Oracle has introduced AI Services in Oracle Cloud Infrastructure (OCI) to make it easier to apply AI to your business scenarios. These services can enhance applications and business processes with new capabilities that create better customer experiences, empower employees with better information, and improve and automate business operations.
Some of the services, such as Language, Speech, and Vision are focused on understanding the world around you - analyzing written language, converting speech-to-text, and extracting information from images. Other services are focused on supporting business decisions such as detecting anomalies in time-series data (like from machinery and equipment sensors) and forecasting metrics and signals that vary over time.
Oracle’s data scientists have done the work to provide these services with great machine learning models so developers can easily use this functionality without needing data science expertise. More importantly, these services give customers the ability to re-train the models with their own data. This ability to tailor a service to business or industry-specific data is required to make AI work for business.
You may already be familiar with Oracle Digital Assistant, which has been around since 2018. It offers prebuilt skills and templates to create conversational experiences for your business applications and customers through text, chat, and voice interfaces. Developers can build on the library of templates and create their own custom skills to automate the customer experiences. And it comes with pre-built skills for common business-related tasks like expense reporting or information retrieval.
OCI Language helps you apply AI to understanding textual information. Use it to analyze customer feedback to understand what your customers are thinking about your products or your business. Apply Language to automate processes that are document heavy – classifying documents, identifying the key points in a contract, or analyzing RFPs or news articles.
OCI Speech performs automatic speech recognition to convert speech to text. Developers can use Oracle’s time-tested acoustic and language models to provide highly accurate transcription for audio or video files across many languages, without needing any data science experience. You can process data directly in object storage and get accurate timestamped transcriptions.
OCI Vision brings in the best of visual and text technologies to deliver results with two main capabilities. The first capability is for analyzing images – both the “cat vs. dog” example, but also for detecting objects and bounding boxes inside an image. You can bring your own labeled data or use our data labeling service to create your own labeled data sets and train custom models. The second capability is around document AI. You can use it to understand documents – whether it’s scanned pages, forms, or even images containing textual information. OCI Vision provides pre-trained models based on industry data, but you can also bring your own data to customize it for your own specific business scenarios.
OCI Anomaly Detection simplifies scalable system monitoring and diagnostic solutions. Users train models using their own multi-variate time series data. The service provides multiple data processing techniques that account for errors and imperfections in real-world input data, such as from low-resolution sensors. It automatically identifies and fixes data quality issues—resulting in fewer false alarms, better operations, and more accurate results. OCI Anomaly Detection is backed by more than 150 patents, using our MSET-2 algorithm and other deep learning techniques, to detect anomalies earlier. These algorithms work together to ensure higher sensitivity and better false alarm avoidance than other machine learning (ML) approaches.
OCI Forecasting allows you to forecast a variety of business metrics such as product demand, revenue, resource requirements, service requests, etc. You can build multiple models and ensembles to create the best model that maximizes forecast accuracy. Use these models to deliver forecasts with confidence intervals and explainability to support data-driven business decisions.
When you want to customize a model, you often need to bring your own labeled data to help deliver the last mile of AI accuracy. To help with that task, Oracle has recently launched OCI Data Labeling, a service that provides an easy and convenient way to bring in your own data and label it for building business specific custom models. It works across the entire Oracle AI stack, so whether you’re building a custom model from scratch in OCI Data Science, or just tuning Vision service to work for your business – the same labeling experience consistently serves you across your enterprise.
Oracle AI helps your applications make sense of all your data. As with all of Oracle’s enterprise solutions, Oracle AI enables true production AI for your most important workloads. Apply AI to help use your data in new ways, whether it’s preparing your data with AI so you can drive better analytics experiences or using AI with your data to help make the right decisions and predictions for your business.
Oracle AI is built on experience with industries. As the world’s leading enterprise applications provider with more than 80 apps ranging from the ERP suite to HCM to vertical offerings like Primavera and Opera, Oracle has gained experience from working with thousands of customers to discover how customers want to use AI to improve their processes and results. The industry experience represented by our apps informs our AI as we pretrain the services and integrate them into those apps.
Oracle is making AI accessible to everyone with OCI AI Services. We’re giving developers the ability to build their own models and the ability to easily consume already-built ML models and extend apps and solutions, without needing to be machine learning experts. We are making AI services easy to leverage collaboratively across all your developer and data science teams with integrated data labeling, consistent APIs, and common discovery across model and dataset catalogs. Finally, we are doing all this with a deep commitment to open frameworks to use open source and third party tools and full portability.
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