Combine SaaS platforms with Amazon SageMaker to allow ML-powered functions

Amazon SageMaker is an end-to-end machine studying (ML) platform with wide-ranging features to ingest, remodel, and measure bias in information, and practice, deploy, and handle fashions in manufacturing with best-in-class compute and providers comparable to Amazon SageMaker Data Wrangler, Amazon SageMaker Studio, Amazon SageMaker Canvas, Amazon SageMaker Model Registry, Amazon SageMaker Feature Store, Amazon SageMaker Pipelines, Amazon SageMaker Model Monitor, and Amazon SageMaker Clarify. Many organizations select SageMaker as their ML platform as a result of it supplies a typical set of instruments for builders and information scientists. A variety of AWS independent software vendor (ISV) partners have already constructed integrations for customers of their software program as a service (SaaS) platforms to make the most of SageMaker and its numerous 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 potential 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 comparable 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 an a variety of benefits for SaaS suppliers to combine their SaaS platforms with SageMaker:

Customers of the SaaS platform can reap the benefits of a complete ML platform in SageMaker
Customers can construct ML fashions with information that’s in or exterior of the SaaS platform and exploit these ML fashions
It supplies customers with a seamless expertise between the SaaS platform and SageMaker
Customers can make the most of basis fashions obtainable in Amazon SageMaker JumpStart to construct generative AI functions
Organizations can standardize on SageMaker
SaaS suppliers can give attention to 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 numerous parts, as proven within the following determine. Relying on the wants, any and all elements of the ML lifecycle will be run in both the shopper AWS account or SaaS AWS account, and information and fashions will be shared throughout accounts utilizing AWS Identity and Access Management (IAM) insurance policies or third-party user-based entry instruments. This flexibility within the integration makes SageMaker an excellent 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 principal levels and canopy the frequent architectures. Observe that there will be different integration factors along with these, however these are much less frequent.

Knowledge 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 working within the buyer AWS account. Many of the choices defined are additionally relevant if SageMaker is working 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 shopper AWS account the place SageMaker is working.

There are a couple of alternative ways through which authentication throughout AWS accounts will 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 useful technique is to make use of IAM roles. Another is to make use of AWS access keys consisting of an entry key ID and secret entry key.

Knowledge entry

There are a number of choices on how information that’s within the SaaS platform will be accessed from SageMaker. Knowledge can both be accessed from a SageMaker pocket book, SageMaker Knowledge Wrangler, the place customers can put together information for ML, or SageMaker Canvas. The commonest information entry choices are:

SageMaker Knowledge Wrangler built-in connector – The SageMaker Data Wrangler connector permits 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 through Amazon Athena utilizing a customized connector that’s developed by the SaaS supplier.
Amazon AppFlow – With Amazon AppFlow, you should use a customized connector to extract information into Amazon Simple Storage Service (Amazon S3) which subsequently will be accessed from SageMaker. The connector for a SaaS platform will be developed by AWS or the SaaS supplier. The open-source Custom Connector SDK permits 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 Improvement Equipment), comparable to a Python SDK, this can be utilized to entry information straight from a SageMaker pocket book.
Different choices – Along with these, there will be different choices relying on whether or not the SaaS supplier exposes their information through APIs, information or an agent. The agent will be put in on Amazon Elastic Compute Cloud (Amazon EC2) or AWS Lambda. Alternatively, a service comparable to AWS Glue or a third-party extract, remodel, and cargo (ETL) instrument can be utilized for information switch.

The next diagram illustrates the structure for information entry choices.

Mannequin coaching

The mannequin will be skilled in SageMaker Studio by a knowledge 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 straight right into a SaaS platform. SageMaker Canvas supplies a no-code visible interface for coaching ML fashions.

As well as, Knowledge scientists can use pre-trained fashions obtainable in SageMaker JumpStart, together with basis fashions from sources comparable to Alexa, AI21 Labs, Hugging Face, and Stability AI, and tune them for their very own generative AI use circumstances.

Alternatively, the mannequin will be skilled in a third-party or partner-provided instrument, service, and infrastructure, together with on-premises sources, supplied the mannequin artifacts are accessible and readable.

The next diagram illustrates these choices.

Mannequin deployment and artifacts

After you’ve got 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 will be saved and imported in normal codecs supported by the frequent ML frameworks, comparable to pickle, joblib, and ONNX (Open Neural Network Exchange).

If the ML mannequin is deployed to a SageMaker mannequin endpoint, further mannequin metadata will be saved within the SageMaker Model Registry, SageMaker Model 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 data, and extra. The place there isn’t a property obtainable within the model package, the information will be saved as custom metadata or in an S3 file.

Creating such metadata will help SaaS suppliers handle the end-to-end lifecycle of the ML mannequin extra successfully. This data will be synced to the mannequin log within the SaaS platform and used to trace adjustments and updates to the ML mannequin. Subsequently, this log can be utilized to find out whether or not to refresh downstream information and functions that use that ML mannequin within the SaaS platform.

The next diagram illustrates this structure.

Mannequin inference

SageMaker provides 4 choices for ML model 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 useful choice is to make use of the Python SDK. The inference sample for every of those is comparable in that the predictor’s predict() or predict_async() strategies are used. Cross-account entry will be achieved utilizing role-based access.

It’s additionally potential to seal the backend with Amazon API Gateway, which calls the endpoint through a Lambda operate that runs in a protected non-public community.

For batch transform, information from the SaaS platform first must be exported in batch into an S3 bucket within the buyer AWS account, then the inference is finished on this information in batch. The inference is finished by first making a transformer job or object, after which calling the remodel() technique 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.

An alternative choice for inference is to do it straight within the SaaS account compute cluster. This is able to be the case when the mannequin has been imported into the SaaS platform. On this case, SaaS suppliers can select from a range of EC2 instances which can 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 study extra about some instance integrations, confer with 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 elements and overlaying the frequent 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 coated 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 part of the AWS Service Ready Program for SageMaker and unlock a wide range 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 will help encourage and progress the event for SaaS suppliers.