Recently I built a Facial Recognition Mobile App using Oracle Visual Builder having set up the Facial recognition APIs using Tensorflow taking some inspiration from FaceNet. As highlighted above the app does the following: record a video of your face and send it to the API that generates various images and classifies them based on the label we provide at runtime. And in turn, invoke another API that is going to train the machine learning model to update the dataset with the new images and label provided. These two APIs will build a facial recognition Database. Once I have this, I can capture the face and compare that with the dataset I have captured earlier in my Facial recognition Database to output if the face exists in our system.
Here is a quick cheat sheet if you ever wanted to build a mobile app that can take advantage of the camera built into the device, capture the vehicle or vehicles nameplate(s) in a frame and process that image and send it on API that can analyze the image and relay back the information it just scanned. This app can be extended to fulfil requirements like checking if the vehicle registration is up to date or insurance renewal is overdue etc. provided if there are APIs already available that can deliver this information.
So what is the tech involved in building this app?
- To build a mobile app that can be deployed on iOS or Android, I used the Visual Builder service from the Oracle Cloud stack. This service provides the capability to build Web as well as Mobile applications through a declarative approach with the ability to introduce code for any complex requirements.
- To store the captured image and use the image for downstream application purposes I used the Oracle Content & Experience service that comes with a rich set of APIs for content ingestion, public document link generation etc. From an enterprise architecture viewpoint, it makes sense to store the images with metadata in a content store, so I decided to archive the image using this service as part of the mobile app build process.
- The most significant bit is to use a library / API that can process the image or OCR and send back the information we are interested n. For these purposes, I used the open source ALPR library. There are API’s available already if you want to fast track your app.
- This one is optional. If you want to validate the information captured, we can set up a few API’s using the Oracle Autonomous Database with some data to complete the validation flow in the app.
This is what the Architecture would look like :
Hope you have heard about the Oracle’s Self Driving Autonomous Database. Autonomous Database is an autonomous data management software in the cloud to deliver automated patching, upgrades, and tuning — including performing all routine database maintenance tasks while the system is running — without human intervention. This new autonomous database is self-driving, self-securing, and self-repairing, which helps to eliminate manual database management and human errors. Also, there is also a secret weapon called Machine Learning in a Box built into the Oracle Autonomous Database Platform. Here is a quick lab guide to get you started on how to use the Oracle Autonomous Database Platform.
In this article, I would like to walk you through a practical example of how we can take advantage of the Machine Learning capability in the Oracle Autonomous Database Platform and make decisions instantly.
Here is a background of our Fictitious company: Vision Housing Finance Corp that deals in home loans. They have a presence across all urban, semi-urban and rural areas. Customer first applies for a home loan after that company validates the customer eligibility for a loan.
VisionCorp wants to automate the loan eligibility process (real-time) based on customer detail provided while filling the online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. So they would like to understand if they can take their existing data sets and apply some machine learning to automate the loan decision-making process. You can download the historical dataset that this company has provided from here.
As we roll out the Oracle Digital Assitant workshop across Australia and New Zeland over the course of the next few weeks, below are the instructions for the participants interested to try out the platform and build a Digital Assistant.
- If you have registered for the event, you would get an email to set up the password for your Oracle cloud account on the day of the workshop.
- Once you set up the password access Digital Assistant UI to start building your first Digital Assistant.
- Download the hands-on lab material from here that has detailed instructions on how to design and deploy a Digital Assistant.
Here are some interesting links that can compliment your learning process of the Oracle Digital Assitant or if you would like to re-visit them later.
Oracle Digital Assistant Channel: This playlist is dedicated to covering all the major features of Oracle Digital Assitant. You can watch in sequence for an end-to-end insight into Oracle Intelligent Bots, or dip into any video to learn about that features.
Additional Blogs that might help you in your Oracle Digital Assistant learning journey
Leave your comments here if you would need any more information on this topic.
Majority of the websites have FAQ pages, and they are always dull. If only you could convert your FAQ to something more tangible that solves the users need you could resolve customer frustration of finding the right information. Moreover, if that experience is more interactive, it leads to an engaging experience where you can contextualise the data and also execute the task on behalf of the customer. We can create this rich experience by making your traditional FAQ’s wrapped inside a chatbot.
In this article let’s have a look at how we can quickly convert FAQ Pages to a Bot in minutes. Oracle’s natural language understanding technology and QnA engine available in Digital Assistant platform can sift through the historical FAQ data and answer even sophisticated requests from your clients. Further, you can handover complex queries to your support as I discussed in an earlier article
In earlier articles, I discussed Autonomous Digital Assistant, provisioning a Digital Assistant, building skills and making it multi-lingual. In this post, I would like to take the discussion forward to address certain scenarios where there is a need for Human Intervention when the Bot cannot handle the conversation and instead redirect the chat to a human agent.
In the last post, we talked briefly about the Oracle Digital Assistant. In this post, I would like to walk you through the provisioning process of the Autonomous Oracle Digital Assistant.