Amazon SageMaker Unified Studio now supports two new connection capabilities: (1) Iceberg REST Catalog (IRC) connections for external Apache Iceberg catalogs, and (2) IAM authentication for Amazon DocumentDB. Together, they let data teams connect to a broader set of governed data sources and use credential-less authentication across projects. Iceberg REST Catalog connections. SageMaker Unified Studio now supports Iceberg REST Catalog (IRC) connections, letting customers connect to external Apache Iceberg catalogs that follow the Iceberg REST specification. Supported catalog types include Snowflake Open Catalog (Polaris), Databricks Unity Catalog, and a Generic IRC type for any other spec-compliant catalog. Once connected, customers can browse the catalog in Data Explorer (list catalog/schema/table, view columns, sample data), read and write it in Visual ETL as a source or append/overwrite sink, and query it from data notebooks. Authentication uses OAuth2 or bearer token, and data access uses vended, short-lived Amazon S3 credentials. The connection lifecycle is fully supported - create, edit, delete, and test connection. IAM authentication for Amazon DocumentDB. SageMaker Unified Studio now supports IAM authentication for Amazon DocumentDB connections, so customers can connect to DocumentDB without storing a database username or password in the connection or notebook. The connection authenticates using its own IAM role, which DocumentDB validates via AWS Security Token Service (AWS STS). This requires Amazon DocumentDB 5.0 or later instance-based clusters with TLS enabled. The connection can be used from data notebooks, Data Explorer, Test Connection, and Visual ETL data preview. These connection capabilities are available today in all AWS Regions where Amazon SageMaker Unified Studio is available, at no additional cost. To learn more about Amazon SageMaker Unified Studio, refer to the Amazon SageMaker Unified Studio User Guide.

Amazon SageMaker Unified Studio now supports two new connection capabilities: (1) Iceberg REST Catalog (IRC) connections for external Apache Iceberg catalogs, and (2) IAM authentication for Amazon DocumentDB. Together, they let data teams connect to a broader set of governed data sources and use credential-less authentication across projects. Iceberg REST Catalog connections. SageMaker Unified Studio now supports Iceberg REST Catalog (IRC) connections, letting customers connect to external Apache Iceberg catalogs that follow the Iceberg REST specification. Supported catalog types include Snowflake Open Catalog (Polaris), Databricks Unity Catalog, and a Generic IRC type for any other spec-compliant catalog. Once connected, customers can browse the catalog in Data Explorer (list catalog/schema/table, view columns, sample data), read and write it in Visual ETL as a source or append/overwrite sink, and query it from data notebooks. Authentication uses OAuth2 or bearer token, and data access uses vended, short-lived Amazon S3 credentials. The connection lifecycle is fully supported - create, edit, delete, and test connection. IAM authentication for Amazon DocumentDB. SageMaker Unified Studio now supports IAM authentication for Amazon DocumentDB connections, so customers can connect to DocumentDB without storing a database username or password in the connection or notebook. The connection authenticates using its own IAM role, which DocumentDB validates via AWS Security Token Service (AWS STS). This requires Amazon DocumentDB 5.0 or later instance-based clusters with TLS enabled. The connection can be used from data notebooks, Data Explorer, Test Connection, and Visual ETL data preview. These connection capabilities are available today in all AWS Regions where Amazon SageMaker Unified Studio is available, at no additional cost. To learn more about Amazon SageMaker Unified Studio, refer to the Amazon SageMaker Unified Studio User Guide.

Amazon SageMaker Unified Studio now supports two new connection capabilities: (1) Iceberg REST Catalog (IRC) connections for external Apache Iceberg catalogs, and (2) IAM authentication for Amazon DocumentDB. Together, they let data teams connect to a broader set of governed data sources and use credential-less authentication across projects. Iceberg REST Catalog connections. SageMaker Unified Studio now supports Iceberg REST Catalog (IRC) connections, letting customers connect to external Apache Iceberg catalogs that follow the Iceberg REST specification. Supported catalog types include Snowflake Open Catalog (Polaris), Databricks Unity Catalog, and a Generic IRC type for any other spec-compliant catalog. Once connected, customers can browse the catalog in Data Explorer (list catalog/schema/table, view columns, sample data), read and write it in Visual ETL as a source or append/overwrite sink, and query it from data notebooks. Authentication uses OAuth2 or bearer token, and data access uses vended, short-lived Amazon S3 credentials. The connection lifecycle is fully supported - create, edit, delete, and test connection. IAM authentication for Amazon DocumentDB. SageMaker Unified Studio now supports IAM authentication for Amazon DocumentDB connections, so customers can connect to DocumentDB without storing a database username or password in the connection or notebook. The connection authenticates using its own IAM role, which DocumentDB validates via AWS Security Token Service (AWS STS). This requires Amazon DocumentDB 5.0 or later instance-based clusters with TLS enabled. The connection can be used from data notebooks, Data Explorer, Test Connection, and Visual ETL data preview. These connection capabilities are available today in all https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/ where Amazon SageMaker Unified Studio is available, at no additional cost. To learn more about Amazon SageMaker Unified Studio, refer to the https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/what-is-sagemaker-unified-studio.html.

