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Benefits of becoming a DevOps Engineer

Professionals who are working in the technical field and the IT industry must know about the DevOps revolution. DevOps is simplifying the automation trend in business organizations. There has always been a disconnect between software developers, IT teams, operations, and product teams in an organization using traditional software development models. DevOps is used to close this gap and smoothen the workflow.

Benefits of becoming a DevOps Engineer

The word DevOps consists of two terms, “Development” and “Operations” which signifies the main purpose of this practice. In the past, the development teams and operations teams faced difficulty working together because of the work differences. The Devs focused on the dexterity of the software application process, whereas Ops worked on its stability. Software development comprises various manual activities like coding, testing, and monitoring. The DevOps methodology brings in a healthy collaboration between these two by automating the manual processes and saving a lot of time. 

DevOps methodology improves the software quality being delivered to the customers. This also enables better communication and feedback ultimately giving a better profit. Every business and organization comprises IT and software because DevOps is needed in all kinds of organizations. Becoming an expert in DevOps, its agile methodology, and software architecture can help gain a lot of benefits. Many companies are moving to the DevOps culture to gain faster continuous delivery increasing the demand for skilled DevOps professionals.

Here are some of the reasons for the increased adoption of DevOps methodology:

  • Reduced rate of failure
  • Reduction in error solving time
  • Increased use of Microservice architecture

DevOps Engineer: Demand, roles, and responsibilities

A DevOps engineer should have soft and hard skills to overcome the barriers coming in between software development and operations teams during the code release process. This will speed up the workflow and produce quality releases. 

There is a growing demand for highly skilled DevOps professionals. Research has shown that there will be up to 30 times more deployments in this field as compared to 2017. Adobe was able to meet 60% more app development demand after applying DevOps methodology. With this methodology, an organization can achieve better customer satisfaction. 

DevOps engineer roles require professionals to have soft as well as technical skills. Coding sections cover the “Dev” part of DevOps. But to be a successful DevOps engineer Ops skills are required. 

Some of the operational skills are listed below:

  • Project management skills
  • Understanding of process reengineering
  • Knowledge of IT systems and production environment
  • Knowledge of software development tools and techniques
  • Experience with data management and operation in the production environment 

These skills help in reducing complexity in product formation, minimizing delays in distribution, and ensuring better integration between various platforms and operating systems.

The average annual salary earned by a lead Development Operations (DevOps) Engineer is USD 137,000 and USD 180,000. Market analysis shows that 81% of the enterprises are using DevOps practices while 70% of small to medium businesses are using this methodology. Approximately 80% of the organizations are paying more than USD 90,000 as a starting salary. On the other hand, 35% pay at least USD 115,000, and 17% pay more than USD 125,000.

Becoming a DevOps Engineer

If you want to become a DevOps engineer then there are more than one career paths for it. A software developer interested in network operations and product deployment can move to this field. A systems administrator can become a software developer by learning some skills and can help in improving testing and deployment. DevOps is going to rule the market in the coming years. Almost all kinds of industries are adopting or planning to adopt this methodology. You can gain skills and enhance your career prospects in this industry. 

You can get yourself enrolled in uCertify’s DevOps training course. Our course has the best learning material that will help you with the skills required for starting your career in this field. The DevOps training course focuses on the objectives covered in both DevOps fundamentals and leadership certification exams. The DevOps certification course covers the most important and critical aspects of the DevOps methodology. The course provides an in-depth knowledge of DevOps and its benefits. The DevOps certification course also provides knowledge on DevOps and its impact on the software development cycle to help you prepare for your first DevOps engineer jobs.

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Start your career in AI and Machine Learning

What are things that strike your mind when it comes to Artificial Intelligence? Digital assistants, chatbots, robots, and self-driving cars would be among the first few things, right? These are some most compelling and intriguing examples of artificial intelligence. 

Various technologies are growing constantly with the market but technologies like deep learning and machine learning are going to take over the market completely. Concurrently, some technologists believe that AI will be the biggest revolution of this age. In the past, we have seen some phenomenal AI updates but many AI initiatives are yet to be introduced. AI is transforming our way of working and living. 

