Last time, we looked at how to use TensorFlow from within SAP HANA, express edition. This allows you to surface your TensorFlow ModelServer models inside your instances and use them as a regular stored procedure.
This allows, for example, to process images or documents stored as blobs with an image classification model or something as simple as a classification on the Iris dataset.
In this tutorial, our aim is to write a schema and load it into our knowledge graph; phone_calls. One that describes the reality of our dataset.
First off, let’s look at the dataset we are going to be working with. Simply put, we’re going to have:
Last time, we looked at how to leverage the SAP HANA R integration, which opens the door to about 11,000 packages. So, if you feel like the built-in libraries (APL and PAL) don’t offer what you need or if you feel like doing something your way too, now you can!
I hope you all managed to try this out, and probably some of you already started comparing the PAL implementation with R algorithms. Feel free to share your feedback!
In this post, we will try and understand the concepts behind evaluation metrics such as sensitivity and specificity, which is used to determine the performance of the Machine Learning models. The post also describes the differences between sensitivity and specificity. The concepts have been explained using the model for predicting whether a person is suffering from a disease or not.
Sensitivity is a measure of the proportion of actual positive cases that got predicted as positive (or true positive). Sensitivity is also termed as Recall. This implies that there will be another proportion of actual positive cases, which would get predicted incorrectly as negative (and, thus, could also be termed as the false negative). This can also be represented in the form of a false negative rate. The sum of sensitivity and false negative rate would be 1. Let’s try and understand this with the model used for predicting whether a person is suffering from the disease. Sensitivity is a measure of the proportion of people suffering from the disease who got predicted correctly as the ones suffering from the disease. In other words, the person who is unhealthy actually got predicted as unhealthy.
Last time, we looked at how to import data in SAP HANA express, and we used the dataset provided by the SAP Predictive Analytics tools (and available online).
But the main idea was to show you how you can import more or less any kind of text/CSV files in your HXE instances.
About a year ago, we told a beautiful story about how KNIME Analytics Platform can be used to automate an established modeling process using the KNIME Model Factory. Recently, our Life Science team faced an exhausting and frightening exercise of building, validating, and scoring models for more than 1500 data sets.
We want to share with you how we adapted the KNIME Model Factory for this monstrous application. We hope this will show you how to implement your own model building routines in the KNIME Model Factory. We will also demonstrate how to scale model building processes to very large tasks using KNIME Server Distributed Executors.
TensorFlow Object Detection is a powerful technology that can recognize different objects in images, including their positions. The trained Object Detection models can be run on mobile and edge devices to execute predictions very quickly. I’ve used this technology to build a demo where Anki Overdrive cars and obstacles are detected via an iOS app. When obstacles are detected, the cars are stopped automatically.
Check out the short video (only 2 mins) for a quick demo.
This article is part of the Key Research Findings from the DZone Guide to Artificial Intelligence: Automating Decision-Making.
As part of the research for our 2018 Guide to Artificial Intelligence, we surveyed 403 developers, data scientists, and technologists. From their responses, we’ve created a quick article on the different algorithms available for working with various AI projects, and which one proved more popular.
Tech companies around the globe have started integrating Artificial Intelligence (AI) into their products for faster processing and substantially reduced manual work. Whether it is search engine results from Google or Siri from Apple, AI has been utilized with perfection by multiple industries to streamline their business practices for better service delivery. However, there are still various tech sub-industries and niches that can take help from AI to finely tune their products and come up with even more customer friendly services. Know Your Customer, also known as KYC, the industry has a lot to benefit from Artificial Intelligence. More and more businesses are being subjected to regulations that require these businesses to carry out full proof KYC verification with the help of an official identity document before a customer is registered. It is high time that AI becomes a cornerstone for KYC software industry for improving the standards of service in this field.
As explained above, KYC stands for Know Your Customer. It is a business practice that is conducted before any product is sold or service is utilized by an end-user. The service provider is required to collect comprehensive personal information from their customers. Verifying those credentials is the responsibility of the company that is collecting information from their customers. Most of the times, an official identity document is used to confirm the identity details of a customer. What aspects of a person’s identity are verified, depends on the regulatory guidelines or the nature of business performing a KYC verification.
I attend many tech conferences around Europe (and occasionally the World), and Africa is always underrepresented. I was delighted to receive an invite to Afrolynk, an annual event that aims to bridge the European and African entrepreneurial scenes. Entering the Microsoft offices in Berlin for the event, a different crowd greeted me from typical tech and startup events, which was an incredible sight to see.
The day started with a round of drumming to bring people into the room and began a solid day of panels and keynotes. Attendees are sharply and colorfully dressed, energetic, and the variety of languages spoken around the venue highlights the multiculturalism of the African continent.
