October 20, 2021

The challenges of cloud data management

IT departments are facing a growing challenge to stay abreast of advancements in cloud technologies, provide day-to-day support for increasingly complex systems, and adhere to ever-changing regulatory requirements. In addition, they must ensure the systems they support are able to scale to meet performance objectives and are secured against unauthorized access. ... Much like data security, adhering to regulatory compliance frameworks is a shared responsibility between the customer and cloud provider. Larger cloud vendors will provide third-party auditor compliance reports and attestations for the regulatory frameworks they support. It will be up to each organization to read the documentation and ensure the contents meet specific compliance needs. Most leading platforms will also provide tools to help clients configure identity and access management, secure and monitor their data, and implement audit trails. But the responsibility for ensuring the tools' configuration and usage meet the framework's control objectives relies solely with the customer. ... We know one of IT's core responsibilities is to transform raw data into actionable insights.

Learning to learn: will machines acquire knowledge as naturally as children do?

We create new-to-the-world machines, with sophisticated specifications, that are hugely capable. But to reach their potential, we have to expose them to hundreds of thousands of training examples for every single task. They just don’t ‘get’ things like humans do. One way to get machines to learn more naturally, is to help them to learn from limited data. We can use generative adversarial networks (GANs) to create new examples from a small core of training data rather than having to capture every situation in the real world. It is ‘adversarial’ because one neural network is pitted against another to generate new synthetic data. Then there’s synthetic data rendering – using gaming engines or computer graphics to render new scenarios. Finally, there are algorithmic techniques such as Domain Adaption which involves transferable knowledge (using data in summer that you have collected in winter, for example) or Few Shot Learning, which making predictions from a limited number of samples. Taking a different limited-data route is multi-task Learning, where commonalities and differences are exploited to solve multiple tasks simultaneously.

IT hiring: 5 signs of a continuous learner

Whatever you call it, it’s an important attribute to consider when hiring or grooming the most capable IT professionals today. A continuous learner can offer more bang for the buck in one of the strongest job markets in recent years. “We have found that many companies, while their job descriptions state they are looking for a certain number of years of experience in a laundry list of technologies, are being more flexible and hiring candidates that may be more junior, or those who lack a few main technologies,” Spathis says, noting that many organizations are willing to take the risk on more junior or less specifically experienced candidates who are eager, trainable, and able to learn new skills. There’s definite agreement on the demand for continuous learners in the IT function today. “To thrive during these changing times, it’s imperative that IT organizations continuously grow and change with changing needs,” says Dr. Sunni Lampasso, executive coach and founder of Shaping Success. “As a result, IT organizations that employ continuous learners are better equipped to navigate the changing work world and meet changing demands.”

Ethical and Productivity Implications of Intelligent Code Creation

AI technology is changing the working process of software engineers and test engineers. It is promoting productivity, quality, and speed. Businesses use AI algorithms to improve everything from project planning and estimation to quality testing and the user experience. Application development continues to evolve in its sophistication, while the business increasingly expects solutions to be delivered faster than ever. Most of the time, organizations have to deal with challenging problems like errors, defects, and other complexities while developing complex software. Development and Testing teams no longer have the luxury of time when monthly product launches were the gold standard. Instead, today’s enterprises demand weekly releases and updates that trickle in even more frequently. This is where self-coded applications come into play. Applications that generate the code themselves help the programmers accomplish a task in less time and increase their programming ability. Artificial intelligence is the result of coding, but now coding is the result of Artificial intelligence. It is now helping almost every sector of the business and coders to enhance the software development process.?

How To Transition From Data Analyst To Data Scientist

Before even thinking about making the transition, one has to be very clear about what a data scientist does and introspect what has to be done to fill the gaps that are needed to make the transition and the skills the person has now. A data scientist not only handles data but provides much deeper insights from it. Other than gaining the right mathematical and statistical know-how, training yourself to look at business problems with the mindset of a data scientist and not just like a data analyst will be of great help. This means that while looking into a problem, developing your critical thinking and analytical skills, getting deep into the problem to be solved at hand, and coming up with the right way to approach the solution will train you for the future. A data analyst might not have great coding skills but surely has to know it well. Data scientists use tools like R and Python to derive interpretations from the massive data sets they handle. As a data analyst, if you are not great at coding or don’t know the common tools, it would be wise to start taking basic courses on them and use them then in real-world applications.

Application Security Manager: Developer or Security Officer?

First, an ASM has to understand what a supervised project is about. This is especially important for agile development, where, unlike the waterfall model, you don’t have two months to perform a pre-release review. An АSМ’s job is to make sure that the requirements set at the design stage are correctly interpreted by the team, properly adopted in the architecture, are generally feasible, and will not cause serious technical problems in the future. Typically, the ASM is the main person who reads, interprets, and assesses automated reports and third-party audits. ... Second, an ASM should know about various domains, including development processes and information security principles. Hard skills are also important because it’s very difficult to assess the results provided by narrow specialists and automated tools if you can’t read the code and don’t understand how vulnerabilities can be exploited. When a code analysis or penetration test reveals a critical vulnerability, it’s quite common for developers (who are also committed to creating a secure system) to not accept the results and claim that auditors failed to exploit the vulnerability.?

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