Sam Camda
Oracle Cloud Infrastructure (OCI) offers AI and ML services that streamline AI training, inferencing, and deployment. These services enhance database performance, integrate generative AI into applications, and cater to diverse business needs, making advanced AI capabilities accessible to organizations of all sizes.
Oracle Cloud Infrastructure AI/ML Services
Oracle Cloud Infrastructure (OCI) provides a suite of AI and ML services designed to enhance AI training, inferencing, and deployment. These services include:
- OCI Generative AI
- Autonomous Database features
- Embedded AI in Fusion Cloud Applications
OCI Generative AI simplifies the integration of generative AI into applications and workflows. It leverages large language models from Cohere, allowing users to interact with pre-built models for text generation, summarization, and text transformation.
The Autonomous Database in OCI uses AI/ML to boost performance and streamline workflows. It generates SQL queries based on natural language inputs through Select AI and integrates Spatial Machine Learning to improve model accuracy with location data.
Embedded AI in Fusion Cloud Applications facilitates the use of AI across an organization, enhancing data with real-time insights and automating processes. For example, Oracle Cloud ERP can automate over 90% of standard financial transactions, increasing efficiency.
The OCI Data Science feature store enhances collaboration and governance by providing a centralized library for features, ensuring consistency and reproducibility across ML models.
Oracle’s AI and ML innovations extend to healthcare through the Oracle Health Data Intelligence platform, which integrates generative AI for care management and pre-built clinical quality analytics.
Data quality is crucial for these OCI services, as it ensures the accuracy and reliability of AI models and analytics, leading to more precise and actionable insights.
Importance of Data Quality in AI
Data quality is vital in ensuring the effectiveness and reliability of AI and ML applications. As these technologies become more integrated into business operations, the accuracy and reliability of data directly influence the outcomes of AI models and analytics.
Poor data quality can significantly undermine the performance of AI models, leading to erroneous predictions, faulty decision-making, and misguided business strategies. To mitigate these risks, organizations must ensure their data adheres to six critical elements of data quality:
- Accuracy
- Consistency
- Validity
- Completeness
- Timeliness
- Uniqueness
Maintaining these elements of data quality supports the development of effective AI models and fosters trust in the data-driven insights they provide. By leveraging Oracle Cloud Infrastructure’s comprehensive suite of AI and ML services, along with stringent data quality management practices, businesses can ensure that their AI initiatives yield reliable, validated, and actionable insights.
Data Quality Management Strategies
Ensuring high data quality involves implementing effective data quality management strategies. Organizations must adopt a proactive approach to identify, document, and address data quality issues.
Key strategies include:
- Data quality assessment frameworks: These provide guidelines for evaluating the accuracy, completeness, consistency, and validity of data sets.
- Data cleansing processes: These involve identifying and correcting errors in data sets using automated tools.
- Data governance: This establishes policies and procedures for managing, storing, and accessing data within an organization.
- Data quality management tools and techniques: These include data profiling software, data quality monitoring systems, and machine learning algorithms to automate and enhance data quality tasks.
Specific techniques include data matching, data validation, and the use of centralized data quality consoles. Collaboration tools and data observability platforms can also improve data quality management.
By adopting these strategies and tools, organizations can maintain high data quality, enhancing their decision-making processes, optimizing operations, and achieving strategic goals more effectively.
Role of Data Quality in Oracle Cloud AI/ML
Oracle Cloud Infrastructure (OCI) emphasizes data quality as essential for its AI and ML services. The OCI Data Science Feature Store exemplifies Oracle’s commitment to maintaining high data quality throughout the AI and ML lifecycle.
OCI’s built-in MLOps capabilities strengthen data quality management by integrating ML model development with deployment and monitoring. The OCI Model Monitoring UI offers a no-code interface to track model performance and detect data quality drifts.
Oracle’s focus on data quality is reinforced by tools like data profiling and cleansing, which automatically identify and correct data anomalies. The integration of machine learning algorithms into these tools automates processes such as data matching and validation.
By ensuring high standards of data accuracy, consistency, timeliness, and completeness, Oracle helps organizations derive precise, actionable insights, enhancing decision-making and operational efficiency.
Case Studies and Real-World Applications
SoundHound, a voice-enabled AI company, leveraged Oracle’s high-performance compute clusters to accelerate the training of its voice AI models. This resulted in more refined and accurate models for its AI-based voice recognition technology.1
A retail giant utilized OCI to process video camera data for customer behavior analysis. By ensuring high data quality and leveraging Oracle’s machine learning services, the retailer improved inventory management, optimized store layouts, and personalized marketing strategies.
In healthcare, Oracle’s Clinical Digital Assistant uses generative AI to assist doctors during patient consultations. The integration of high-quality data from Oracle Health Data Intelligence ensures that these AI-driven tasks are executed accurately.2
A global financial services firm integrated Oracle Cloud ERP with advanced AI models to automate over 90% of its standard financial transactions. This improved operational efficiency and provided strategic insights for better business decisions.
A biotech firm used Oracle Cloud Infrastructure’s AI services to enhance its drug discovery capabilities. By maintaining high data integrity and accuracy, the company reduced the time and costs associated with drug discovery.
These case studies demonstrate how Oracle Cloud Infrastructure’s AI capabilities and data quality management practices enable organizations across various industries to achieve significant improvements in efficiency, innovation, and operational excellence.
Oracle Cloud Infrastructure’s commitment to maintaining high data quality ensures that AI and ML initiatives deliver reliable, actionable insights. By leveraging OCI’s AI capabilities and data quality management practices, businesses can achieve notable outcomes across diverse industries, driving innovation and operational excellence.
- SoundHound Inc. Annual Report 2022. Securities and Exchange Commission.
- Oracle Corporation. Oracle Health: AI-Powered Healthcare Solutions. Oracle Corporation; 2023.