Amazon SageMaker Unified Studio now supports Iceberg REST Catalog connections and IAM authentication for Amazon DocumentB, enabling credential-less access to governed data sources.

Aurora PostgreSQL zero-ETL integration with Amazon SageMaker enables near real-time replication of operational data to a lakehouse without building custom ETL pipelines.

Announcing Spark Connect on Amazon EMR on EKS: build, test, and debug Spark applications from VS Code, PyCharm, Jupyter notebooks, Amazon SageMaker Unified Studio, or dbt, while running full-scale Spark operations on your existing Amazon EKS clusters.

Amazon Aurora PostgreSQL zero-ETL integration with Amazon SageMaker replicates your operational data to a lakehouse in near real time, without building custom ETL pipelines. Learn the architecture and change data capture mechanics, then set up the integration and query your data in Amazon SageMaker.

Managed MLflow on Amazon SageMaker AI now syncs richer model metadata (training metrics, evaluation results, inference specs, and lineage) into the SageMaker AI Model Registry, with lifecycle stage promotion. Part 1 shows how to govern candidate models in a single account using IAM guardrails.

AI and Machine Learning applications need more than just a model. Developers also need cloud services for storing data, running applications, accessing AI models, and monitoring performance. AWS provides many services for these tasks. Here are five useful AWS services that every beginner AI/ML developer should know.

vLLM-OmniとSageMaker AIで画像・動画を生成する技術詳細。

HyperPod InstantStart is an open source control plane that composes Amazon EKS orchestration with the managed capabilities of Amazon SageMaker HyperPod. It drives the same guarded operations through both a web interface and an AI agent, turning cluster bootstrap, capacity, training, inference, and storage into dependable, agent-driven infrastructure.

