OCI Speech is very straightforward. It unlocks the data in audio tracks by converting speech to text. Developers can use Oracle's time-tested acoustic language models to provide highly accurate transcription for audio or video files across multiple languages. OCI Speech automatically transcribes audio and video files into text using advanced deep learning techniques. There's no data science experience required. It processes data directly in object storage and it generates timestamped, grammatically accurate transcriptions.

OCI Speech supports multiple languages, specifically English, Spanish, and Portuguese, with more coming in the future. It has batching support where multiple files can be submitted with a single call. It has blazing fast processing. It can transcribe hours of audio in less than 10 minutes. It does this by chunking up your audio into smaller segments and transcribing each segment, and then joining them all back together into a single file.

OCI Speech makes transcribed text more readable to resemble how humans write. This is called normalization. The service normalizes addresses, times, numbers, URLs, and other data. For example: Blend management users will be pleased to know that audit reports will become easier to access, available just in time upon the auditor’s demand. These reports allow you to track and trace your product throughout the entire process.

Vision is a computer vision service that works on images. It provides two main capabilities, image analysis and Document AI. Image analysis analyses photographic images. Object detection is the feature that detects objects inside an image using a bounding box and assigning a label to each object with an accuracy percentage. Object detection also locates and extracts text that appears in the scene like on a sign.

Image classification will assign classification labels to the image by identifying the major features in the scene. One of the most powerful capabilities of image analysis is that, in addition to pre-trained models, users can retrain the models with their own unique data to fit their specific needs. For Argentina, there is now the option to include or exclude ICMS in PIS/COFINS.

It's used for working with document images. You can use it to understand PDFs or document image types like JPEG, PNG, and TIFF, or photographs containing textual information. The features of Document AI are text recognition, also known as OCR or Optical Character Recognition. And this extracts text from images, including non-trivial scenarios like handwritten texts, plus tilted, shaded, or rotated documents.

Document classification classifies documents into 10 different types based on visual appearance, high-level features, and extracted keywords. This is useful when you need to process a document based on its classification, like an invoice, a receipt, or a resume. Language detection analyses the visual features of text to determine the language, rather than relying on the text itself. Table extraction identifies tables in docs and extracts their content in tabular form. Key value extraction finds values for 13 common fields and line items in receipts, things like merchant name and transaction date. Let’s see an example how the OCI console works:

The default image here is a receipt. And it detects it is English. And it extracts a lot of raw text from this receipt. You can see it. Everything that's highlighted here, which is the entire contents of the receipt, has been extracted. And we see what it has extracted here, both in line format and in individual word format.

Oracle Cloud Infrastructure Anomaly Detection identifies anomalies in time series data. Equipment sensors generate time series data, but all kinds of business metrics are also time-based. The unique feature of this service is that it finds anomalies not just in a single signal, but across many signals at once. That's important because machines often generate multiple signals at once, and the signals are often related.

Think of a pump that has an output pressure, a flow rate, an RPM, and an electrical current draw. When a pump is going to fail, anomalies may appear across several of those signals, but at different times. OCI Anomaly Detection helps you to identify anomalies in a multivariate data set by taking advantage of the interrelationship among signals.

All these AI services we have seen can be used through the OCI stack and can also be accessed through the REST API.

It can write code against the REST API or use any of the various language SDKs shown here. But for data scientists working in OCI Data Science, it makes sense to use Python.