Start your career in AI and Machine Learning

The AI Outlook

AI is getting more and more attraction these days and that is because of the recent innovations that took place. The growing adoption of this technology across industries is also creating a buzz among the professionals. As the industries are moving to this technology the organizations are also looking for trained professionals in this field to do the job. Researches have made speculations that AI will create approximately 2.3 million jobs by the end of the year. On the other hand, surveys also reported that because of this technology over 1.7 million jobs will be lost. But we can’t deny that AI is offering many career opportunities for professionals. AI is used in almost every industry, whether it is entertainment or transportation. This is the reason that the demand for skilled professionals for this industry is increasing.AI is getting more and more attraction these days and that is because of the recent innovations that took place. The growing adoption of this technology across industries is also creating a buzz among the professionals. As the industries are moving to this technology the organizations are also looking for trained professionals in this field to do the job. Researches have made speculations that AI will create approximately 2.3 million jobs by the end of the year. On the other hand, surveys also reported that because of this technology over 1.7 million jobs will be lost. But we can’t deny that AI is offering many career opportunities for professionals. AI is used in almost every industry, whether it is entertainment or transportation. This is the reason that the demand for skilled professionals for this industry is increasing.

AI and Machine Learning

If you’re new to this industry, you might just want to know what Artificial Intelligence is? AI helps in making intelligent machines. This software mimics the human way of learning and learns in the same way a human will learn. This can take over some of our jobs to finish them in a better and faster way than a human can do. Machine learning is a subspace of AI, machine learning is the process by which AI learns. Therefore, sometimes when we are describing AI, we are also describing machine learning.

With machine learning, algorithms use data to enable computers to learn to perform tasks even if they are not programmed to do. Machine learning provides us with technology to amplify our human efficiency.

AI has numerous benefits in almost all industries. Professionals are also utilizing Ai for improving their work. Customers use AI in navigation and ride-sharing apps, in smart home devices, in personal assistants, and more. AI also helps in assessing risk and defining the opportunity, cutting costs, and boosting research and innovation with the help of AI.

Industries using AI

AI is being used in various types of applications in almost all kinds of industries. The self-driving car is one of the prominent examples where AI has been used. Predictive maintenance is a part of AI, this process anticipates the points where a machine component may fail. Predicting these points helps in replacing the components before its failure. This results in minimizing the downtime and increasing the component lifetime. Smart cities are becoming more energy-efficient, reducing crime, and improving its safety with the help of AI. AI applications are countless, and the numbers are growing all the time.  

Many big brands like IBM, Amazon, Microsoft, and Accenture are using AI. These systems use machine learning on a large scale to perform the operations. In the future, all the industries will be using AI and machine learning to achieve benefits and growth in the job market. All types of industries including transportation, manufacturing, energy, farming, and finance are moving towards this technology.

Starting a career in AI and machine learning

If you’re interested in starting your career in this field and don’t know where to start then we are here. There are different paths for different professionals first are the beginners, second are programmers, and third are the professionals already working in data science. It is also known that different industries require different skill sets. One common skill that a person working in AI should have is excellent communication skills. It is more prominent than in maths and computing skills.  

For beginners, starting with mathematics and learning machine learning would be good choices. If you want to move into AI then you should have strong computer skills and programming skills like C++ along with an understanding of the algorithms. You should also have hands-on general business knowledge.

You can easily start working with algorithms and coding if you’re already a programmer moving into AI. 

For a data analyst or scientist getting into AI, it is important to learn programming skills. To bypass these challenges you should know how to prepare, model building, and visualizing the data. 

Future of AI and machine learning

Predicting the future is not very easy as the technology is changing every single day. There are tremendous changes going to occur in the coming years that we are unaware of but this is certain that the skilled AI and machine learning professionals are less in numbers. This gap can only be reduced with the trained professionals filling millions of AI jobs. If you want to get one of these jobs, you will have to start your training for the certification exam right away. If you start your training at the right time and get certified then your chances of working in this field will increase. As the demand for AI and machine learning professionals are increasing, organizations are in the need of professionals with great knowledge of these growing technologies and hands-on experience. If you’re interested in becoming an AI expert then we have the artificial intelligence certification course. The Artificial Intelligence course will give you the knowledge and skills required to work with the most trending technologies. This course has the best AI learning resource to become a successful AI expert.