Hello folks, as Generative AI continues to revolutionize various industries, mastering tools like Amazon Bedrock and SageMaker has become crucial for AI and machine learning professionals. If you want to learn Amazon Bedrock and AWS SageMaker and looking for resources then you have come to the right place. Earlier, I have shared best AI courses, best ChatGPT courses, best Data Science courses and best Machine Learning courses and in this article, I am going to share best Udemy courses to learn AWS SageMaker and Bedrok in 2026. These are very affordable and up-to-date Udemy courses which you can use to become proficient in these powerful services. By the way, if you are new to AI and Machine Learning then I also suggest you to go through Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2026] course by Kirill Eremenko on Udemy. It’s a great course to learn AI and Machine Learning. What is Amazon Bedrock? Amazon Bedrock is a fully managed service that provides easy access to foundation models (FMs) from leading AI companies. It allows developers to build and scale generative AI applications quickly and securely. Why use Amazon Bedrock? * Access to a variety of pre-trained models * Customization options for specific use cases * Seamless integration with other AWS services * Enhanced security and privacy features What is Amazon SageMaker? Amazon SageMaker is a comprehensive machine learning platform that enables developers and data scientists to build, train, and deploy machine learning models quickly. Why use Amazon SageMaker? * End-to-end ML workflow support * Integrated development environment with SageMaker Studio * Automated machine learning with SageMaker Autopilot * Scalable and cost-effective model training and deployment What is Generative AI on AWS? Generative AI on AWS encompasses various tools and services that allow you to create AI-generated content, including text, images, and more. Why use Generative AI on AWS? * Access to state-of-the-art AI models * Scalable infrastructure for AI workloads * Integration with existing AWS services and workflows * Continuous updates and improvements to AI capabilities Top 7 Udemy Courses to Learn Amazon Bedrock, SageMaker & AWS Generative AI in 2026 These courses offer a mix of theoretical knowledge and hands-on practice, catering to different skill levels and learning preferences. Whether you’re a beginner or an experienced professional, these courses will help you master Amazon Bedrock, SageMaker, and Generative AI on AWS. 1. Amazon Bedrock & AWS Generative AI— Complete HandsON Students: 10,451 (Bestseller) Key Features: * Comprehensive coverage from AI basics to advanced Generative AI concepts * No prior AI or coding experience required * Hands-on projects in various industries Curriculum Highlights: * Fundamentals of AI, Machine Learning, and Artificial Neural Networks * Deep dive into Foundation Models and how Generative AI works * Detailed walkthrough of Amazon Bedrock console, architecture, and pricing * Real-world use cases: * Movie poster design using Stable Diffusion * Text summarization for manufacturing using Cohere * Chatbot development with Llama 2, Langchain, and Streamlit * HR Q&A app with Retrieval Augmented Generation (RAG) Why Choose This Course: Ideal for beginners looking for a practical, industry-focused approach to learning Generative AI on AWS. The diverse use cases provide a broad understanding of Bedrock’s capabilities. Here is the link to join this course — — Amazon Bedrock & AWS Generative AI — — Complete Hands ON 2. Amazon Bedrock — — The Complete Guide to AWS Generative AI Students: 1,663 (Bestseller) Key Features: * Focus on deploying scalable and secure Generative AI applications * Covers both Python and TypeScript implementations * Emphasis on AWS ecosystem integration Curriculum Highlights: * Fundamentals of Generative AI and its industry applications * Overview of essential AWS services (EC2, S3, Lambda) * In-depth study of Amazon Bedrock Managed Service * Step-by-step guide to creating infrastructure for Generative AI workloads * Development of Generative AI apps using Python and TypeScript Why Choose This Course: Best for developers who want to build production-ready Generative AI applications on AWS. The dual-language approach (Python and TypeScript) makes it versatile for different development preferences. Here is the link to join this course — — Amazon Bedrock — — The Complete Guide to AWS Generative AI 3. Amazon Bedrock — Learn AI on AWS with Python! Students: 2,821 Key Features: * Python-focused implementation of Generative AI on AWS * Deep dive into text processing with Amazon Titan * Coverage of advanced AI techniques Curriculum Highlights: * Comprehensive understanding of Amazon Bedrock’s architecture and capabilities * Hands-on experience with Amazon Bedrock’s tools and Amazon Titan * Advanced techniques like Retrieval Augmented Generation (RAG) * Practical applications: Processing complex information from PDFs and call transcripts * Exploration of AI-powered text and image processing, including Stability AI parameters Why Choose This Course: Perfect for Python developers looking to specialize in text and image processing using Amazon Bedrock. The focus on RAG and complex data extraction makes it valuable for building sophisticated AI applications. Here is the link to join this course — — Amazon Bedrock — Learn AI on AWS with Python! 