Oracle Machine Learning

Machine Learning on Oracle Cloud - What Is it?

Machine Learning in Oracle databases enables organizations to analyze data using advanced ML algorithms directly within the database, eliminating the need for complex and time-consuming data transfers. This capability extends beyond on-premises deployments, offering seamless scalability and flexibility in Oracle Cloud Infrastructure (OCI). By leveraging OCI’s fully managed database services, businesses can run ML models natively in the cloud, benefiting from high performance, built-in security, and automatic scaling—all while keeping data close to where it’s stored.

Oracle offers a wide range of tools and features to facilitate the implementation of ML solutions, including:

Oracle Machine Learning (OML):

A comprehensive tool that enables the creation, training, and deployment of ML models directly within the Oracle database. It supports the "move the model to the data" philosophy.

SQL for Machine Learning:

Extensions to the SQL language that facilitate performing ML tasks, such as classification, regression, and clustering, directly within the database.

Integration with ML Libraries:

The ability to integrate with popular ML libraries, such as TensorFlow and PyTorch, leveraging the computational power of the database.

Support for SQL, R, and Python:

Access to interfaces in SQL, Python, and R for data exploration, modeling, and deployment of ML solutions.

Improved Forecast Accuracy:

ML models help predict future trends and behaviors, enabling better planning and strategic decision-making. With advanced ML models, you can accurately forecast demand, optimize prices, and personalize offers, leading to increased revenue and better resource utilization.

Process Automation:

ML can be used to automate various processes, such as fraud detection, recommendation personalization, and supply chain optimization. ML-based automation allows for real-time fraud detection, automatic product recommendations, and optimization of logistics processes, reducing costs and improving operational efficiency.

Increased Efficiency:

Analyzing data with ML directly in the database helps in identifying bottlenecks and optimizing processes, resulting in a significant increase in efficiency. This eliminates the time-consuming and resource-intensive process of moving large datasets.

Cost Reduction:

Automation and better forecasting, combined with the efficiency of in-database processing, can contribute to a significant reduction in operating costs. "Moving the model, not the data" minimizes data transfer costs and infrastructure needs.

Scalability and Performance:

Oracle Database offers high performance and scalability for data exploration, building, and deploying models, thanks to parallel architecture and optimizations.

Simplified Architecture:

By processing data in the database, you can simplify the IT architecture and maintain data synchronization and security.

Operating on massive datasets often presents challenges when traditional methods require extracting data from a database and transferring it to an external ML environment. This process can create performance bottlenecks, increase costs, and introduce security risks.Oracle's in-database machine learning capabilities eliminate these issues by allowing ML models to run directly within the database. This architecture enhances performance, reduces costs, and mitigates security concerns by avoiding:

Data Transfer Delays – No need to move large datasets to external systems.
Security Risks – Minimizes exposure of sensitive data during transfers.
Computational Overhead – Reduces resource consumption for data extraction and transformation.

Fraud detection, credit risk assessment, personalization of investment offers.

Personalization of marketing campaigns, prediction of customer behavior, price optimization.

Disease diagnosis, prediction of hospitalization risk, treatment personalization.

Optimization of production processes, prediction of machine failures, quality control.

Inventory optimization, personalization of product recommendations, analysis of shopping baskets.

Defining Business Goals: Determining the problems to be solved with ML.
Collecting and Preparing Data: Gathering data, cleaning it, and preparing it for analysis within the database.
Selecting Appropriate ML Algorithms: Choosing algorithms that best fit the data and goals. Oracle Machine Learning offers a wide selection of algorithms tailored to various problems.

Training ML Models: Training models on prepared data directly in the database.
Deploying ML Models: Deploying models in a production environment. Models can be easily deployed using SQL, REST APIs, and no-code interfaces.
Monitoring and Optimization: Monitoring model performance and optimizing them. Oracle Machine Learning offers mechanisms for monitoring data and models to ensure their continuous performance.

Automated Machine Learning (AutoML)

With AutoML, you can quickly Utilize AutoML to quickly and easily create ML models, even without advanced data science knowledge. This tool automatically selects and tunes algorithms, significantly speeding up the ML implementation process. AutoML simplifies modeling and makes ML more accessible to non-technical users.

OML Services:

OML Services enable flexible management and deployment of ML models via REST APIs. This allows for the integration of ML models with real-time applications optimized for performance. OML Services support model management, deployment, and monitoring.

Data and Model Monitoring:

Oracle Machine Learning offers data and model monitoring capabilities to detect data drift and changes in quality metrics. This enables a quick response to potential issues and maintains the quality of predictions.

Rapid Enterprise Deployments

Oracle Machine Learning offers easy model deployment options via SQL and REST APIs, ensuring immediate availability of ML models. This allows for the quick integration of ML into existing applications and dashboards.

Data Security:

Oracle Database provides built-in data and ML model security, including encryption and role-based access control. This ensures that your data and ML models are protected.

To ensure a complete implementation of a Machine Learning solution in Oracle Databases, Goldenore has a number of key skills and expertise. These can be divided into the following areas:

What Goldenore offers:

Goldenore brings deep expertise in Oracle Machine Learning (OML) and data-driven solutions, offering:

Comprehensive OML Knowledge – Expertise in OML components, including in-database algorithms, OML for SQL and Python, AutoML, and OML Services. Proficiency in no-code tools such as Oracle Data Miner and the AutoML interface ensures seamless ML adoption.
Advanced Data Engineering – Skilled in data access, integration, transformation, and preparation within Oracle Database environments. Proficient in SQL and ETL processes to enable efficient ML workflows.
Strong Data Science Capabilities – Extensive experience in building, training, evaluating, and deploying ML models using classification, regression, clustering, anomaly detection, and time-series forecasting. Expertise in SQL, Python, and AutoML interfaces ensures optimal algorithm selection and tuning.
Scalable Model Deployment – Knowledge of multiple deployment strategies, including SQL-based scoring, REST API integration with OML Services, and embedded Python execution, ensuring high performance and scalability.
Seamless Application Integration – Proficiency in integrating ML models and insights into applications and dashboards using SQL, REST APIs, and Oracle APEX.
Robust Security & Data Management – Expertise in leveraging Oracle Database’s built-in security features to protect data, models, and scripts.
Open-Source & Hybrid Integration – Familiarity with the Python ecosystem, popular ML libraries, and model exchange formats like ONNX, ensuring smooth interoperability between Oracle Machine Learning and open-source tools.

Having these key skills allows Goldenore to implement Machine Learning solutions in Oracle Databases comprehensively and efficiently, bringing value to customers and realising the full potential of OML technology.

Machine Learning on Oracle Cloud - What Is it?

Oracle offers a wide range of tools and features to facilitate the implementation of ML solutions, including: