March 22, 2022
Kannan Subbiah
FCA | CISA | CGEIT | CCISO | GRC Consulting | Independent Director | Enterprise & Solution Architecture | Former Sr. VP & CTO of MF Utilities | BU Soft Tech | itTrident
Many folks just getting started with data science have an illusory idea of the field as a breeding ground where state-of-the-art machine learning algorithms are produced day after day, hour after hour, second after second. While it is true that getting to push out cool machine learning models is part of the work, it’s far from the only thing you’ll be doing as a data scientist.In reality, data science involves quite a bit of not-so-shiny grunt work to even make the available data corpus suitable for analysis. According to a Twitter poll conducted in 2019 by data scientist Vicki Boykis, fewer than 5% of respondents claimed to spend the majority of their time on ML models [1]. The largest percentage of data scientists said that most of their time was spent cleaning up the data to make it usable. ... Data science is a burgeoning field, and reducing it down to one concept is a misrepresentation which is at best false, and at worse dangerous. To excel in the field as a whole, it’s necessary to remove the pop-culture tunnel vision that seems to only notice machine learning.?
The pandemic has also played a significant role in spurring NaaS adoption, Chambers says. "During the early days of COVID-19 there was a rapid push for users to be able to connect quickly, reliably, and securely from anywhere at any time," he says. "This required many companies to make hardware/software purchases and rapid implementations that accelerated an already noticeable increase in overall network complexity over the last several years." Unfortunately, many organizations faced serious challenges while trying to keep pace with suddenly essential changes. "Companies that need to quickly scale up or down their network infrastructure capabilities, or those that are on the cusp of major IT infrastructure lifecycle activity, have become prime NaaS-adoption candidates," Chambers says. It’s easiest for organizations to adopt small-scale NaaS offerings to gain an understanding of how to evaluate potential risk and rewards and determine overall alignment to their organization’s requirements.
The process of requesting a certificate from a CA, receiving it, manually binding it to an endpoint, and self-managing it can be slow and lack visibility. Sometimes, DevOps teams avoid established quality practices by using less secure means of cryptography or issuing their own certificates from a self-created non-compliant PKI environment – putting their organizations at risk. However, PKI certificates from certified and accredited globally trusted CAs offer the best way for engineers to ensure security, identity and compliance of their containers and the code stored within them. A certificate management platform, which is built to scale and manages large volumes of PKI certificates, is perfect for the DevOps ethos and their environments. Organizations can now automate the request and installation of compliant certificates within continuous integration/continuous deployment (CI/CD) pipelines and applications to secure DevOps practices and support digital transformation. Outsourcing your PKI to a CA means developers have a single source to turn to for all certificate needs and are free to focus on core competencies.?
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Fintech firms typically apply digital technology to processes those legacy institutions find difficult, time consuming, or costly to undertake, and they often focus on getting a single use case like payments, or alternative lending right. In contrast, neo banks, or challenger banks, deliver their services primarily through phone apps that often aim to do many things that a bank can do, including lending money and accepting deposits. A key advantage for both is that they don’t have to spend time, money, and organisational capital to transform into something new. They were born digital. Likewise, they both claim convenience as their prime value proposition. However, while customers want convenience, many still see banking as a high-touch service. If their bank has survived decades of consolidation and has served a family for generations, familiarity can be a bigger draw than convenience. That said, the COVID-19 pandemic has accelerated the online trend. More and more of us auto-pay our bills and buy our goods as well as our entertainment and services via e-commerce.?
The no free lunch theorem entails that a machine learning algorithm’s average performance is dependent on the amount of data it has. “Industry-built quantum computers of modest size are now publicly accessible over the cloud. This raises the intriguing possibility of quantum-assisted machine learning, a paradigm that researchers suspect could be more powerful than traditional machine learning. Various architectures for quantum neural networks (QNNs) have been proposed and implemented. Some important results for quantum learning theory have already been obtained, particularly regarding the trainability and expressibility of QNNs for variational quantum algorithms. However, the scalability of QNNs (to scales that are classically inaccessible) remains an interesting open question,” the authors write. This also suggests a possibility that in order to model a quantum system, the amount of training data might also need to grow exponentially. This threatens to eliminate the edge quantum computing has over edge computing. The authors have discovered a method to eliminate the potential overhead via a newfound quantum version of the no free lunch theorem.
The most likely people to leave a company are highly skilled employees who are in high demand (e.g., IT). Employees who feel they are underutilized and who want to advance their careers, and employees who are looking for work that can more easily balance with their personal lives, are also more likely to leave. It’s also common knowledge that IT employees change jobs often, and that IT departments don't do a great job retaining them for the long haul. HR AI can help prevent attrition if you provide it with internal employee and departmental data that it can assess your employees, their talents and their needs based upon the search criteria that you give it. For instance, you can build a corporate employee database that goes beyond IT, and that lists all the relevant skills and work experiences that employees across a broad spectrum of the company possess. Using this method, you might identify an employee who is working in accounting, but who has an IT background, enjoys data analytics, and wants to explore a career change. Or you could identify a junior member of IT who is a strong communicator and can connect with end users in the business.?