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It covers how to frame business problems as ML problems, select suitable models, and understand domain areas such as modeling plus machine learning implementation and operations. The chapter explains practical AWS offerings including Amazon SageMaker for building, training, and deploying models, and Amazon Rekognition for image and video analysis, detailing deep learning concepts like CNNs, transfer learning, object detection, and semantic segmentation along with common use cases.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":20,"@type":76,"position":81},"https://docshare.wps.com/document/exam/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/aws-ai-ml-stack-chapter-1-exam-objectives-covered/317476/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/aws-ai-ml-stack-chapter-1-exam-objectives-covered/317476.png","ImageObject",300,407,{"name":92,"@type":93},"Aurora","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-20",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",13,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What exam objectives are covered in this chapter under Domain 3.0 Modeling?","Question",{"text":113,"@type":114},"The chapter covers framing business problems as ML problems, selecting appropriate model(s), and example ML architectures for common business workflows like video analysis and text mining, including algorithms for unstructured data such as text and images.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What does Domain 4.0 cover regarding machine learning implementation and operations?",{"text":118,"@type":114},"It focuses on recommending and implementing the appropriate ML services and features for a given problem, plus details about algorithms for different ML use cases and when to use the proper AWS AI/ML service.",{"name":120,"@type":111,"acceptedAnswer":121},"How does Amazon Rekognition help with image and video analysis in applications?",{"text":122,"@type":114},"It enables implementing image or video analysis workflows such as classification, object detection, text detection in images, facial recognition, sentiment, and public safety use cases, using pretrained or custom models without requiring extensive ML or deep learning expertise.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},317476,1790616599,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":19,"category_name":20,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":146,"preview_page_count":4},4810365810221,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Chapter  \n1  \nAWS AI ML Stack  \nTHE AWS CERTIFIED MACHINE LEARNING (ML) SPECIALTY EXAM OBJECTIVES COVERED IN THIS CHAPTER INCLUDE BUT ARE NOT LIMITED TO THE FOLLOWING:  \n✓ Domain 3.0: Modeling  \n■ 3.1 Frame business problems as machine learning problems  \n■ 3.2 Select the appropriate model(s) for a given machine learning problem  \n■ Sample ML architectures for common business workflows such as video analysis, text mining, and others  \n■ Details about some common algorithms used in solving complex problems involving unstructured data like text and image  \n✓ Domain 4.0: Machine Learning Implementation and Operations  \n■ 4.2 Recommend and implement the appropriate machine learning services and features for a given problem  \n■ Details about algorithms for different ML use cases  \n■ Details about when to use the proper AWSAI/ML Service  \nIn this chapter, you will learn about different AWS Services for Machine Learning, starting with the artificial intelligence  \n(AI) services for common machine learning (ML) tasks such  \nas image and video analysis, natural language processing, text-to-speech conversion or viceversa, or building recommendation systems or time-series forecasting into your applications. These services make it easy for you to build ML-powered applications without machine learning experience. You will then learn about Amazon SageMaker, which is a fully managed service for data scientists and machine learning developers to build, train, and deploy ML models in the AWS cloud for various business applications. For reference, the exam guide can be found at [https://d1.awsstatic.com/training-and-certification/](https://d1.awsstatic.com/training-and-certification/)[ ](https://d1.awsstatic.com/training-and-certification/)[docs-ml/AWS-Certified-Machine-Learning-Specialty_Exam-Guide.pdf](docs-ml/AWS-Certified-Machine-Learning-Specialty_Exam-Guide.pdf.)[.](docs-ml/AWS-Certified-Machine-Learning-Specialty_Exam-Guide.pdf.)  \nAmazon Rekognition  \nAmazon Rekognition is an AI service that makes it easy for users to implement image or video analysis workflows into their applications. Amazon Rekognition aims to leverage Amazon’s vast experience in using deep learning for various image-based workloads such as image classification, object detection, detection of text in image, facial recognition, sentiment, and most recently, public safety.  \nAlthough there is a vast amount of deep learning research behind developing models for image analytics, training these deep learning models is often computationally expensive and can take several cycles of data scientist or developer time. That’s where Amazon Rekognition comes in. With Amazon Rekognition, developers can simply leverage pretrained models or train custom machine learning models without having to worry about writing the algorithm code, or about setting up or managing the infrastructure to train and deploy a deep learning model. More importantly, you don’t require any prior machine learning or deep learning knowledge to use this service.  \nBefore diving into Amazon Rekognition, let’s quickly grasp the lay of the land on the subject of images and videos in deep learning. Image recognition typically relies on convolutional neural network (CNN) architectures. CNNs are deep learning algorithms consisting of alternating convolutional layers, which apply various filters on the input data to capture different information at different scales, followed by pooling layers, which reduce the number of parameters in the network and also the spatial size of the representation. The initial layers capture low-level features like edges and curves, whereas latter layers build up to higher-level  \nAmazon Rekognition 5  \nones to eventually identify the object. There are many popular architectures for CNNs such as ResNet or Inception V4, but it is important to understand the basic concept.  \nIt is also useful to understand the concept of transfer learning. Transfer learning refers to taking a model that was ","cbCainlOz01qMyIy","https://ap.wps.com/l/cbCainlOz01qMyIy","pdf",869576,46,"English","# AWS AI ML Stack\n## Exam objectives: Domain 3.0 Modeling\n## Exam objectives: Domain 4.0 ML Implementation and Operations\n## AWS services for ML tasks (AI services overview)\n## Amazon SageMaker (managed ML for building, training, deploying)\n## Amazon Rekognition (image/video analysis workflows)\n## Deep learning foundations: CNNs and transfer learning\n## Object detection architectures (SSD, R-CNN/Faster R-CNN, YOLO)\n## Semantic segmentation and example applications\n## Rekognition key use cases (image labeling, custom image labeling)","[{\"question\":\"What exam objectives are covered in this chapter under Domain 3.0 Modeling?\",\"answer\":\"The chapter covers framing business problems as ML problems, selecting appropriate model(s), and example ML architectures for common business workflows like video analysis and text mining, including algorithms for unstructured data such as text and images.\"},{\"question\":\"What does Domain 4.0 cover regarding machine learning implementation and operations?\",\"answer\":\"It focuses on recommending and implementing the appropriate ML services and features for a given problem, plus details about algorithms for different ML use cases and when to use the proper AWS AI/ML service.\"},{\"question\":\"How does Amazon Rekognition help with image and video analysis in applications?\",\"answer\":\"It enables implementing image or video analysis workflows such as classification, object detection, text detection in images, facial recognition, sentiment, and public safety use cases, using pretrained or custom models without requiring extensive ML or deep learning expertise.\"}]","AWS AI ML Stack - Chapter 1 - Exam Objectives Covered | PDF",1789933398,116]