4. AWS SageMaker Machine Learning Engineer in 30 Days + ChatGPT Students: 8,709 (Highest Rated) Key Features: * Intensive 30-day program with 30+ ML projects * Comprehensive coverage of SageMaker tools and features * Integration of ChatGPT into the learning process Curriculum Highlights: * Hands-on experience with SageMaker JumpStart, Canvas, AutoPilot, and DataWrangler * Integration of AWS Lambda and S3 in ML workflows * Practical implementation of various machine learning algorithms * Real-world projects spanning different industries and use cases * Exploration of ChatGPT integration in AWS environments Why Choose This Course: Ideal for aspiring ML engineers who want an intensive, project-based learning experience. The inclusion of ChatGPT makes it relevant for those interested in the latest AI trends. Here is the link to join this course — — AWS SageMaker Machine Learning Engineer in 30 Days + ChatGPT 5. AWS SageMaker Practical for Beginners | Build 6 Projects Students: 14,895 Key Features: * Focus on practical implementation of ML algorithms in SageMaker * Six hands-on projects covering different ML domains * Emphasis on SageMaker Studio and AutoML Curriculum Highlights: * Training and deploying AI/ML models using AWS SageMaker * Hyperparameter optimization techniques * Projects include: * Linear regression for predictions * Multi-polynomial regression for store sales prediction * Deep learning-based image classification * Time series forecasting with DeepAR * Sentiment analysis model development and deployment * Interaction with deployed NLP models Why Choose This Course: Best for beginners who want a solid foundation in practical ML implementation using SageMaker. The diverse project portfolio provides hands-on experience across various ML domains. Here is the link to join this course — — AWS SageMaker Practical for Beginners | Build 6 Projects 6. Complete Generative AI Course With Langchain and Huggingface Students: 21,966 (Bestseller) Key Features: * Integration of Langchain and Huggingface with AWS services * Focus on building and deploying advanced generative AI applications * Coverage of architecture and design patterns for AI systems Curriculum Highlights: * Creating advanced generative AI applications using Langchain framework * Leveraging Huggingface’s state-of-the-art models * Understanding architecture and design patterns for robust AI systems * Hands-on experience in deploying models to cloud platforms and on-premise servers * Best practices for scaling and optimizing generative AI applications Why Choose This Course: Ideal for developers looking to integrate cutting-edge AI libraries with AWS services. The focus on Langchain and Huggingface provides valuable skills for building sophisticated AI systems. Here is the link to join this course — — Complete Generative AI Course With Langchain and Huggingface 7. AWS Certified Machine Learning Specialty 2026 — Hands On! Students: 103,175 (Bestseller) Key Features: * Comprehensive preparation for AWS Certified Machine Learning Specialty exam * Covers SageMaker, Generative AI, data engineering, and modeling * Hands-on approach to learning Curriculum Highlights: * In-depth coverage of AWS machine learning services * Data engineering and feature engineering techniques * Model training, tuning, and deployment on AWS * Generative AI concepts and implementation on AWS * Exam-specific tips and strategies Why Choose This Course: Perfect for professionals aiming to obtain the AWS Certified Machine Learning Specialty certification. While focused on exam preparation, it provides a comprehensive overview of ML on AWS, including the latest in generative AI. Here is the link to join this course — — AWS Certified Machine Learning Specialty 2026— Hands On! That’s all about the best Udemy courses to learn Bedrock, Generative AI and SageMaker in AWS in 2026. Each of these courses offers a unique perspective on Generative AI, Amazon Bedrock, and SageMaker. You should consider your current skill level, learning goals, and preferred learning style when choosing the course that’s right for you. Remember, the field of AI is rapidly evolving, so courses with recent updates are particularly valuable for staying current with the latest AWS features and best practices. The key to success in this field is continuous learning and practice. As you progress through these courses, try to apply your knowledge to real-world projects and stay updated with the latest developments in the rapidly evolving field of Generative AI. Other AI, Cloud and ML resources you may like to explore * Top 5 Courses to Prepare for AIF-C01 Exam * How to Prepare for AWS Solution Architect Exam * 6 Udemy Courses to learn AWS Bedrock * 5 Best Courses to learn AWS SageMaker * 7 Best Courses to learn AWS S3 and DynamoDB * 10 Best Udemy Courses to learn Artificial Intelligence * 8 Udemy courses to learn Prompt Engineering and ChatGPT * 5 Best Udemy Courses to learn Building AI Agents * Top 5 Udemy Courses to learn Large Language Model * Top 5 Udemy courses for AWS Cloud Practitioner Exam Thanks for reading this article so far. If you find these AWS Bedrock and SageMaker courses useful, then, please share them with your friends and colleagues. If you have any questions or feedback, then please drop a note. P. S. — — By the way, if you want to join multiple course on Udemy, its better to get a Udemy Personal Plan, which will give instant access of more than 28,000 top quality Udemy courses for just $30 a month. If you got a lot of time and want to save money, Udemy Personal Plan will be perfect for you. Review — Is the Udemy Personal Plan Worth It? Is Udemy’s Personal Plan better than buying individual courses? medium.com --- Java, Unix, Tibco RV and FIX Protocol Tutorial

Pathway's Baby Dragon Hatchling (BDH) is a brain-inspired, post-transformer architecture that reasons in latent space instead of emitting chain-of-thought tokens. See how Pathway develops and scales BDH on Amazon SageMaker HyperPod, and how BDH-CQ set a new cost-efficiency mark on the ARC-AGI-1 benchmark.

Pathway's Baby Dragon Hatchling (BDH) is a brain-inspired, post-transformer architecture that reasons in latent space instead of emitting chain-of-thought tokens. See how Pathway develops and scales BDH on Amazon SageMaker HyperPod, and how BDH-CQ set a new cost-efficiency mark on the ARC-AGI-1 benchmark.

Deploy a text-to-speech model on Amazon SageMaker AI with the AWS vLLM-Omni Deep Learning Container and stream generated speech over a persistent bidirectional connection. This Part 1 tutorial deploys Qwen3-TTS and streams speech through a Gradio application.

Deploy two generative media models from one AWS vLLM-Omni Deep Learning Container on Amazon SageMaker AI. Generate an image with FLUX.2-klein through real-time inference, then animate it into video with Wan2.1-VACE through asynchronous inference, and retrieve the MP4 from Amazon S3.