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What is an AI Operating System: A Deep Dive with SORBA.ai
In this blog post, we're exploring the technical intricacies of enterprise AI applications. We'll delve into the essential technical requirements of an enterprise AI application and discuss why these are critical for unlocking economic value from digital transformations.
The Traditional Approach to Building Enterprise Applications
Traditionally, application development involved choosing a database, piping selected datasets into that store, modeling the data, applying business logic via an application server, and exposing insights through a user interface (UI) and web server. This method, while simple and powerful for its original purpose, has limitations in the context of evolving enterprise needs.
This approach allowed for some customization in workflows and automation like robotic process automation (RPA), but generally, it was limited to traditional use cases and simple automation.
Enterprise Applications in the Cloud Era
The emergence of cloud computing revolutionized enterprise software, offering virtually unlimited scalability, improved flexibility, and better economies of scale. Organizations migrated to the cloud for a cheaper, faster, and more reliable technology, yet the application architecture largely remained the same, except for the shift from physical data centers to cloud hosting. However, the core structure of running on a single database, applying business logic, and using a UI for insights persisted.
The Inflection Point: AI/ML and Advanced Analytics
The advent of AI/ML and advanced analytics marked a crucial turning point. Enterprises began to recognize the potential of these technologies to transform their businesses. The journey often started with consolidating data in warehouses, evolving into data lakes or scalable cloud data stores as data volumes and cloud computing capabilities grew.
Many organizations embarked on data projects, often not directly correlated with specific business use cases or disconnected from the actual economic value for the business. This led to significant issues, as organizing an entire enterprise’s data in one go is an overwhelming task.
The Challenge with Data Lakes and AI Application Development
As data consolidated into data lakes, data science teams began to add analytic and ML models, working directly against these data lakes. This approach resulted in each team interpreting, cleaning, and merging data independently, leading to a lot of custom work and inconsistency.
While companies hired skilled data scientists capable of prototyping models and conducting effective science, translating these efforts into scalable, economically valuable AI/ML digital transformations proved challenging. Most initiatives stalled at the prototyping stage.
The Unaddressed Problems in AI/ML Integration
Two major issues remained unresolved. First, enabling data scientists and developers to build reusable data and analytic artifacts, standardize data and analytic management across the enterprise, and publish scalable data products. Second, providing end users, often frontline business users, access to these artifacts for day-to-day decision making.
Attempts to address these challenges with reporting tools proved insufficient, especially for dynamic, interactive, and collaborative use cases.
The Reality of Bolt-On AI Approach
This approach, though theoretically sound, led to various analytic challenges due to the separation of models and applications. Issues included data mismatches, governance challenges, difficulties in operating models at scale, and trust issues in the algorithms.
The Enterprise AI Operating System: A Comprehensive Solution
SORBA.ai’s Enterprise AI Operating System addresses these shortcomings by integrating AI/ML models directly within the business application. It combines a unified data image, business application logic, visualizations, workflows, and system integrations. This approach eliminates the divide between AI models and business applications.
Technical Requirements for Enterprise AI Applications
Developing, deploying, and operating enterprise AI applications with SORBA.ai involves:
- Complex data integrations from different data types in disparate stores
- Unified data management
- Rich model development tooling and comprehensive ML Ops services
- Application development tools for rapid building of data models, user interfaces, and logic
- A new tech stack for standardization and scalability across data integration, model, and application development, and operations
At SORBA.ai, we've developed an integrated software stack to meet these diverse requirements, offering data integration, model development, ML Ops, and application development capabilities for large-scale enterprise AI deployments.
Machine Learning Development and Deployment with SORBA.ai
The development of machine learning models is the most iterative and crucial step in the enterprise AI lifecycle. It involves data engineers and scientists working collaboratively to define the best features and develop high-performing machine learning models. This process is characterized by numerous experiments, testing various machine learning features and modeling techniques across different libraries such as Keras, PyTorch, TensorFlow, and others.
SORBA.ai presents an open framework that significantly accelerates model development and experimentation. Our platform provides a library of more than 30 pre-built ML pipelines, spanning deep learning, natural language processing, forecasting, and tree-based models. These pipelines are developed by our expert data scientists and cater to a wide range of enterprise AI use cases across numerous industries. Throughout the development lifecycle, teams leverage a unified data image, build reusable data and analytic artifacts, perform standard transformations, and publish approved data and analytic products, enabling rapid development and scaling.
Post model development, the deployment phase is arguably even more complex. Large-scale enterprise deployments of machine learning models impose diverse requirements. These range from different deployment schemas, such as champion-challenger deployments and randomized A/B tests, to ongoing inference and model performance monitoring, ensuring continued value creation from the production enterprise AI applications. Managing a significant population of production models with diverse data uses, third-party library integrations, hardware optimization (CPU vs. GPU), and inference workloads is impractical with a bolt-on AI approach.
The SORBA.ai platform provides a comprehensive model deployment framework that offers the flexibility and agility necessary for enterprise-scale AI deployments. This framework supports various deployment schemas, provides multiple asynchronous runtimes, hardware profiles for inference, and ensures sustained value delivery from AI applications through ongoing model performance monitoring. This ensures that AI insights are directly embedded into business processes with interpretable evidence packages, ensuring fairness, lack of bias, and trust in AI models.
Orchestration Across All IT and OT Systems
Operating multiple production enterprise AI systems requires sophisticated orchestration across various databases, enterprise IT and OT systems, different ML libraries and runtimes, and processing paradigms. SORBA contains over 30 industrial drivers and a suite of IOT connectors in order to help consolidate data from across different silos into a single location where it can be collectively analyzed. Data processing and model inference requirements can vary dramatically across different enterprise AI applications. For instance, a predictive maintenance application that monitors asset performance might require continuous processing of machine learning features and predictions, while batch processing might suffice for a demand forecasting application.
SORBA.ai provides a common orchestration and abstraction layer that sits atop all enterprise IT and OT systems. This platform enables utmost scalability for at-scale deployments of enterprise AI applications, supporting both continuous and batch processing and providing elastic scalability based on development and operations workloads.
Accelerating Application Development with SORBA.ai
Accelerating application development is critical in ensuring that enterprise AI delivers tangible value for the business. Alongside model development and data science work, development teams focus on defining application logic, developing user interfaces, and building integrations with existing business applications. SORBA.ai offers a low-code/deep-code development environment, SORBA.ai Studio, which provides tools and configurable components for rapid development of user interfaces, visualizations, and workflows. This accelerates the deployment of enterprise AI applications significantly.
Additionally, SORBA has a built in class and instance structure. Once you have developed a machine learning application for a single machine, you can generate a class from this instance, and use that class to replicate the same design across other similar systems. This can be done at a cloud level as well, using a SORBA cloud instance to deploy a created class across multiple devices, significantly accelerating the replication process across an organization.
SORBA.ai's Comprehensive Enterprise AI Solutions
The complexities and evolving requirements of enterprise AI applications are formidable. The bolt-on AI approach, with its limitations, is not viable for enterprise-scale AI deployments, as evidenced by numerous failed experiments. We have significantly invested in our product portfolio to enable large-scale AI deployments and have built a substantial production footprint of enterprise AI globally. We continue to partner with the world’s largest and most iconic organizations, delivering the transformative power of enterprise AI.
