AI-Driven Intelligence for Applications Built on SAP HANA Cloud

Estimated read time 13 min read

Today’s application users face an overwhelming challenge of making critical business decisions while trying to navigate an unprecedented surge in data. For developers, this translates to building applications that can not only process larger volumes of data but also manage the complexity of data across diverse formats from a myriad of sources—structured or unstructured, local or remote, and everything in between. Yet, many development tools often fall short in meeting these demands. Legacy applications were not designed to handle the scale and complexity of today’s information or deliver the intuitive experiences application users now expect. At the same time, developers face increasing pressure to incorporate even more artificial intelligence (AI) into their solutions.

Intelligent data applications (IDAs) address these three significant trends: ubiquitous data, natural human interaction, and intelligent processing. SAP HANA Cloud especially powers these next-generation applications with advanced database features like machine learning (ML), generative AI, and sophisticated analytics. With its powerful combination of speed, flexibility, and innovation, SAP HANA Cloud’s in-memory database is the foundation for building applications that help businesses realize the full potential of their data. These intelligent applications not only surpass traditional tools but are specifically designed to enhance the experiences of application users and optimize their roles within the company’s business processes.

The Database for Next-Generation Applications 

Using SAP HANA Cloud, developers can create applications that transcend traditional data processing, incorporating data science technologies like machine learning to continuously learn and adapt. By leveraging these capabilities, developers enable intelligent data applications to enhance user experiences, elevate application user expertise, and streamline business operations.  

Four core capabilities make SAP HANA Cloud the database for building IDAs: 

1. Data Integration: SAP HANA Cloud connects to a wide variety of data sources, including both SAP systems and third-party platforms. This solution offers flexible methods for the application to federate and/or directly integrate all types of business data into the database.

2. Multi-Tiered Storage: With options for storing data in-memory (hot), on disk (warm), and/or in a data lake (cold), SAP HANA Cloud ensures businesses can optimally store data balancing access speed, data density, and cost efficiency.

3. Multi-Model Processing: Advanced processing engines within SAP HANA Cloud bring together diverse data types—relational, graph, spatial, vector, and more—into a single database, eliminating the complexity of data-rich applications that must access multiple niche solutions.

4. Advanced Analytics & AI: Embedded machine learning libraries empower sophisticated business applications with predictive models, advanced data analysis, and forecasting capabilities.

Together, these four features of the SAP HANA Cloud database enable tailored solutions that empower application users to perform at a higher level, augmenting their expertise beyond what their years of experience alone would allow. Ultimately, intelligent data apps on SAP HANA Cloud pave the way for delivering enhanced experiences that users desire, while driving greater effectiveness within the company’s business operations.  

Note: For those interested in learning more about intelligent data applications, check out our new e-book, Intelligent Data Apps: The Impact of Generative AI on Applications

Machine Learning Pushdown to the Database 

Machine learning is a cornerstone of intelligent data applications and modern business innovation. With the rise of trends like generative AI, users now expect intuitive and intelligent interactions with their applications. As a result, businesses are increasingly turning to AI to meet the demand for these enhanced, personalized experiences. To help developers and data scientists deliver such applications, SAP HANA Cloud offers an integrated environment where machine learning and other AI capabilities are embedded within the database and can be directly called from the application layer of the stack. 

One of the key advantages of having machine learning embedded within SAP HANA Cloud is that it eliminates the need for complex integrations with additional ML and AI runtimes and environments. In turn, businesses can streamline data analysis, leverage real-time insights for better decision-making, and deliver innovative solutions even faster. In summary, embedded machine learning offers two major benefits:

Optimal design: Algorithms run where the data resides, eliminating costly and time-intensive data transfers.

Singular data model: A unified data architecture reduces the complexity of deploying and managing machine learning workflows. 

Additionally, SAP HANA Cloud makes it easier to automate and simplify the creation of machine learning models using tools like AutoML (automated machine learning). These capabilities enable developers with limited data science experience to incorporate the right ML models into their solutions. For data science experts, on the other hand, the flexibility and depth of SAP HANA Cloud’s machine learning capabilities enables even more sophisticated analyses. 

Machine Learning Libraries in SAP HANA Cloud 

SAP HANA Cloud offers two specialized machine learning libraries: the Predictive Analytics Library (PAL) and the Automated Predictive Library (APL). Each library is designed with distinct features to suit both scientific and business needs.

Predictive Analytics Library (PAL)  

The Predictive Analytics Library (PAL) is a robust toolkit tailored for data scientists and developers with deep mathematical understanding. Optimized for massively parallel in-memory processing, PAL provides over 100 advanced ML algorithms for scalable applications, including classification, regression, clustering, and time series forecasting. Its versatility and depth make it ideal for sophisticated use cases, from building intelligent models to leveraging specialized functions for advanced analytics. 

