Nexus is a repository manager functions as stores, organizes, and distributes Artifacts needed for the development.

This tool is one of the finest ways to continuously control binary, packages and build Artifacts. But this repository is not something like a Git repository where you store the source code, this repository is basically for packages, binaries that you build, like apt packages, yum packages, android packages, NPM packages, Docker packages, etc.

There are two types of version one is the community version and 2^nd is the pro version which you called a professional version. The professional version is for paid and the enterprise community version is free with very limited functions.

From Nexus, developers can fully control access to, and deployment of, every Artifact in a company from a single location, making it easier to allocate software.  Internally, you configure your build to issue Artifacts to Nexus, so they come out there to rest developers. Nexus Repository helps organizations build better, safer software even quicker for free. It’s the only repository manager with free support for all popular formats, like Java, Docker, JavaScript, Ruby, Python, NET, GitLFS, etc.

Why Nexus is used for?

Basically, it is repository management and is used to store any kind of packages.  But there is another use of nexus as well, which is the availability of packages.

When you store the packages in the tools like nexus, they will be available to the entire team. Because of this coordination time is reduced.

Now the security of the packages is very important here. Security of packages means only a few persons should be having the right access or few CI tools. Most of the times CI tools will have the right access to make the nexus. But the rest of the people will not have the right access because the security of the package is also a major concern.

Now if you look at the kind of places where we deploy our applications, most of the applications we are using everywhere are cloud-based applications like making my trip, yatra.com, Swiggy, Ola, FB, etc, software as a service, so security is very important.

If one wrong package gets deployed into the system, operations, production, which will create a lot of problems so not only the support security and runtime security is important but package security is also important.  So that’s one of the reasons where we can use nexus where the security can be enforced.

Storage over the period of time will die but if you store the packages in tools like nexus and Artifactory, etc. they will make sure that the packages they store in a sort of object format, they maintain the integrity of packages, so that way it will not get corrupted over the period of time, so that is a thing which will be taking care by tools is nexus. 

What are the benefits of nexus?

Given below are few points on the benefits of nexus are–

• Speedier Builds

• Your own bandwidth savings

• Predictability and Stability

• Ability to deploy 3rd-party Artifacts

• Control and Auditing

• Ability to Host Internal Repositories

• Ability to Host Public Repositories
• Easy administration and management

Who should enroll for Nexus Certification Course?

The person who wants to be a professional on nexus tool and wants to learn what and how repository manager works as nexus and to take the advantage of nexus advanced features who let a user search, use fields, use lookups, and create basic statistical reports and dashboards in the Nexus Intermediate Enterprise or Nexus Intermediate Cloud Platforms.

 It enhances the value of your resume and you can get leading job posts with the help of this certification in leading MNC’s of the world. The certification is only provided after the successful completion of training and practical-based projects.

What are the alternatives of Nexus?

Given below are the few tools which is an alternative to Nexus.

• Archiva
• Nuget gallery
• PopScrap Pro
• ScrapRight
• ScrapYardPro
• RecyclerGuard
• RazorERP
• RecycleManager
• Makor ERP
• Waste Logics
• Waste & Recycling One
• JFrog Artifactory.
• CircleCI.
• Postman.
• GitHub.
• Amazon Elastic Container Registry (ECR)
• Jenkins.
• CloudBees CI.
• Github Package Registry.

What are the pre-requisites to learn Nexus

      For essential nexus training

• Basic understanding of Linux/Unix system concepts
• Familiarity with Command Line Interface (CLI)
• Familiarity with a Text Editor
• Experience with managing systems/applications/infrastructure or with deployments/automation

For intermediate nexus training

• An understanding of basic marketing concepts.
• Should have a keen interest in engaging in social media constructively.
• Enthusiasm and commanding communication skills

For advanced nexus training

• Basic knowledge of Elastic search/Nexus and would like to expand on their knowledge
• Basic Knowledge of JSON
• Basic of Linux and windows

How to learn Nexus?

Through a certified course and training from well-trained trainers and from a popular and well-known platform like DevOpsSchool which makes the trainee is a perfect use of this Nexus tool. 

DevOpsSchool is the best supply of Nexus courses, training, and certification. Their Nexus trainers and specialists are highly educated with a good rich experience in the Software industry. DevOps has captured a stimulating position within the IT world and has become an important part of software engineering methodology. For enhancing the software quality, reducing cost, and increasing the agility for a quicker release, DevOps and Nexus is playing a very important role therefore finding the best Nexus trainers and training seller is very necessary.

The Nexus courses are designed in certain methods where participants can get advantages with a lot of content in less span of time. DevOpsSchool gives its services of Nexus training and courses online and classroom mode in India and abroad. DevOpsSchool tends to run several public classrooms training programs within Bangalore, Hyderabad, Pune, Mumbai, India, Netherlands, etc.

The post Nexus appeared first on Artificial Intelligence.

Digital giants dominate the cloud and ecommerce markets, and part of the reason for their dominance is artificial intelligence and advanced analytics. The good news is that mainstream enterprises can learn from their experiences and employ cutting-edge technologies. 

