The services would help developers integrate AI into their operations without needing data science expertise.
Oracle is rolling out a suite of artificial intelligence (AI) services called Oracle Cloud Infrastructure (OCI) that is designed to assist companies with turbocharging innovation, accurately assessing business conditions and enabling new experiences for their customers.

OCI is a suite of services that makes it easier for developers to integrate AI functionality into their applications without having to rely on data science specialists. The new OCI AI gives developers the option of using out-of-the-box AI models that have been pretrained on business data, or custom trained using their company’s data.
The platform features six new fully managed AI services.
OCI Language performs text analysis at scale. This service analyzes unstructured text in documents, customer feedback exchanges, support tickets and even social media interactions. OCI Language bypasses the need for machine learning expertise, instead empowering developers to apply tools such as sentiment analysis, key-phrase extraction, text classification and named entity recognition directly into their business applications.
OCI Speech brings automatic speech recognition functionality to the platform. Its prebuilt models are trained on thousands of native and non-native language speakers for real-time speech recognition. This feature allows developers to seamlessly convert file-based speech audio into text transcriptions with impressive accuracy. These transcriptions can be used for in-workflow closed captions, content indexing and enhanced analytics on audio and video content.
OCI Vision empowers image recognition and document analysis tasks through pretrained computer vision models. Those models can also be extended to other industries and customer-specific use cases, including scene monitoring, detection of defects and document processing with their own data. In addition, this feature can be deployed to identify visual anomalies in manufacturing, extract text from forms to automate business workflows, and identify and tag individual items in images to accurately count products and shipments.
OCI Anomaly Detection supports business-specific anomaly detection models that identify critical irregularities early. This enables faster fixes at earlier stages, minimizing operational disruption. OCI Anomaly Detection provides REST APIs and SDKs for multiple programming languages, enabling developers to integrate anomaly detection models into their company’s applications. This feature is powered by Oracle’s MSET2 algorithm—which is used for critical jobs such as nuclear reactor health monitoring and fraud detection. The algorithm is also used to prevent equipment breakdown and predict failures based on multiple data sources.

OCI Forecasting uses machine learning and statistical algorithms to deliver time-series forecasts—without needing data science expertise. OCI Forecasting helps developers quickly create accurate forecasts for critical business metrics such as product demand, revenue and resource requirements. The resulting forecasts include confidence intervals and explain-ability that empowers developers to make more accurate and timely business decisions.
OCI Data Labeling is a tool that helps users create labeled datasets for training AI models. Users can use OCI Data Labeling to assemble data, create and navigate datasets and apply labels to data records generated from user interfaces and public APIs. The datasets can be exported and used for model development in other Oracle AI and data science services, including OCI Vision and OCI Data Science. This allows for consistent model building across the platform.
Children’s Medical Research Institute uses OCI AI to process data—and look for cures.
Oracle is rolling out this offering in a business environment where companies are becoming increasingly reliant on AI to take stock of their operations and innovate. However, these companies often run into problems when trying to implement AI-driven improvements—from a scarcity of data science experts to difficulties in training models on their business data to problems in running the platform once it goes live to obstacles in connecting data silos. These problems result in companies spending valuable time and resources in getting their AI up and running—when what they really need is a responsive and consistent AI platform that can work in their business applications and operational environments and provide actionable insights to help them compete.
“It’s essential for organizations to bridge the gap between the promise of AI and implementing AI that helps them achieve real results,” said Greg Pavlik, chief technology officer, Oracle Cloud Platform. “Oracle is best positioned to realize the value of AI through our industry-leading expertise in enterprise applications and enterprise data, our next-generation cloud infrastructure, and our deep commitment to building AI services and solutions.”
Oracle’s OCI platform faces stiff competition from the SageMaker platform from Amazon Web Service (AWS) and Azure Machine Learning Studio from Microsoft. These competitors have also geared their offerings to developers who might not have extensive data science expertise.
While Oracle will have to work to catch up to its competitors, a bolstered OCI is a promising new entry for businesses seeking to use their data to grow and innovate.
Read more about what Oracle’s competitors are doing with AI at IBM Embraces Hybrid Cloud and AI with New Operating System.