Mastering AI on AWS: Training AWS Certified AI-Practitioner

Explore fundamentals of machine learning, including supervised and unsupervised learning, key algorithms, and training versus inference, with a hands-on lab building and evaluating a simple model on aws services.

Discover supervised learning with labeled data that maps inputs to outputs, and unsupervised learning with unlabeled data that uncovers patterns such as clustering and principal component analysis.

Explore key supervised and unsupervised machine learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines, k means clustering, hierarchical clustering, and PCA, with use cases.

Understand training and inference in machine learning, including supervised learning, labels, input features, and gradient descent optimization across epochs to generalize to unseen data, exemplified by AWS SageMaker workflows.

Explore evaluating machine learning models on AWS using metrics like accuracy, precision, recall, F1, and MSE. Understand confusion matrices, overfitting, underfitting, and cross-validation for deployment readiness.

Engage in a hands-on lab to train a simple binary spam-detection model on AWS SageMaker, covering data preprocessing, training, and evaluation in SageMaker Studio.

Explore AWS AI services for vision, speech, language, and recommendations, and how they support machine learning decision making. Gain hands-on experience with Amazon SageMaker through an AWS AI services lab.

Master Amazon SageMaker to build, train, and deploy machine learning models at scale with an end-to-end pipeline. Use SageMaker Studio, Autopilot, Ground Truth, Neo, and Jumpstart for secure, scalable ML.

Explore AWS AI services for vision, speech, language, and recommendations, including Rekognition, Textract, Polly, Transcribe, Comprehend, Translate, and Personalize.

Discover real-world AWS AI services use cases across industries, from Rekognition image search and content moderation to Personalize recommendations and SageMaker predictive maintenance.

Learn the AI and ML decision-making workflow on AWS, from problem definition and data prep to training, deployment, monitoring, and optimization with SageMaker, Rekognition, and Comprehend.

Participate in a hands-on lab exploring AWS AI services, including Amazon Rekognition for image analysis, Amazon Polly for text-to-speech, and Amazon Comprehend for text analysis, with practical exercises and API usage.

Explore natural language processing with AWS AI services, including comprehend for text analysis, transcribe for speech to text, translate for real time translation, and a hands-on lab with Amazon Comprehend.

Explore natural language processing (NLP) and its applications, including sentiment analysis, entity recognition, translation, speech recognition, and chatbot development using machine learning and deep learning.

Explore how Amazon Comprehend analyzes sentiment, recognizes entities (including custom entities), detects language, extracts keyphrases, and performs topic modeling, with API and AWS services like S3, Lambda, and Kinesis.

Leverage amazon transcribe to convert speech to text with real-time and batch transcription, speaker identification, custom vocabulary, and timestamps, and integrate with Polly for speech synthesis.

Learn to analyze text data with Amazon Comprehend to extract sentiment, named entities, and key phrases, then review language detection results and apply insights to customer feedback and documents.

Explore AWS AI services for computer vision, including Amazon Rekognition for image and video analysis and Amazon Textract for text extraction from documents, with a hands-on lab to process media.

Explore computer vision on AWS, including image classification, object detection, facial recognition, text extraction, and video analysis, while leveraging Rekognition and Textract for scalable, pay-as-you-go analysis.

Learn how Amazon Rekognition enables image and video analysis with object and facial detection, text extraction, content moderation, and real time processing via S3, Kinesis, and API integration.

Explore Amazon Textract, an OCR service that extracts printed and handwritten text, plus forms and tables, from multi-page documents; integrate with S3, Amazon Comprehend, and Lambda for automated workflows.

Explore image and video analysis with Amazon Rekognition to detect objects, faces, and text, and demonstrate end-to-end lab steps from setup to result review.

Explore AWS AI services for speech recognition, including Amazon Polly for text-to-speech and Amazon Transcribe for automatic speech recognition, and build real-time speech interfaces.

Master Amazon Polly, a cloud-based text-to-speech service using deep learning to convert text into natural speech across multiple languages and voices, with support for timestamps and lexicons.

