Amazon Web Services' new visual data preparation tool for AWS Glue allows users to clean and normalize data with an interactive point-and-click visual interface without writing custom code. AWS Glue DataBrew helps data scientists and data analysts get the data ready for analytics and machine learning (ML) 80 percent quicker than traditional data preparation approaches, according to the cloud provider, which made the tool generally available on Wednesday. The new offering builds on AWS Glue, which AWS generally released in April of 2017. AWS Glue is a serverless, fully managed, extract, transform and load (ETL) service to categorize, clean, enrich and move data between various data stores. It has a central data repository called the AWS Glue Data Catalog, an ETL engine that generates Python code automatically and a flexible scheduler to handle dependency resolution, job monitoring and retries.
Because of the availability of various data analytics software, it is possible to examine the large quantity of data utilized for competitive benefits. This software is used for mining the data, which helps to track a various array of business activities. These activities involve current sales data and historic inventories information that can be processed based on scientific queries. Several linked technologies enable visualization software to represent the outcomes of the data. These involve ETL tools, data warehouse devices, and sometimes cloud computing support too.
Machine learning and AI continue to reach further into IT services and complement applications developed by software engineers. IT teams need to sharpen their machine learning skills if they want to keep up. Cloud computing services support an array of functionality needed to build and deploy AI and machine learning applications. In many ways, AI systems are managed much like other software that IT pros are familiar with in the cloud. But just because someone can deploy an application, that does not necessarily mean they can successfully deploy a machine learning model.
Services Australia has a data exchange program underway with the Australian Taxation Office (ATO) that flags people who are on the federal government's JobKeeper scheme. "There are some people who haven't declared JobKeeper payments as income on their record," Services Australia deputy CEO, customer service delivery Michelle Lees said. "Based on the data exchange information, we're aware there are approximately 135,000 people who were receiving a social security payment who were identified by an employer as being eligible for JobKeeper. It doesn't necessarily mean, in some instances when we contact them, they might actually say they haven't received a JobKeeper payment, whereby we'd refer that back to the ATO to follow up." Lees said in the event that there was a recalculation of entitlement required, because someone has updated their details, the program could flag that there was a provisional debt.
When designing and building data pipelines to load data into data warehouses you might have heard of the common ETL and ELT paradigms. This post goes over what they mean, their differences and which paradigm you might want to choose. If you are wondering why we have a staging area click here. ELT is very similar but the data is loaded into a table before being transformed to a final table which is used by users. As you can see it has fewer components compared to the ETL approach.
Researchers at the National University of Singapore recently demonstrated the advantages of using neuromorphic sensor fusion to help robots grip and identify objects. It's just one of a number of interesting projects they've been working on including developing a new protocol for transmitting tactile data, building a neuromorphic tactile fingertip, and developing new visual-tactile datasets for the development of better learning systems. Because the technology uses address-events and spiking neural networks it is extremely power efficient: 50 times more using one of the Intel Loihi neuromorphic chips than a GPU. However, what's particularly elegant about this work is that it points the way towards neuromorphic technology as a means of efficiently integrating -- and extracting meaning from -- many different sensors for complex tasks in power-constrained systems. The new tactile sensor they used, NeuTouch, consists of an array of 39 taxels (tactile pixels) and the movement is transduced using a graphene-based piezo-resistive layer; you can think as this as the front of the robot's fingertip.
At FutureLearn we work in short sprints & regularly share, reflect on and iterate on our work. This helps us focus on shipping small, iterative changes and responding quickly to changing business or user needs. We care about work/life balance and supporting learning at work. The Data Platform Team builds and maintains tooling and infrastructure that supports decision making processes across the business and enables product improvements by providing a complete and consistent view of our business data. Our tech stack consists of an ETL process written in Ruby and managed by Airflow which sources data from our production database (MySQL), our email provider (Sendgrid), application logs, and other operational data sources.
This note is a little break from our model homotopy series. I have a neat example where one combines two classifiers to get a better classifier using a method I am calling "ROC surgery." In ROC surgery we look at multiple ROC plots and decide we want to cut out a section from one the plots for use. It is a sensor fusion method to try and combine the best parts of two classifiers.
Oracle is sprucing up its customer data platform, CX Unity, with a little machine learning. The platform will now support real-time behavioral data collection and personalization capabilities through Infinity, Oracle's digital streaming technology. Infinity captures web event, app and point-of-sale data to help brands build realistic representations of the customer journey. That previously required repeated and rigorous A/B testing, said Rob Tarkoff, EVP and general manager of Oracle Cloud CX and Oracle Data Cloud. "But by applying machine learning to that, you can come up with predictions and insights based on a holistic set of data, and it doesn't require you to do one-off activities," he said.