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Become a Systems Security Certified Practitioner with uCertify's SSCP study guide

Are you planning a career in the field of cybersecurity? Because cybersecurity threats are common and a concern for most businesses, this decision can create a great number of job opportunities. There are various cybersecurity certifications you can earn to validate your expertise because the need for highly-skilled security personnel is increasing everyday. The (ISC)² Systems Security Certified Practitioner (SSCP) and CompTIA Security+ certifications can be used by professionals as a stepping stone to the much more difficult CISSP certification. This certification is more technical than the CISSP certification and can add value to your resume.

Become a Systems Security Certified Practitioner with uCertify

uCertify brings you the Systems Security Certified Practitioner (SSCP) course to help you prepare for the SSCP cert exam. The course offers complete coverage of all the domains of (ISC)² SSCP exam and includes topics such as information security fundamentals, integrated risk management and mitigation, communications and network security, identity and access control, cryptography, hardware and systems security, and so on. You will get Test Prep and PrepEngine in the course to ensure that you get to practice under exam conditions. The course delivers hands-on training of the real world through labs. These labs allow you to experiment in a risk free environment and apply what you have learned in chapters and lessons to real-life situations. 

About SSCP Exam

The (ISC)² SSCP certification exam is an entry-level security exam offered by International Information Systems Security Certification Consortium, Inc., or (ISC)². It is considered as a stepping stone on the path of earning the (ISC)² Certified Information Systems Security Practitioner (CISSP). The SSCP certification is ideal for those with proven technical skills and practical security knowledge in hands-on operational IT roles. The (ISC)² SSCP certification offers career prospects in the fields such as:

  • Security Analyst
  • Systems Engineer
  • Security Administrator
  • Database Administrator

So, start your prep for the (ISC)² SSCP certification with the uCertify SSCP course and labs!

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Data Analytics vs Data Science: An Overview

Data analysts and data scientists both work with data, the difference is in the work that they do with it. Data analysts inspect sets of data and use it to identify trends, develop charts, and create visual presentations. With the help of the results provided by Data analysts, businesses can make more strategic decisions. On the other hand, Data scientists create and formulate new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analysis.

Operating Data Analytics

The job role of data analysts varies across industries, however, data analysts usually use data to understand and solve problems. Data analysts work on a wide range of fields including business, finance, advertising, operations, pricing, and international strategy analysis. Data analysts evaluate well-defined sets of data using different tools to answer business demands. This knowledge will help find answers to the questions like why sales decreased this quarter, why a marketing campaign didn’t do well in all regions, what internal factors affecting revenue, and more. 

Data analysts have skills including data mining, data modeling, R or SAS, SQL, and database management and reporting.

Data analysts usually design and maintain data systems and databases with the help of statistical tools to depict data sets. They are also responsible for preparing reports that reveal movements, patterns, and predictions based on useful results.

Operating Data Science

Data scientists measure the required or undiscovered results by asking questions, creating algorithms, and statistical models. Unlike Data analysts, Data scientists do not deal with heavy coding. Data scientists work with limitless data sets with multiple tools and build their automation systems and frameworks. 

A Data scientist should have practical and working knowledge of machine learning, software development, Hadoop, Java, data warehouse, python, object-oriented programming, and more.

Data scientists are mostly responsible for designing data modeling processes, as well as creating algorithms and predictive models. These models and algorithms help in extracting required information and solving complex problems.

Data Science or Data Analytics: which one to choose?

You can access to choose the right path for you after understanding the differences between data analytics and data science and identifying each career’s requirement. There are three key factors you should consider before deciding which career path is the best-suited choice for your professional goals.

1. Choosing the right education background

Though there are many similarities in the work of Data Scientists and Data analysts, you need different educational backgrounds for each. 

Data analysts examine large data sets to identify trends that help businesses make important decisions. Pursuing an undergraduate degree in a science, technology, engineering, or math (STEM) major can provide the required skills to perform these tasks. Some professionals prefer to have an advanced degree in analytics or a related field. Professional experience in programming, databases, modeling, and predictive analytics is also suited for this job.

Data scientists are responsible for designing and constructing new processes for data modeling and production. In this process, they use data mining and machine learning. That’s why a master’s in data science is considered as an essential education prerequisite for professional advancement.