Explore Amazon Transcribe, a fully managed automatic speech recognition service that converts speech to text, with real-time and batch transcription, speaker identification, and time stamping for diverse applications.

Build real-time speech interfaces on AWS by integrating Amazon Polly and Amazon Transcribe to enable voice-driven interactions, supported by AWS Lambda and Amazon Lex for processing and responses.

Build a simple voice interface using Amazon Polly to convert text to speech, synthesize speech, download audio, and customize voice settings, while exploring transcription with Amazon Transcribe and Lambda integration.

Secure AI and ML workloads on AWS by applying encryption, compliance, monitoring, and logging across AWS AI services and SageMaker, with a hands-on lab implementing security best practices.

Understand AWS's shared responsibility security model and implement encryption with KMS, IAM controls, and PrivateLink for AI services like SageMaker, Comprehend, and Rekognition, aligned with ISO 27001, HIPAA, and GDPR.

Encrypt data at rest with KMS for S3, SageMaker, and RDS, secure data in transit with TLS, and protect models and pipelines with AWS Glue, IAM, and VPC.

Monitor and log ai workflows on aws with SageMaker and ai services using CloudTrail, CloudWatch, SageMaker debugger, VPC flow logs, and Model Monitor to detect anomalies and data drift.

Encrypt training data and models in encrypted S3 buckets, secure SageMaker notebooks in a VPC, and monitor activity with CloudWatch and CloudTrail for AI services.

Explore Amazon Personalize for building personalized recommendation engines, examine real-world use cases across e-commerce, media, and healthcare, and complete a hands-on lab creating a personalized recommendation system.

Explore Amazon Personalize, a fully managed machine learning service for real-time, personalized recommendations. Leverage customizable models, recipes, and simple API calls, and integrate with AWS for scalable, data-driven recommendations.

Use Amazon Personalize to build a recommendation engine: prepare data, define schemas, import into S3, select recipe, train a solution version, deploy campaign, and serve real-time recommendations via APIs.

Explore use cases for e-commerce, media, and healthcare with Amazon Personalize, delivering personalized product recommendations, dynamic search results, and content recommendations for media and healthcare.

Create a personalized recommendation system with Amazon Personalize by preparing data, building datasets, training a model, deploying a campaign, generating recommendations, and evaluating performance.

Explore AI and ML use cases on AWS across healthcare, finance, retail, and manufacturing. Review production examples and case studies, and discuss best practices for deployment.

Explore how ai and ml empower healthcare, finance, retail, and manufacturing using AWS services like SageMaker, Comprehend Medical, Forecast, and Rekognition for diagnostics, fraud detection, and predictive maintenance.

Explore real-world production uses of AWS AI services across industries, from Netflix personalizing with Amazon Personalize to Intuit Textract, Moderna with Amazon SageMaker, Zalando Rekognition, and Capital One fraud detection.

Explore how AWS case studies reveal successful AI and ML projects, from F1 real-time insights with SageMaker, Lambda, and S3 to underwriting automation with Textract.

Explore best practices for deploying AI and ML in production on AWS, including data privacy and security (GDPR, HIPAA), SageMaker model monitor, drift management, scalable infrastructure, and interpretability.

Explore ethics in AI and responsible use on AWS, focusing on fairness, bias, and interpretability, with a hands-on lab to mitigate bias using AWS services.

Explore how AI ethics guides the responsible development and deployment of AI systems to promote fairness, transparency, accountability, and privacy, while mitigating bias and harm.

Explore AWS guidelines for responsible AI, prioritizing transparency and fairness with SageMaker Clarify for bias detection, privacy protection, audit trails, and continuous monitoring of AI systems.

Explore fairness, bias, and interpretability in AI models, addressing data, algorithmic, and representation biases with tools like SageMaker Clarify, Shap, and Lime for transparent AI.

Detect and mitigate bias in ai models using SageMaker Clarify, covering pre-training bias, post-training bias, and model explainability. Prepare data, train a model, mitigate bias, and monitor fairness in production.