and effectiveness of deploying machine learning solutions on AWS.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Amazon Comprehend: analyze and extract insights from the text; integrate large volumes of unstructured text data, such as customer feedback or social media posts into ML workflows for better decision-making.<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Amazon Transcribe: convert spoken language into written text and extract textual data from audio sources like customer service calls, interviews, or lectures, which can be used to train models.<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Amazon Lex: create chatbots and virtual assistants and automate customer interactions with real-time responses.<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Amazon Polly: convert text into lifelike speech in multiple languages and voices and enhance accessibility by providing spoken content for applications.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Amazon Rekognition: identify objects, people, text, scenes, and activities in images and videos for advanced visual analysis and processing.<\/span><\/li>\n<\/ul>\n<p><b>Use Case: Fraud Detection in International roaming<\/b><\/p>\n<p><span style=\"font-weight: 400;\">A telecom operator wants to deploy a real-time fraud detection system that monitors international roaming to safeguard against fraudulent activities and protect their revenue. The system will identify suspicious behavior patterns that deviate from normal user activities, such as unusually high call volumes or data usage in regions where the customer does not typically roam.<\/span><\/p>\n<table>\n<tbody>\n<tr>\n<td><span style=\"font-weight: 400;\">Business Goal<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Problem Framing<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Minimize fraudulent international roaming by 20% by next year.<\/span><\/td>\n<td><span style=\"font-weight: 400;\">The problem is a <\/span><b>binary classification<\/b><span style=\"font-weight: 400;\"> task where the model must identify whether an international roaming transaction is fraudulent or legitimate. The features will include customer usage patterns, roaming locations, device information, and historical roaming activity.<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><span style=\"font-weight: 400;\">Figure.\u00a0 AWS Services for Optimizing the ML Deployment<\/span><\/p>\n<p><img decoding=\"async\" class=\"alignnone wp-image-97308 size-large\" src=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/09\/ML-Deployment-1024x585.webp\" alt=\"AWS Services for Optimizing the ML Deployment \" width=\"1024\" height=\"585\" srcset=\"https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/09\/ML-Deployment-1024x585.webp 1024w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/09\/ML-Deployment-300x171.webp 300w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/09\/ML-Deployment-768x439.webp 768w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/09\/ML-Deployment-1536x878.webp 1536w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/09\/ML-Deployment-2048x1170.webp 2048w, https:\/\/www.whizlabs.com\/blog\/wp-content\/uploads\/2024\/09\/ML-Deployment-150x86.webp 150w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">The preceding figure illustrates the different Amazon Web Services machine learning services used for optimizing model deployment:<\/span><\/p>\n<p><b>Data optimization<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Telecom training data is batch transferred or streamed using\u00a0 Kinesis, into an S3 bucket. Glue (Data Catalog) stores the data.<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">SageMaker (Data Wrangler)\u00a0helps transform the data into features. Data is sourced directly from\u00a0Amazon S3\u00a0or using\u00a0Amazon Athena\u00a0queries. Features are stored in\u00a0Amazon SageMaker (Feature Store).\u00a0<\/span><\/li>\n<\/ul>\n<p><b>Model Optimization<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">SageMaker trains the model for fraud detection and validates it for real-world use.<\/span><\/li>\n<li aria-level=\"1\"><span style=\"font-weight: 400;\">Trained models are stored in\u00a0SageMaker\u00a0(Model Registry to track).<\/span><\/li>\n<\/ul>\n<p><b>Deployment Optimization<\/b><b><br \/>\n<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Amazon SageMaker Model Monitor\u00a0monitors model quality over time, including data and model quality, and bias drift.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">SageMaker\u00a0Endpoints provide near real-time inference and deploy the model.\u00a0\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">At inference time, data from the telecom operator and partners is streamed in using\u00a0 Kinesis\u00a0to a Lambda\u00a0function.\u00a0API Gateway\u00a0provides features to control access to the model endpoint.\u00a0<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">The telecom operator consumes the fraud detection results the model produces.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span><span style=\"font-weight: 400;\">Conclusion<\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">AWS remains a popular choice for ML deployment for three main factors: scalability, security, and cost-effectiveness. By seamlessly integrating non-ML services with ML services, AWS facilitates a comprehensive solution and streamlined workflows. Such a cross-functional solution also needs a deployment team with a profound understanding of ML concepts and services to tap the boundless potential of model optimization. Earning the AWS Certified Machine Learning &#8211; Specialty (MLS-C01) is a testament to your ability to design, build, deploy, and maintain machine learning solutions.\u00a0Sign up for Whizlabs AWS Certified Machine Learning Specialty course to earn this certificate. For additional hands-on experience of the services, check our <\/span><a href=\"https:\/\/www.whizlabs.com\/labs\/library\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">AWS hands-on labs<\/span><\/a><span style=\"font-weight: 400;\"> and <\/span><a href=\"https:\/\/www.whizlabs.com\/labs\/sandbox\/aws\/aws-sandbox\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">AWS sandboxes<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This blog discusses how AWS ready-made ML services can help you tame the ML labyrinth with its ability to process large and complex data quickly and focus more on your core competencies. The blog also focuses on AWS Certified Machine Learning Specialty certification, which equips developers like you with the skills to deploy and optimizing machine learning models on AWS and help organizations meet their business objectives. In addition, this certification provides a comprehensive understanding of ML principles and deployment techniques that you can use to harness the full ML potential. Machine Learning Development Life Cycle (MLDC) To streamline the 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Maniraj","author_link":"https:\/\/www.whizlabs.com\/blog\/author\/sudha-maniraj\/"},"uagb_comment_info":15,"uagb_excerpt":"This blog discusses how AWS ready-made ML services can help you tame the ML labyrinth with its ability to process large and complex data quickly and focus more on your core competencies. The blog also focuses on AWS Certified Machine Learning Specialty certification, which equips developers like you with the skills to deploy and optimizing&hellip;","_links":{"self":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts\/97298","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/users\/415"}],"replies":[{"embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/comments?post=97298"}],"version-history":[{"count":10,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts\/97298\/revisions"}],"predecessor-version":[{"id":97330,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts\/97298\/revisions\/97330"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/media\/97302"}],"wp:attachment":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/media?parent=97298"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/categories?post=97298"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/tags?post=97298"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}