The pandemic-caused lockdown has given rise to the second wave of technology reforms for organizations and has proved the need for automation. Most companies do major of their operations through digitized means and automation today. Artificial intelligence, digital aids, IT are important for performance. This shift of platform in the business world has resulted in a demand for individuals with strong technical skills. The work personalities that have digital skills in both existing, and upcoming technologies. Professionals who are proficient in the tools of demand are required to operate the digital face of organizations. DevOps is one such strategic concept that is used to improve the use of IT that has an impact on the services provided by an organization.

In traditional practices the SDLC process was too slow, then DevOps came in between and showed how a faster and continuous production and delivery of software provides a good profit to the organization and became today’s demand in the market. According to studies, organizations that have adopted DevOps, have over 60% improvement in their software development cycles. The DevOps market has already proven to grow faster than any other field in the software development sector. That’s why DevOps is a better carrier right now and in upcoming years.

Basically, DevOps helps to improve the overall efficiency of any program. It provides developers with more control over the production of any product. Most tech companies use it because it provides a far better production infrastructure than other methods. This is exactly why a future in DevOps would mean a career that will be challenging, but equally rewarding.

The DevOps market size keeps increasing day by day. As organizations try to transform their digital presence, they are seeing for professionals to take charge of digitization. As in result, more DevOps engineers are being hired.

Individuals trained in DevOps have many career options such as:

1. DevOps leader
2. Automation Architect
3. Code release manager
4. Software developer/tester
5. Security Engineer

DevOps is a growing technology. Therefore, in the upcoming times, it will only rise. As many stages of software development will depend on it, professionals will begin to move towards it. It is best to start your journey as a DevOps professional with a course that teaches you the basics of DevOps. The best way to rise in this industry is to keep learning and adapting to new technologies.

Yes! DevOps is the best career option. In 2021 and the upcoming year. DevOps will be the best choice for IT professionals as it is in demand and so many companies are adopting the DevOps culture. In India, so many companies have adopted DevOps culture and they can deduct the cost and enhance the software quality and software delivery has been improved. Continuous integration and continuous deployment/ delivery help the Company to reduce the cost, improve software quality, and help to deliver software faster in the market.

DevOps is all about collaboration and communication. The best benefit of working in DevOps is the organizational culture to be working in. It promotes better collaboration between the teams and helps you to develop and sharpen your networking skills to advance your future career paths.

DevOps is a recent skill. This means several professionals who are working in this domain will most likely have upskilled themselves. This will increase your value in the IT industry. Your career is defined by how much you are keen to learn and invest in your career. If you don’t upskill, you will soon be kicked out of demand, as IT technologies and methods are being developed every day. 

DevOps helps you apply a comprehensive approach to building applications. It helps you in Security skills as well in your profile. DevOps skills help bridge the gaps in your profile, which helps you make the value of your’s in the IT domain.

As a DevOps Engineer, there are opportunities for you to work in any company in the IT sector. Start-ups, consultancies, multinational companies, etc., are looking to integrate a DevOps model into their business and create new DevOps Engineer roles to strengthen their current tech teams.

As DevOps continues to transform modern business technology, more specialist senior-level positions are expected to be formed in the next five years, with wide-scale adoption of DevOps anticipated across the industry. It means, more than enough opportunity for internal progression is there as businesses invest in a DevOps function.      

As per the report in 2017, 78% of CIOs and CTOs considered applying DevOps into their organization (a 4% increase in 2016). An additional increase is being expected this year as multiple tech businesses realize the benefits of DevOps Engineers that can work over functions. This is the time for a sustainable carrier. The market has expanded up to 40 to 45 percent over the last five years, increasing the DevOps demand.

Learning DevOps will enable you to learn many more tools and technologies. Companies favor skilled and experienced people with multiple skill sets to improve company costs. If you are a DevOps person, you will be a valuable asset to your company. With DevOps, you will know how to utilize different tools and implement solutions faster and efficiently.

DevOps is a standard career choice now. According to research from Forbes magazine, a DevOps specialist with a basic degree in high school also can earn a middle wage of almost $106,734. Nevertheless, your salary will depend on your role, the average pay for the different roles isn’t completely different. For example, a DevOps Release Manager earns approximately $92K, a Site Reliability Engineer $125K, and a DevOps Engineer $115K. Researches have analyzed, in the upcoming years, DevOps will be the major hiring criteria of the IT companies.

A DevOps career permits you to deploy a broad set of skills that are constantly refined due to the new challenges you solve every day. It’s a constantly fascinating career, encompassing a broad variety of work. Being part of the DevOps process from build to release, you are continuously increasing your understanding of the framework, facing new technologies, and obtaining new knowledge to enable the faster deployment of better quality software.

Having involvement in every element of the DevOps ecosystem provides you as well a broad business understanding as you work to maintain and monitor an effective relationship between operations and development.

DevOps is in high demand, and many companies are incorporating the DevOps process to make their development processes even much faster. There is a high demand for DevOps professionals over various IT domains. In India, a DevOps Engineer earns approximately a salary of INR 7 lakhs per year, according to Glassdoor.com. This statistic changes with experience, skill sets, and the company, you work for.

