Amazon SageMaker tutorial
A single platform for data, analytics, and artificial intelligence is the next version of SageMaker. The next iteration of SageMaker offers a unified access to all of your data and an integrated analytics and artificial intelligence (AI) experience by combining the extensively used AWS machine learning (ML) and analytics capabilities.
With the help of Amazon Q Developer, the most powerful generative AI assistant for software development, SageMaker enables you to work together and build more quickly from a single studio (preview) utilising well-known AWS resources for model creation, generative AI, data processing, and SQL analytics. Furthermore, you have access to all of your data, regardless of whether it is kept in data lakes, data warehouses, or federated or third-party data sources, and governance is integrated to meet organisational security requirements.
Amazon SageMaker offers a single access to all of your data and an integrated analytics and artificial intelligence (AI) experience by combining the extensively used AWS machine learning (ML) and analytics capabilities. With Amazon Q Developer, the most powerful generative AI assistant for software development, you can work together and produce more quickly from a single studio (preview) utilising well-known AWS resources for model creation, generative AI, data processing, and SQL analytics. Access all of your data, whether it is kept in federated or third-party data sources, data lakes, or data warehouses, with governance integrated to satisfy enterprise security requirements.
Advantages
Use a single data and AI development platform to collaborate and create more quickly
The preview version of Amazon SageMaker Unified Studio offers an integrated experience for using all of your data and analytics and artificial intelligence capabilities. Using well-known AWS technologies for model creation, generative AI, data processing, and SQL analytics, find your data and put it to use. Use unified notebooks to collaborate across computational resources, find and query a variety of data sources using an integrated SQL editor, train and implement AI models at scale, and quickly create unique generative AI applications. To accelerate the release of data products, create and securely distribute analytics and AI artefacts, including data, models, and generative AI applications.
Create and expand AI use cases using a variety of technologies
With a full suite of AI development tools that are safe by design, SageMaker can speed up AI. ML and foundation models (FMs) can be trained, customised, and deployed on a very efficient and economical infrastructure. Make use of specially designed tools covering every stage of the AI lifecycle, from distributed training and high-performance IDEs to inference, AI operations, governance, and observability. Utilise your proprietary data and state-of-the-art models to quickly develop generative AI solutions customised for your company. Amazon Q Developer speeds up AI development by making it easier to find data, create and train machine learning models, perform SQL queries, and create and execute data pipeline operations using natural language.
To consolidate all of your data, employ an open lakehouse to reduce data silos
Use Amazon SageMaker Lakehouse to consolidate all of your data across Amazon Redshift data warehouses and Amazon Simple Storage Service (Amazon S3) data lakes. Get the freedom to use any tools or engines that are compatible with Apache Iceberg to access and query your data on a single copy of analytics data. Create fine-grained permissions for all of your analytics and artificial intelligence technologies in the Lakehouse to protect your data. With zero-ETL connectors, you can get data into your lakehouse almost instantly from running databases and apps. Additionally, use federated query capabilities across third-party data sources to access and query existing data.
Use end-to-end data and AI governance to meet your company security requirements
With integrated governance across the whole data and AI lifecycle, you can guarantee enterprise security. With SageMaker, you can manage who has access to what data, models, and development artefacts for what purposes. Using a single permission model and fine-grained access restrictions with Amazon SageMaker Catalogue, build and implement access policies consistently. Use data classification, toxicity detection, guardrails, and responsible AI regulations to safeguard and defend your AI models. Detect sensitive data, automate data-quality monitoring, and trace data and machine learning to build trust across your company.
