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Amazon SageMaker is an end-to-end machine studying (ML) platform with wide-ranging options to ingest, remodel, and measure bias in information, and prepare, deploy, and handle fashions in manufacturing with best-in-class compute and companies resembling Amazon SageMaker Information Wrangler, Amazon SageMaker Studio, Amazon SageMaker Canvas, Amazon SageMaker Mannequin Registry, Amazon SageMaker Function Retailer, Amazon SageMaker Pipelines, Amazon SageMaker Mannequin Monitor, and Amazon SageMaker Make clear. Many organizations select SageMaker as their ML platform as a result of it offers a typical set of instruments for builders and information scientists. Numerous AWS unbiased software program vendor (ISV) companions have already constructed integrations for customers of their software program as a service (SaaS) platforms to make the most of SageMaker and its varied options, together with coaching, deployment, and the mannequin registry.
On this submit, we cowl the advantages for SaaS platforms to combine with SageMaker, the vary of attainable integrations, and the method for creating these integrations. We additionally deep dive into the most typical architectures and AWS sources to facilitate these integrations. That is supposed to speed up time-to-market for ISV companions and different SaaS suppliers constructing related integrations and encourage clients who’re customers of SaaS platforms to companion with SaaS suppliers on these integrations.
Advantages of integrating with SageMaker
There are a number of advantages for SaaS suppliers to combine their SaaS platforms with SageMaker:
- Customers of the SaaS platform can make the most of a complete ML platform in SageMaker
- Customers can construct ML fashions with information that’s in or outdoors of the SaaS platform and exploit these ML fashions
- It offers customers with a seamless expertise between the SaaS platform and SageMaker
- Customers can make the most of basis fashions out there in Amazon SageMaker JumpStart to construct generative AI purposes
- Organizations can standardize on SageMaker
- SaaS suppliers can deal with their core performance and provide SageMaker for ML mannequin growth
- It equips SaaS suppliers with a foundation to construct joint options and go to market with AWS
SageMaker overview and integration choices
SageMaker has instruments for each step of the ML lifecycle. SaaS platforms can combine with SageMaker throughout the ML lifecycle from information labeling and preparation to mannequin coaching, internet hosting, monitoring, and managing fashions with varied parts, as proven within the following determine. Relying on the wants, any and all components of the ML lifecycle might be run in both the client AWS account or SaaS AWS account, and information and fashions might be shared throughout accounts utilizing AWS Id and Entry Administration (IAM) insurance policies or third-party user-based entry instruments. This flexibility within the integration makes SageMaker a really perfect platform for patrons and SaaS suppliers to standardize on.
Integration course of and architectures
On this part, we break the combination course of into 4 foremost levels and canopy the widespread architectures. Word that there might be different integration factors along with these, however these are much less widespread.
- Information entry – How information that’s within the SaaS platform is accessed from SageMaker
- Mannequin coaching – How the mannequin is skilled
- Mannequin deployment and artifacts – The place the mannequin is deployed and what artifacts are produced
- Mannequin inference – How the inference occurs within the SaaS platform
The diagrams within the following sections assume SageMaker is operating within the buyer AWS account. Many of the choices defined are additionally relevant if SageMaker is operating within the SaaS AWS account. In some circumstances, an ISV might deploy their software program within the buyer AWS account. That is often in a devoted buyer AWS account, which means there nonetheless must be cross-account entry to the client AWS account the place SageMaker is operating.
There are just a few alternative ways during which authentication throughout AWS accounts might be achieved when information within the SaaS platform is accessed from SageMaker and when the ML mannequin is invoked from the SaaS platform. The really helpful methodology is to make use of IAM roles. An alternate is to make use of AWS entry keys consisting of an entry key ID and secret entry key.
Information entry
There are a number of choices on how information that’s within the SaaS platform might be accessed from SageMaker. Information can both be accessed from a SageMaker pocket book, SageMaker Information Wrangler, the place customers can put together information for ML, or SageMaker Canvas. The most typical information entry choices are:
- SageMaker Information Wrangler built-in connector – The SageMaker Information Wrangler connector allows information to be imported from a SaaS platform to be ready for ML mannequin coaching. The connector is developed collectively by AWS and the SaaS supplier. Present SaaS platform connectors embrace Databricks and Snowflake.
- Amazon Athena Federated Question for the SaaS platform – Federated queries allow customers to question the platform from a SageMaker pocket book by way of Amazon Athena utilizing a customized connector that’s developed by the SaaS supplier.
- Amazon AppFlow – With Amazon AppFlow, you need to use a customized connector to extract information into Amazon Easy Storage Service (Amazon S3) which subsequently might be accessed from SageMaker. The connector for a SaaS platform might be developed by AWS or the SaaS supplier. The open-source Customized Connector SDK allows the event of a personal, shared, or public connector utilizing Python or Java.
- SaaS platform SDK – If the SaaS platform has an SDK (Software program Growth Package), resembling a Python SDK, this can be utilized to entry information immediately from a SageMaker pocket book.