Common PAL use cases include:

Clustering: Segmenting customers for targeted marketing.Time series forecasting: Predicting sales trends or inventory needs.Outlier detection: Identifying fraud in financial transactions or anomalies in sensor data.Text classification and search: Deriving the semantic context from text data stored in SAP HANA Cloud using text embedding models.

Automated Predictive Library (APL) 

The Automated Predictive Library (APL) simplifies machine learning for non-expert data scientists, business analysts, and developers, enabling them to utilize predictive modeling without extensive technical expertise. Designed with ease of use in mind, APL incorporates AutoML functionality to streamline the entire machine learning process. By automating tasks such as data preparation, variable encoding, and model testing, this ML library allows teams to quickly apply machine learning and focus on generating actionable insights rather than navigating technical complexities. 

Common APL use cases:

Customer retention: Predicting customer churn to inform retention strategies.Sales forecasting: Using time series analysis to predict demand.

Get Hands-on with Machine Learning on SAP HANA Cloud 

Whether you’re a seasoned expert or just beginning your journey into the world of AI, SAP HANA Cloud provides a range of resources to explore and experiment with its machine learning tools:

1. SAP HANA Cloud Basic Trial – Try out SAP HANA Cloud’s machine learning capabilities, including the Predictive Analytics Library (PAL) AutoML features. Use sample data in a free, no-commitment trial environment to get started and explore what the platform has to offer.

2. SAP BTP Free Trial – As the primary choice for individuals and trainings, the SAP BTP free trial is a time-limited sandbox account that gives you a comprehensive, hands-on experience of SAP BTP, including services like SAP HANA Cloud.

3. SAP HANA Cloud Free Tier – As an enterprise customer, access free service plans for SAP BTP solutions (limitations apply), including SAP HANA Cloud. Follow this hands-on machine learning tutorial and easily upgrade to a paid version when you’re ready—without losing any of your work.

4. Learning JourneyDeveloping Regression Models with the Python Machine Learning Client for SAP HANA – Follow a step-by-step guide to create machine learning workflows using the Python ML client for SAP HANA and the hana-ml library. To learn more, go through these additional ML tutorials, review the hana-ml overview, and check out some code examples on GitHub.

5. AI and Machine Learning Blogs – Stay informed with the latest updates to PAL (as of Q3 2024) or explore a comprehensive list of machine learning enhancements in SAP HANA Cloud since its release. 

 

