Introduction of the end-to-end Machine Learning operations with SAP AI Core

In our modern era, technology evolves rapidly, as do our customers’ needs. Several things have changed since the introduction of MLF, such as the evolution of SAP Cloud Platform to SAP Business Technology Platform and its architectural paradigm.

Furthermore, technically and economically, the underlying infrastructure components of MLF provided by our hyperscaler partners have greatly improved.

Most importantly, our customers’ and partners’ expectations of Artificial Intelligence (AI), particularly in intelligent business processes, have evolved.

SAP has made artificial intelligence (AI) a critical strategic pillar for enabling intelligent enterprise.

This necessitated a revision of the underlying framework that was powering Machine Learning (ML) applications.

This is how SAP AI Core and SAP AI Launchpad came to be. AI foundation is sometimes used as an umbrella term to describe the combination of SAP AI Core and SAP AI Launchpad.

The AI foundation serves as the primary vehicle through which customers, partners, and SAP’s internal teams manage and extend SAP’s offerings with AI capabilities.

The AI API, an SAP-governed interface that allows unified consumption of AI content, whether provided on SAP or partner technology, unifies the consumption of these AI capabilities.

The AI foundation unifies the management and operations of AI content (versioning, deployment, and monitoring). However, different toolkits can be used to create AI content.

The AI foundation’s goal is to enable:

  • AI capabilities can be seamlessly and effortlessly integrated into other applications.
  • Using high-volume data from applications to build robust machine learning models.
  • Machine learning training on accelerated hardware.
  • Serving ML inference with low latency and high throughput at a low cost.
  • Adherence to a legal, explicable, and sustainable process.
  • All stages of the AI lifecycle are managed using a comprehensive set of tools and services.
  • Concentrate on the productization and operationalization of machine learning scenarios.

Infusing AI Into Business Applications Via SAP Business Technology Platform

Let’s take a quick look at SAP’s AI Strategy before diving into the details of end-to-end ML Ops with SAP AI Core.

SAP is dedicated to creating intelligent, sustainable, and connected enterprises by incorporating AI technologies into applications and business scenarios, which it accomplishes through SAP BTP’s AI offerings.

SAP has made artificial intelligence (AI) a critical strategic pillar for enabling intelligent enterprise.

This necessitated a revision of the underlying framework that powered AI applications in the SAP Business Technology Platform.

As a result, two new components have been added to SAP BTP:

SAP AI Core is a service that allows you to create custom AI models and advanced AI use cases using open-source frameworks.

It is intended to support multitenancy and provide CI/CD capability.

SAP AI Launchpad: a user interface (UI) that serves as the single point of entry for all AI API-enabled runtimes available in SAP BTP, including SAP AI Core. It can be used as a client to access the capabilities of runtimes and thus manage the lifecycle of AI use cases.

How Can an End – to – End ML Workflow be Achieved in SAP AI Core?

Let’s take a closer look at how to implement an end-to-end ML workflow in SAP AI Core.

SAP AI Core and SAP AI Launchpad onboarding:

1. Model Training Operation

2. Model Inferencing Operations

SAP AI Core and SAP AI Launchpad integration

The onboarding process is the first step in getting started with SAP AI Core and SAP AI Launchpad. It includes all of the necessary configurations.

Everything begins with creating a SAP AI Core instance in SAP BTP and purchasing a subscription to SAP AI Launchpad.

This is a simple task to complete using the SAP BTP Cockpit.

SAP AI Core is intended to integrate with and use a wide range of open-source platforms and cloud infrastructures.

To work with SAP AI Core, a user must first create an account on those platforms and authorize SAP AI Core to access them.

All of these activities comprise SAP AI core’s one-time initial configurations.

A closer look at the cloud infrastructures that make SAP AI Core a modern and complete tool

SAP AI Core is a runtime for executing ML pipelines and deploying trained machine learning models as APIs to serve inference requests in a scalable and high-performance manner. This includes designing the pipelines, serving applications, and writing the code executed in containers.

But why are containers used?

Containers package application software with its dependencies to isolate it from the infrastructure on which it runs. Containerized applications are built and deployed by developers, who break down monolithic apps into microservices for resource optimization and easier maintenance in the Cloud.

SAP AI Core adheres to this time-tested paradigm, and this consideration leads us to the first platform integrated with SAP AI Core, Docker. Docker is the platform SAP AI Core uses to store your code in portable containers for training and serving.

Containers must be managed and deployed in some way. As a result, SAP AI Core is supported by a Kubernetes infrastructure for managing containerized workloads and services. As a result, SAP AI Core uses these two tools, which are complementary to one another and aid in the development of cloud-native architectures.

What are the advantages of this combination?

First and foremost, applications are easier to maintain because they are broken down into smaller parts; second, these parts run on a more robust infrastructure, and the applications are more widely available. Furthermore, applications can handle more load-on-demand, improving user experience and reducing resource waste.

Let us now turn our attention to another common requirement in software development:

They make application definitions, configurations, and environments declarative and version controlled. This is significant because application deployment and lifecycle management must be automated, auditable, and straightforward.

How does SAP AI Core accomplish this?

It is accomplished through declarative configuration files (referred to as “templates”) that can specify the desired state of the infrastructure, pipeline steps, and all required input parameters and artifacts.

Wrapping It Up!

Here was everything you need to know about the end-to-end Machine Learning operations with SAP AI Core.

SAP AI Core and SAP AI Launchpad are ideal services for assisting customers in managing and running their AI workloads. The AI API includes SAP AI Core and other supported AI workload engines.

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