Training place

I would like to tell you about one of the best places to get trained and certification in DevOps, DevSecOps, and SRE courses is DevOpsSchool. This Platform offers the best trainers who have good experience in DevOps and also they provide a friendly eco-environment where you can learn comfortably and free to ask anything regarding your course and they are always ready to help you out whenever you need, that’s why they provide pdf’s, video, etc. to help you.

They also provide real-time projects to increase your knowledge and to make you tackle the real face of the working environment. It will increase the value of yours as well as your resume. So do check this platform if you guys are looking for any kind of training in any particular course and tools.

The post 15 Solid Reasons Learning DevOps Is Good For Your Career Advancement. appeared first on Artificial Intelligence.

As AI becomes more commonplace in business, organizations that wait for the technology to be perfected risk missing out on its benefits.

Ilike imperfect things. I like my sweater with its holes at the elbows, that painting of mine that my cat walked over while it was drying, that source code I’m using for my doctorate that never seems to execute as I’d expected it to. I like it that way, though. Imperfection makes things more interesting.

When you’re talking about business, however, there’s money to be made — potentially lots of money. And unlike other parts of life, in the business world, a small imperfection might result in millions of dollars in losses. 

That’s scary. What’s even scarier is when these losses happen because engineers make mistakes while trying to implement a new and rapidly evolving technology whose risks nobody fully understands yet and the regulations of which are just getting written. If business leaders are hesitant about this potential minefield, it just proves that they’re human.

I’m talking about AI. However scary it may be, though, many people, including many business leaders, remain incredibly enthusiastic about AI. The potential upsides are huge because AI can finish processes that used to take hours in seconds. That time savings is an improvement of several orders of magnitude. Given such returns, it’s no wonder that companies are pouring billions of dollars into AI every year.

Despite this massive investment, AI uptake is still fitful. The difficulty comes not only from uncertainty regarding the risks and regulations but also the fact that many businesses fail, at least initially, to make a realistic assessment of the types of changes that AI can and can’t bring. 

The post UNHAPPY WITH YOUR AI IMPLEMENTATION? GOOD — YOU SHOULD BE. appeared first on Artificial Intelligence.

IT teams must work with managers who oversee data scientists, data engineers, and analysts to develop points of intervention that complement model ensemble techniques.

Most managers feel euphoria when implementing a technology meant to enhance the workflow of a team or an organization. But they often overlook the details that help implement the technology successfully. The same sentiment can occur for managers who oversee data scientists, data engineers, and analysts examining machine learning initiatives.

Every organization seems to be in love with machine learning. Because love is blind, so to speak, IT teams become the first line of defense in protecting that euphoric feeling. They can start that protection by helping managers appreciate how models fit observations from data sources.  Appreciating the statistical balance in data models is essential for establishing management that minimizes errors that lead to very poor real-world decisions. Overfitting and underfitting is the key part of that discussion. 

Overfitting and underfitting address how training data performance compares to production data performance of a model or machine learning algorithm.  An analyst can see good performance on the training data but experience results that exhibit poor generalization with a new data sample or, even worse, in production.   

So how does all of this work in practice? Overfit means the model treats noise in the training data as a reliable indicator, when in reality the noise distorts. The model creates a poor prediction from any new dataset that does not contain the same or any noise in it — namely the production data. From a statistics standpoint, overfitting occurs if the model or algorithm shows low bias but high variance

Underfit introduces a different model performance issue. Intuitively, underfit implies that the model or the algorithm does not capture all the data well enough to understand the statistical relationships among the data. From a statistics perspective, underfitting occurs if the model or algorithm shows low variance but high bias.

Both model conditions reduce generalizations to poor decisions. Generalizations are the capacity for machine learning models to accurately access unseen data. Getting the right generalization is at the heart of establishing a good machine learning model.

One avenue for analysts is to examine the training data to determine if additional observations are possible to avoid adding unbalanced data sets to models. I explained unbalanced datasets previously in a previous post.   

But there are limits to adding observations or adding features. There are phenomena in which adding more data yields no further performance improvements. One example is called the Hughes phenomenon, which shows that as the number of features increases, a classifying model’s performance increases up to a point of optimal number of features, then decreases performance as more features based on the same size as the training set are added. The Hughes phenomenon should certainly remind data professionals of the curse of dimensionality. The number of possible unique rows grow exponentially for many instances, such as high-dimensional models. The variance increases from the additional observations as well. The result is a model with more opportunities to overfit, making accurate generalization harder to establish and raising development inefficiency.

Thus, the most likely efforts will involve finding a balance between bias and variance. Having low bias and variance is a desired objective but usually is impractical or impossible to achieve.  Analysts should focus on cross-validation techniques, like gradient boosting, to minimize the likelihood of implementing a poor model.

