Information groups at this time battle with fragmented instruments, complicated infrastructure provisioning, and hours spent writing boilerplate code to hook up 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 corresponding to Bayer, NatWest, and Service have adopted it to deliver their information groups into one collaborative workspace with unified instruments, simple infrastructure provisioning, and quick connections to information sources.
Persevering with our mission to offer 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 routinely to information throughout Amazon Easy Storage Service (Amazon S3), Amazon Redshift, third-party databases, and extra from a single setting with no need 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, clients can go from their current AWS information to operating analytics and machine studying workloads a lot sooner, spending their time on evaluation fairly than setup and configuration.
On this publish, we stroll you thru how these new capabilities in SageMaker Unified Studio may also 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 current AWS datasets
Get began exploring your information with one-click onboarding that provisions and configures environments in minutes as a substitute of weeks. The brand new onboarding expertise can reuse current AWS Id and Entry Administration (IAM) roles to offer entry to SageMaker Unified Studio, routinely connecting to information sources throughout S3 buckets, S3 Tables, AWS Glue Information Catalog, and AWS Lake Formation insurance policies, eradicating the necessity for added information permission setup. Below the covers, a brand new IAM-based area and challenge are created with default pocket book and compute assets 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 clients, and rework them from getting older to agentic in weeks, not months.“
– Abhinav Sharma, Chief Information Officer, Codex
You can begin straight from Amazon SageMaker, Amazon Athena, Amazon Redshift, or Amazon S3 Tables, giving them a quick path from their current instruments and information to the unified expertise in SageMaker Unified Studio. After you select Get Began and specify an IAM function, SageMaker routinely creates a challenge with the present information permissions intact from Information 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 data, see New one-click onboarding and notebooks with a built-in AI agent in Amazon SageMaker Unified Studio
Serverless SageMaker notebooks
The absolutely 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 routinely primarily based 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 velocity 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 provides 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 Information Agent
To speed up improvement additional, the brand new SageMaker Information Agent helps create SQL, Python, or Spark code utilizing pure language prompts. As a substitute of spending hours writing boilerplate code to hook up with your information sources and perceive schemas, you possibly can describe what you need to accomplish. The agent analyzes information catalog metadata about your accessible datasets, schemas, and relationships to offer context-aware help.
Within the previous instance picture, if you happen to immediate Construct and analyze a whole gross sales forecast primarily based on the pattern retail information, the agent helps determine the related tables and suggests the suitable joins and evaluation strategy, remodeling what may take hours into minutes. To do this 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 routinely accessible if you first arrange SageMaker Studio. The agent breaks down complicated analytical workflows into manageable, executable steps, so you possibly can transfer from query to perception sooner.
Study extra about SageMaker
On this publish, we centered 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 an inventory 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 workers 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 offer unified entry to 40 completely different traces of enterprise of their ongoing information transformation journey. Watch their presentation.
- Service World Company improved pure language to SQL agent accuracy by 38% via 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, scientific, 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 velocity up your information and AI workflows throughout information workforce features whereas sustaining safety and compliance. To study extra go to the SageMaker product web page or get began within the SageMaker console.
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