If you’re a student and thinking about which stream you should choose then you may find a data analytics role more attractive. Employers prefer candidates with an undergraduate degree for these positions. But if you have decided to devote yourself to an advanced degree then you would like to go for Data scientists’ roles. 

2. Choosing your area of interest

If you love working with numbers and statistics from or are interested in computer science and business, then these jobs are right for you.

Data analysts deal with numbers, statistics, and programming so they love these fields. A Data analyst has to work in databases to discover data points from complex sources because they work as the protector of an organization. They should also understand the work culture and nature of the industry they work in.

Data scientists are required to understand math, statistics, and computer science and have knowledge of the business world. If this characterization matches your educational background and experience then a data scientist role is the right pick for you. 

In both cases, you should have an understanding of which career matches your interests. This will help you select what kind of work you’ll enjoy. 

3. Choosing your desired salary and career path

The pay scale of data analysts and data scientists’ roles varies with the level of experience. 

Data analysts’ pay scale varies between USD 83,750 and USD 142,500. They mainly have to deal with the databases, but their salaries can be increased by learning additional programming skills, such as R and Python. Researches have shown that data analysts with more than 10 years of experience earn more money and receive promotions.

Data scientists with a graduate degree are generally more experienced and are considered more superior to data analysts. That’s why they are better paid. According to reports, data scientists receive an annual salary between USD 105,750 and USD 180,250. Data scientists achieve more promotions as well. They can advance to senior roles such as data architect or data engineer.

Data analysts and data scientists differ in responsibilities, educational requirements, and career trajectory. A qualified professional for data-focused careers are highly demanded these days because of businesses increased reliability on data.
So, now when you know about all the deciding factors for choosing between data scientists and data analysts, you will be able to decide which career is the right fit for you. uCertify will help you with the next step, gaining the correct skills to become a data scientist or analyst. Our comprehensive courses and other learning resources will teach you everything you need to know. So, start learning with uCertify today!

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Check out uCertify’s PeopleCert DevOps Certification Prep

The term DevOps is used to describe an evolution of existing IT best practices from ITIL, Lean, and Agile into a development and operations approach. It supports automation and continuous delivery, and encourages a culture of collaboration and learning to help IT deliver business value better, faster, and cheaper than ever before. uCertify offers PeopleCert DevOps certification prep for IT professionals. It provides complete coverage of the certification exam objectives and provides expertise in the following topics:

  • Full Stack
  • DevOps Leadership
  • DevOps Principles & Concepts
  • Engaging and Implementing the DevOps Full Stack
  • Planning and Approaching the DevOps Transformation
  • Validating Results & Sustaining the DevOps Transformation
  • Clarifying and Aligning the DevOps Conversion to Value Delivery
Check out uCertify’s PeopleCert DevOps Certification Prep

uCertify’s DevOps certification prep comes with exam based practice tests that help to prepare for the certification in the simulated test environment. The practice test is a simple, cloud-based solution that can be used effortlessly to prepare for certification exams. In addition to the practice test, uCertify provides PrepEngine, a gamified version of TestPrep. PrepEngine helps students both retain and recall better by using randomization, mastery, and spaced learning. It is provided at no additional cost.

About PeopleCert DevOps exam

The DevOps certifications offered by PeopleCert are hands-on and validates candidates’ knowledge in DevOps processes, tools, and culture implementation. This certification has been designed for professionals and leaders in software and application development, IT department, as well as business analysts, project and product managers, or anyone who wants to gain knowledge of DevOps principles.

To be a PeopleCert DevOps certified professional, you need to pass the following exams:

PeopleCert DevOps Fundamentals: This certification provides expertise to work in a DevOps environment using a full-stack approach.

PeopleCert DevOps Leadership: This certification provides expertise in principles and tools necessary to sustain a DevOps cultural shift.

The DevOps model enables your organization to move at a high velocity so you can innovate for customers faster, adapt to changing markets better, and grow more efficiently at driving business results. It helps to increase the frequency and pace of releases so you can innovate and improve your product faster. It also ensures the application updates quality and infrastructure changes so you can reliably deliver at a rapid pace while maintaining a positive experience for the end-users. You can operate and manage your infrastructure and development processes at scale and build more effective teams under a DevOps cultural model, which emphasizes values such as ownership and accountability.

So, start learning and prepare for the DevOps certification today with uCertify!