IT teams must work with managers who oversee data scientists, data engineers, and analysts to develop points of intervention that complement model ensemble techniques. The interaction can also lead to forming robust management processes like observability for incident detection and root-cause reporting.  The result is a system that minimizes operational downtime related to data issues. It also produces a process point for managing a balance of bias and variance that protects model accuracy and yield fair outcomes.

Signal noise does not mean that ethics exists in an outcome. Good judgment will make sure ethics in the outcome occur.  Such outcomes are certainly worth a euphoric feeling.

The post When A Good Machine Learning Model Is So Bad appeared first on Artificial Intelligence.

The Federal Statistical Office (FSO) is joining forces with the Swiss Data Science Center, a joint venture between the two federal institutes of technology, to encourage the use of data science and artificial intelligence within the administration.

As society becomes more digitalized, institutions need more and more data science skills. This includes the integration of tools arising from artificial intelligence in a way that is safe and beneficial to society as a whole. To this end, as a pioneer within the federal administration, the FSO is teaming up with the Swiss Data Science Center (SDSC), a joint venture between the two federal institutes of technology.

“I am extremely enthusiastic about this strategic partnership in a joint flagship project between our two institutes and the FSO,” said Martin Vetterli, President of the EPFL. “It reminds me of the partnership between the Alan Turing Institute in London and the British Office for National Statistics, committed to a joint vision of research and innovation in regard to data science.”

Three strands of cooperation

The first strand of cooperation is to strengthen the FSO’s Data Science Competence Center (DSCC), of which the SDSC will become a strategic partner. The EPFL and the ETH Zurich will support the FSO in identifying the necessary profiles and in recruiting the specialists that will form the DSCC. The two institutes also undertake to receive these associates ‘in residence’ at the SDSC laboratories with a view to developing their skills and helping with networking in this field in Switzerland. Lastly, the partners will together identify the DSCC’s priority projects and cooperate in their execution.

The second strand concerns cooperation in regard to infrastructure. The open-source technological platform RENKU, developed by the SDSC will be used by the DSCC. On the academic network SWITCH, RENKU enables the practice of data science and artificial intelligence in a collaborative and reproducible way, criteria essential to the FSO. A dedicated instance was specifically created for the FSO so that the principles of data protection provided for in law can be systematically applied. At no time do the data leave the Swiss national territory.

The third strand aims to promote understanding and the sharing of data science and artificial intelligence knowledge and practices within the federal administration.

Digitalisation and public administration

“With this partnership, the FSO is enabling itself to fulfill the fundamental mission assigned to it by the Federal Council in May 2020. Following the launch of its DSCC on 1 January 2021, it will also be able to meet the challenges brought about by the profound changes in the data ecosystem and by the increased use of algorithms in the public sector,” enthused Georges-Simon Ulrich, Director General of the FSO.

In concrete terms, the DSCC will function as a service provider in the field of data science for the whole of the federal administration. It seeks and encourages cooperation with other levels of government (cantons and communes). The DSCC’s mission is to work at the crossroads between data science and artificial intelligence. Its mission is to build skills allowing the use of methods, techniques and practices arising from data science and artificial intelligence to obtain a new understanding of fields related to public policy and thus to help with decision-making for the public good, in accordance with ethical principles.

The President of the ETH Zurich, Joël Mesot, pointed out the relevance of this partnership. “Putting data science and artificial intelligence at the service of the public good is one of today’s major challenges. The Federal Institutes of Technology are proud to be able to bring their skills into this field and to support the Confederation in this transition so that it will benefit all sectors of society.”

In 2017, the ETH Board launched a national initiative in regard to data science that resulted in the creation of a unique joint venture between the EPFL and the ETH Zurich: the Swiss Data Science Center (SDSC), aimed at promoting innovation in data science, multi-disciplinary research and open science. The SDSC’s mission is to accelerate the use of data science and machine learning techniques within academic disciplines and industry.

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Founder and CEO, Analytics Insight, providing organizations with strategic insights on disruptive technologies. 

Artificial intelligence (AI) is considered one of the most revolutionary developments in human history, and the world has already witnessed its transformative capabilities. Not surprisingly, AI-based innovations are powering some of the most cutting-edge solutions we use in our daily lives.

Today, AI empowers organizations, governments and communities to build a high-performing ecosystem to serve the entire world. Its profound impact on human lives is solving some of the most critical challenges faced by society. Here are a few innovations for social causes that I find most notable. 

Developing New Drugs: The healthcare industry is ripe with disruptive applications of AI, including the discovery and development of new drugs. AI and machine learning have been used to identify potential molecules by leveraging a large volume of data. Pharmaceutical companies use predictive analytics to discover these molecule candidates and optimize them with several rounds of iteration to select the best one for drug manufacturing.

Reporting Sexual Harassment: Artificial intelligence offers new ways of reporting gender-based violence, child sex abuse and more. AI programs are being designed to monitor internal communications, such as corporate documents, emails and chat, for inappropriate content. Various applications and platforms have been developed to help victims share their experiences of sexual harassment and abuse along with the time and location these events took place.

The post Artificial Intelligence For Good: How AI Is Helping Humanity appeared first on Artificial Intelligence.