​ Today’s application users face an overwhelming challenge of making critical business decisions while trying to navigate an unprecedented surge in data. For developers, this translates to building applications that can not only process larger volumes of data but also manage the complexity of data across diverse formats from a myriad of sources—structured or unstructured, local or remote, and everything in between. Yet, many development tools often fall short in meeting these demands. Legacy applications were not designed to handle the scale and complexity of today’s information or deliver the intuitive experiences application users now expect. At the same time, developers face increasing pressure to incorporate even more artificial intelligence (AI) into their solutions.Intelligent data applications (IDAs) address these three significant trends: ubiquitous data, natural human interaction, and intelligent processing. SAP HANA Cloud especially powers these next-generation applications with advanced database features like machine learning (ML), generative AI, and sophisticated analytics. With its powerful combination of speed, flexibility, and innovation, SAP HANA Cloud’s in-memory database is the foundation for building applications that help businesses realize the full potential of their data. These intelligent applications not only surpass traditional tools but are specifically designed to enhance the experiences of application users and optimize their roles within the company’s business processes.The Database for Next-Generation Applications Using SAP HANA Cloud, developers can create applications that transcend traditional data processing, incorporating data science technologies like machine learning to continuously learn and adapt. By leveraging these capabilities, developers enable intelligent data applications to enhance user experiences, elevate application user expertise, and streamline business operations.  Four core capabilities make SAP HANA Cloud the database for building IDAs: 1. Data Integration: SAP HANA Cloud connects to a wide variety of data sources, including both SAP systems and third-party platforms. This solution offers flexible methods for the application to federate and/or directly integrate all types of business data into the database.2. Multi-Tiered Storage: With options for storing data in-memory (hot), on disk (warm), and/or in a data lake (cold), SAP HANA Cloud ensures businesses can optimally store data balancing access speed, data density, and cost efficiency.3. Multi-Model Processing: Advanced processing engines within SAP HANA Cloud bring together diverse data types—relational, graph, spatial, vector, and more—into a single database, eliminating the complexity of data-rich applications that must access multiple niche solutions.4. Advanced Analytics & AI: Embedded machine learning libraries empower sophisticated business applications with predictive models, advanced data analysis, and forecasting capabilities.Together, these four features of the SAP HANA Cloud database enable tailored solutions that empower application users to perform at a higher level, augmenting their expertise beyond what their years of experience alone would allow. Ultimately, intelligent data apps on SAP HANA Cloud pave the way for delivering enhanced experiences that users desire, while driving greater effectiveness within the company’s business operations.  Note: For those interested in learning more about intelligent data applications, check out our new e-book, Intelligent Data Apps: The Impact of Generative AI on Applications. Machine Learning Pushdown to the Database Machine learning is a cornerstone of intelligent data applications and modern business innovation. With the rise of trends like generative AI, users now expect intuitive and intelligent interactions with their applications. As a result, businesses are increasingly turning to AI to meet the demand for these enhanced, personalized experiences. To help developers and data scientists deliver such applications, SAP HANA Cloud offers an integrated environment where machine learning and other AI capabilities are embedded within the database and can be directly called from the application layer of the stack. One of the key advantages of having machine learning embedded within SAP HANA Cloud is that it eliminates the need for complex integrations with additional ML and AI runtimes and environments. In turn, businesses can streamline data analysis, leverage real-time insights for better decision-making, and deliver innovative solutions even faster. In summary, embedded machine learning offers two major benefits:Optimal design: Algorithms run where the data resides, eliminating costly and time-intensive data transfers.Singular data model: A unified data architecture reduces the complexity of deploying and managing machine learning workflows. Additionally, SAP HANA Cloud makes it easier to automate and simplify the creation of machine learning models using tools like AutoML (automated machine learning). These capabilities enable developers with limited data science experience to incorporate the right ML models into their solutions. For data science experts, on the other hand, the flexibility and depth of SAP HANA Cloud’s machine learning capabilities enables even more sophisticated analyses. Machine Learning Libraries in SAP HANA Cloud SAP HANA Cloud offers two specialized machine learning libraries: the Predictive Analytics Library (PAL) and the Automated Predictive Library (APL). Each library is designed with distinct features to suit both scientific and business needs.Predictive Analytics Library (PAL)  The Predictive Analytics Library (PAL) is a robust toolkit tailored for data scientists and developers with deep mathematical understanding. Optimized for massively parallel in-memory processing, PAL provides over 100 advanced ML algorithms for scalable applications, including classification, regression, clustering, and time series forecasting. Its versatility and depth make it ideal for sophisticated use cases, from building intelligent models to leveraging specialized functions for advanced analytics. Common PAL use cases include:Clustering: Segmenting customers for targeted marketing.Time series forecasting: Predicting sales trends or inventory needs.Outlier detection: Identifying fraud in financial transactions or anomalies in sensor data.Text classification and search: Deriving the semantic context from text data stored in SAP HANA Cloud using text embedding models.Automated Predictive Library (APL) The Automated Predictive Library (APL) simplifies machine learning for non-expert data scientists, business analysts, and developers, enabling them to utilize predictive modeling without extensive technical expertise. Designed with ease of use in mind, APL incorporates AutoML functionality to streamline the entire machine learning process. By automating tasks such as data preparation, variable encoding, and model testing, this ML library allows teams to quickly apply machine learning and focus on generating actionable insights rather than navigating technical complexities. Common APL use cases:Customer retention: Predicting customer churn to inform retention strategies.Sales forecasting: Using time series analysis to predict demand.Get Hands-on with Machine Learning on SAP HANA Cloud Whether you’re a seasoned expert or just beginning your journey into the world of AI, SAP HANA Cloud provides a range of resources to explore and experiment with its machine learning tools:1. SAP HANA Cloud Basic Trial – Try out SAP HANA Cloud’s machine learning capabilities, including the Predictive Analytics Library (PAL) AutoML features. Use sample data in a free, no-commitment trial environment to get started and explore what the platform has to offer.2. SAP BTP Free Trial – As the primary choice for individuals and trainings, the SAP BTP free trial is a time-limited sandbox account that gives you a comprehensive, hands-on experience of SAP BTP, including services like SAP HANA Cloud.3. SAP HANA Cloud Free Tier – As an enterprise customer, access free service plans for SAP BTP solutions (limitations apply), including SAP HANA Cloud. Follow this hands-on machine learning tutorial and easily upgrade to a paid version when you’re ready—without losing any of your work.4. Learning Journey: Developing Regression Models with the Python Machine Learning Client for SAP HANA – Follow a step-by-step guide to create machine learning workflows using the Python ML client for SAP HANA and the hana-ml library. To learn more, go through these additional ML tutorials, review the hana-ml overview, and check out some code examples on GitHub.5. AI and Machine Learning Blogs – Stay informed with the latest updates to PAL (as of Q3 2024) or explore a comprehensive list of machine learning enhancements in SAP HANA Cloud since its release.    Read More Technology Blogs by SAP articles 

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