Knowledge groups right this moment battle with fragmented instruments, advanced infrastructure provisioning, and hours spent writing boilerplate code to connect with information sources. This forces analysts, information scientists, and engineers to work in separate environments, which slows collaboration and time to perception. Since our launch of Amazon SageMaker Unified Studio in March 2025, main corporations resembling Bayer, NatWest, and Service have adopted it to carry their information groups into one collaborative workspace with unified instruments, simple infrastructure provisioning, and quick connections to information sources.
Persevering with our mission to supply sooner time-to-value for patrons, in November 2025, we introduced Amazon SageMaker notebooks, a serverless workspace with a built-in AI agent in Amazon SageMaker Unified Studio. Now you can launch a pocket book in seconds, generate code from pure language prompts, and join robotically to information throughout Amazon Easy Storage Service (Amazon S3), Amazon Redshift, third-party databases, and extra from a single setting without having to pre-provision or tune information processing infrastructure. Inside these serverless notebooks, analysts can carry out SQL queries, information scientists can execute Python code, and information engineers can course of large-scale information jobs in Spark inside a single workspace. Along with the brand new one-click onboarding accessible for SageMaker Unified Studio, prospects can go from their present AWS information to operating analytics and machine studying workloads a lot sooner, spending their time on evaluation slightly than setup and configuration.
On this submit, we stroll you thru how these new capabilities in SageMaker Unified Studio may help you consolidate your fragmented information instruments, scale back time to perception, and collaborate throughout your information groups. Right here’s a brief demo of the brand new capabilities:
One-click onboarding of present AWS datasets
Get began exploring your information with one-click onboarding that provisions and configures environments in minutes as an alternative of weeks. The brand new onboarding expertise can reuse present AWS Id and Entry Administration (IAM) roles to supply entry to SageMaker Unified Studio, robotically connecting to information sources throughout S3 buckets, S3 Tables, AWS Glue Knowledge Catalog, and AWS Lake Formation insurance policies, eradicating the necessity for extra information permission setup. Beneath the covers, a brand new IAM-based area and venture are created with default pocket book and compute sources preconfigured. When full, you enter SageMaker Unified Studio with all of your instruments accessible within the left-side navigation together with built-in samples to speed up first use, as seen within the following screenshot.
“New options with Amazon Sagemaker will unlock a brand new paradigm of innovation, permitting Codex to considerably speed up time-to-value for our prospects, and remodel them from getting old to agentic in weeks, not months.“
– Abhinav Sharma, Chief Knowledge Officer, Codex
You can begin straight from Amazon SageMaker, Amazon Athena, Amazon Redshift, or Amazon S3 Tables, giving them a quick path from their present instruments and information to the unified expertise in SageMaker Unified Studio. After you select Get Began and specify an IAM position, SageMaker robotically creates a venture with the present information permissions intact from Knowledge Catalog, Lake Formation, and Amazon S3. In consequence, groups can instantly uncover and act on their information utilizing the present information permissions and infrastructure.
For extra info, see New one-click onboarding and notebooks with a built-in AI agent in Amazon SageMaker Unified Studio
Serverless SageMaker notebooks
The totally managed, web-based notebooks in SageMaker Unified Studio help a number of programming languages, letting you write Python, SQL, and Spark code in the identical pocket book. The infrastructure adjusts robotically based mostly in your workload, whereas built-in libraries create charts and insights straight in your workflow. When your evaluation scales past interactive queries to large-scale information processing, Amazon Athena for Apache Spark engine delivers optimized efficiency, integrating with the serverless pocket book expertise to execute analytical workloads effectively. This serverless strategy eliminates the necessity to provision clusters or preserve servers, decreasing the time from query to perception.
“The brand new SageMaker interface brings readability and pace to the complete ML lifecycle. Its developer-friendly design has made our experimentation and supply considerably sooner,“
– Sachin Mittal, Product Supervisor at Deloitte.
As proven within the previous picture, the pocket book offers information engineers, analysts, and information scientists one place to carry out SQL queries, execute Python code, course of large-scale information jobs, run machine studying workloads, and create visualizations with out having to change between instruments.
AI-assisted improvement with Knowledge Agent
To speed up improvement additional, the brand new SageMaker Knowledge Agent helps create SQL, Python, or Spark code utilizing pure language prompts. As a substitute of spending hours writing boilerplate code to connect with your information sources and perceive schemas, you possibly can describe what you wish to accomplish. The agent analyzes information catalog metadata about your accessible datasets, schemas, and relationships to supply context-aware help.
Within the previous instance picture, if you happen to immediate Construct and analyze a whole gross sales forecast based mostly on the pattern retail information, the agent helps determine the related tables and suggests the suitable joins and evaluation strategy, remodeling what would possibly take hours into minutes. To do that your self, navigate to the Overview tab in your SageMaker Studio setting and search for the Retail Gross sales Forecasting with SageMaker XGBoost pocket book within the pattern notebooks assortment—these examples are robotically accessible while you first arrange SageMaker Studio. The agent breaks down advanced analytical workflows into manageable, executable steps, so you possibly can transfer from query to perception sooner.
Study extra about SageMaker
On this submit, we targeted on three new SageMaker Unified Studio capabilities not too long ago made accessible, however they’re a fraction of the greater than 40 launches final 12 months. Right here’s a listing of movies of re:Invent periods and the measurable outcomes from main organizations adopting SageMaker Unified Studio, together with:
- Abstract of 2025 launches: What’s new with Amazon SageMaker within the period of unified information and AI (ANT216)
- NatWest Group plans to scale to 72,000 staff having federated information entry utilizing SageMaker Unified Studio. Watch their presentation.
- Commonwealth Financial institution of Australia migrated 10 petabytes and 61,000 pipelines into AWS and has setup SageMaker Unified Studio to supply unified entry to 40 completely different strains of enterprise of their ongoing information transformation journey. Watch their presentation.
- Service International Company improved pure language to SQL agent accuracy by 38% by means of the SageMaker Catalog’s ruled metadata and enterprise glossary. Watch their presentation.
- Bayer is now positioned to onboard over 300 TB of biomarker information and combine siloed omics, medical, and chemistry information repositories right into a cohesive setting constructed on Amazon SageMaker. Learn their story.
Conclusion
Utilizing Amazon SageMaker Unified Studio serverless notebooks, AI-assisted improvement, and unified governance, you possibly can pace up your information and AI workflows throughout information crew features whereas sustaining safety and compliance. To be taught extra go to the SageMaker product web page or get began within the SageMaker console.
Concerning the authors