- Different choices – Along with these, there might be different choices relying on whether or not the SaaS supplier exposes their information by way of APIs, recordsdata or an agent. The agent might be put in on Amazon Elastic Compute Cloud (Amazon EC2) or AWS Lambda. Alternatively, a service resembling AWS Glue or a third-party extract, remodel, and cargo (ETL) device can be utilized for information switch.
The next diagram illustrates the structure for information entry choices.
Mannequin coaching
The mannequin might be skilled in SageMaker Studio by an information scientist, utilizing Amazon SageMaker Autopilot by a non-data scientist, or in SageMaker Canvas by a enterprise analyst. SageMaker Autopilot takes away the heavy lifting of constructing ML fashions, together with characteristic engineering, algorithm choice, and hyperparameter settings, and it’s also comparatively easy to combine immediately right into a SaaS platform. SageMaker Canvas offers a no-code visible interface for coaching ML fashions.
As well as, Information scientists can use pre-trained fashions out there in SageMaker JumpStart, together with basis fashions from sources resembling Alexa, AI21 Labs, Hugging Face, and Stability AI, and tune them for their very own generative AI use circumstances.
Alternatively, the mannequin might be skilled in a third-party or partner-provided device, service, and infrastructure, together with on-premises sources, offered the mannequin artifacts are accessible and readable.
The next diagram illustrates these choices.
Mannequin deployment and artifacts
After you’ve skilled and examined the mannequin, you’ll be able to both deploy it to a SageMaker mannequin endpoint within the buyer account, or export it from SageMaker and import it into the SaaS platform storage. The mannequin might be saved and imported in customary codecs supported by the widespread ML frameworks, resembling pickle, joblib, and ONNX (Open Neural Community Alternate).
If the ML mannequin is deployed to a SageMaker mannequin endpoint, extra mannequin metadata might be saved within the SageMaker Mannequin Registry, SageMaker Mannequin Playing cards, or in a file in an S3 bucket. This may be the mannequin model, mannequin inputs and outputs, mannequin metrics, mannequin creation date, inference specification, information lineage info, and extra. The place there isn’t a property out there within the mannequin package deal, the information might be saved as customized metadata or in an S3 file.
Creating such metadata may help SaaS suppliers handle the end-to-end lifecycle of the ML mannequin extra successfully. This info might be synced to the mannequin log within the SaaS platform and used to trace modifications and updates to the ML mannequin. Subsequently, this log can be utilized to find out whether or not to refresh downstream information and purposes that use that ML mannequin within the SaaS platform.
The next diagram illustrates this structure.
Mannequin inference
SageMaker presents 4 choices for ML mannequin inference: real-time inference, serverless inference, asynchronous inference, and batch remodel. For the primary three, the mannequin is deployed to a SageMaker mannequin endpoint and the SaaS platform invokes the mannequin utilizing the AWS SDKs. The really helpful possibility is to make use of the Python SDK. The inference sample for every of those is analogous in that the predictor’s predict() or predict_async() strategies are used. Cross-account entry might be achieved utilizing role-based entry.
It’s additionally attainable to seal the backend with Amazon API Gateway, which calls the endpoint by way of a Lambda perform that runs in a protected non-public community.
For batch remodel, information from the SaaS platform first must be exported in batch into an S3 bucket within the buyer AWS account, then the inference is completed on this information in batch. The inference is completed by first making a transformer job or object, after which calling the remodel() methodology with the S3 location of the information. Outcomes are imported again into the SaaS platform in batch as a dataset, and joined to different datasets within the platform as a part of a batch pipeline job.
Another choice for inference is to do it immediately within the SaaS account compute cluster. This could be the case when the mannequin has been imported into the SaaS platform. On this case, SaaS suppliers can select from a vary of EC2 cases which might be optimized for ML inference.
The next diagram illustrates these choices.
Instance integrations
A number of ISVs have constructed integrations between their SaaS platforms and SageMaker. To be taught extra about some instance integrations, seek advice from the next:
Conclusion
On this submit, we defined why and the way SaaS suppliers ought to combine SageMaker with their SaaS platforms by breaking the method into 4 components and masking the widespread integration architectures. SaaS suppliers trying to construct an integration with SageMaker can make the most of these architectures. If there are any customized necessities past what has been lined on this submit, together with with different SageMaker parts, get in contact along with your AWS account groups. As soon as the combination has been constructed and validated, ISV companions can be a part of the AWS Service Prepared Program for SageMaker and unlock quite a lot of advantages.
We additionally ask clients who’re customers of SaaS platforms to register their curiosity in an integration with Amazon SageMaker with their AWS account groups, as this may help encourage and progress the event for SaaS suppliers.
In regards to the Authors
Mehmet Bakkaloglu is a Principal Options Architect at AWS, specializing in Information Analytics, AI/ML and ISV companions.
Raj Kadiyala is a Principal AI/ML Evangelist at AWS.
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