As businesses increasingly embrace intelligent automation and data-driven decision-making, machine learning (ML) has become a key technology in the SAP ecosystem. SAP Business Application Studio (BAS), SAP’s cloud-based integrated development environment, offers an ideal platform to build, train, and deploy machine learning applications that extend and enhance SAP solutions.
This article guides you through the essentials of developing machine learning applications within BAS, highlighting how SAP developers can leverage built-in tools, services, and frameworks to create smart applications efficiently.
SAP BAS is designed for cloud-native development and provides rich tooling for various SAP technologies, including AI and ML:
- Cloud-based environment: No local setup required, ready to connect to SAP services.
- Integration with SAP AI services: Easily consume SAP AI Core, SAP AI Business Services, and SAP Data Intelligence.
- Multiple language support: Python, Node.js, and Java are available to build ML apps.
- Access to SAP HANA ML capabilities: Integrate with in-database ML using SAP HANA Predictive Analytics Library (PAL).
- Prebuilt SAP project templates: Jumpstart ML projects quickly.
- Built-in Git and CI/CD support: Manage and deploy ML models reliably.
- Launch SAP Business Application Studio via your SAP BTP account.
- Create a new Dev Space and select the Machine Learning or Python environment to get pre-installed packages like TensorFlow, scikit-learn, or SAP-specific SDKs.
- Alternatively, create a Full Stack Cloud Application Dev Space to build end-to-end ML apps with frontend, backend, and data services.
¶ Step 2: Connect to SAP AI and Data Services
- Use SAP AI Core and SAP AI Business Services to train, deploy, and consume ML models.
- In BAS, create destinations to securely connect to these services.
- Access SAP HANA as your data source via HANA Cloud connectors for training and inference.
¶ Step 3: Develop and Train ML Models
- Write your model code in Python or JavaScript within BAS.
- Use popular ML frameworks like TensorFlow, PyTorch, or scikit-learn installed in the environment.
- Optionally, use SAP HANA's Predictive Analytics Library (PAL) to run ML algorithms close to your data for faster insights.
Example: Simple Python code snippet to train a classification model:
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
# Load dataset
data = load_iris()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2)
# Train model
model = RandomForestClassifier()
model.fit(X_train, y_train)
# Evaluate
score = model.score(X_test, y_test)
print(f"Model accuracy: {score:.2f}")
¶ Step 4: Package and Deploy the ML Application
- Use Node.js or Python to build APIs exposing your model.
- Containerize your ML app using Dockerfiles supported within BAS.
- Deploy your containerized ML app to SAP BTP Cloud Foundry or Kyma runtime.
- Automate deployment with CI/CD pipelines integrated in BAS.
SAP AI Business Services offer ready-to-use ML capabilities like document information extraction, image classification, or chatbot integration. From BAS:
- Consume these services via REST APIs or SDKs.
- Combine pre-trained SAP models with your custom models.
- Accelerate development by focusing on business logic rather than ML model building from scratch.
- Version control your ML code and data pipelines using integrated Git tools.
- Use virtual environments or containers to manage dependencies cleanly.
- Test models locally and with sample datasets before full deployment.
- Monitor model performance post-deployment using SAP Application Logging and Monitoring tools.
- Automate retraining workflows with CI/CD for continuous improvement.
SAP Business Application Studio empowers developers to create sophisticated machine learning applications tightly integrated with SAP systems and data. With support for popular ML frameworks, seamless integration with SAP AI services, and cloud-native deployment options, BAS is an excellent choice for accelerating intelligent application development in the SAP ecosystem.
Whether you want to embed AI in SAP S/4HANA extensions, build predictive analytics dashboards, or create smart chatbots, BAS provides the tools and platform to turn your ML ideas into enterprise-ready applications.