That’s the word from R. “Ray” Wang who provides a roadmap for AI success in his latest book, Everybody Wants to Rule the World. Wang calls the capability needed to move forward “AI Smart Services” that help automate precision decisions. “To fine-tune precision decisions at scale-that is, to develop decision velocity, [data-driven enterprises] must automate the process of turning signal intelligence into a decision or action. And the way to do this is by creating AI smart services-automated processes powered by AI.” 

The catch is, of course, “AI smart services are not easy to master,” Wang cautions. “They require more than just great algorithms.” He outlines six requirements for advancing in the AI era:

Computing power. To develop AI smart services, data-driven enterprises “must have access to or own cheap computing power,” Wang says. “The ultimate metric for AI rests in pricing not in terms of computing power, but in terms of potential cost per kilowatt hour. The cheapest rate of computing power may determine the cost structure for AI smart services. The most efficient code for finding signal intelligence will provide a cost advantage for each decision made.”

Time. Time waits for no one, Wang says. “The ability to compress time, or take tasks that would normally take weeks and complete them in minutes, provides [data-driven enterprises] an inherent advantage over their competitors.”  However, “AI smart services need more time to identify new patterns. That’s why early adopters who train their AI smart services to process the massive petabytes of data coming in to them gain an advantage. The earlier and the quicker the AI smart services learns, the more precision they put back into their algorithms.” 

Math talent. Algorithms are only as good as the math talent behind it, Wang says. “Success requires hiring digital artisans — those who can balance right brain and left brain expertise. Digital giants typically have armies of data scientists and a brain trust on hand to fine tune AI smart services for their data-driven enterprises.”

Vertical specific expertise. To make precision decisions, AI smart services “must understand nuances of the various verticals in which they operate — such as size of company, industries, and cultural regions.”

Natural user interfaces and user experiences. Data-driven enterprises must develop AI smart services “that engage users in a variety of human computer interfaces that mimic human interaction in terms of their sensory, visualization, voice, and gesture
capabilities. The interfaces might range from chat bots to virtual assistants, and from augmented reality to brain wave mind readers and computer vision.” 

Contextually relevant recommendations. “Once users are confident about how the system arrives at a recommendation, these AI driven smart services start automating decisions-augmenting humanity, accelerating decision making, and ultimately providing filters that deliver situational awareness (the ability to perceive one’s surroundings, events in a timeline, and the potential future state.”

The post Six essential artificial intelligence capabilities leveraged by the digital giants appeared first on Artificial Intelligence.

You may have heard that the field of data science is comparatively new. You may have also heard that there is a high demand for professionals and the trend is likely to continue for the next decade or so. All these are definitely tempting and but are not enough to make an informed decision. As with any other degree, you cannot randomly become a data scientist without knowing the full story. You must judge whether this is the right career path for you and know what all comes after you have graduated.

To ease your process of selection and decision making, this post compiles a set of essential questions that you should get answers to before starting your career in Data Science. These answers will clear the picture for you and help you to take the first step right while joining the top data science courses after 12^th.

What do you do as a data scientist?

A common misconception that goes around is that the work of a data scientist is only about coding. The statement, in fact, is only partially true as the role of a data scientist is yet to be properly defined. Companies hire data scientists under different roles but the core job remains the same: deal with and analyse a massive set of data that will provide value to the company’s business goals.

By analysing data, you will have to solve matters such as how to increase the current productivity, improve product quality and customer satisfaction, and reduce the manufacturing time and so on. To summarise, you will have to increase the business’ revenue using data.

How much coding should you learn?

While data science may not only be about coding, it is definitely an integral part. Coding comes in during the mathematical processes of dealing with large quantities of data that you simply cannot solve manually.

For instance, if a course is teaching you Python, try to learn this language to an expert level. Even if you face any other language during your job, the core principles of using them remain the same. So, you may not know five different languages but you can still land a top job.

Who hires data scientists and what is the pay like?

Data scientists are required everywhere. You can get into fields such as technology, marketing, financial services, corporate setting, government services, healthcare, gaming and so on. In fact, tech companies hire most of the data scientists (41 per cent) followed by marketing companies (almost 13 per cent). To give you a clearer idea, Google, Facebook, Amazon and Flipkart hire data scientists on a regular basis.

As the demand is high and the number of professionals available is low, the pay in this field is generally high. You can easily expect a six-figure annual income once you graduate and that can easily go up to seven within a matter of two years. Depending on your skills, you can get any amount you want.

What does the future look like?

Data science has already been named the top job in America for the year 2016. Statistics also suggest that during the decade 2014-2024, the field has an expected growth rate of 11-14 per cent. Also, almost 80 per cent of the data scientists out there suggest that there is indeed a shortage of professionals in this current field. All these establishes the fact that data science is, in fact, the next big career option.

But the field is also prone to automation as most of the task is ultimately done by machines. Algorithms can run bulk quantities of data through tools and produce faster results than any human possibly can. However, this does not mean that machines will replace data scientists totally. The courses will teach you to understand the automatic algorithms, better their technology and use your creativity to invent new techniques. If you learn to find optimal solutions for a problem at hand, you will never be searching for jobs.

To conclude

If you are fresh out of the 12^th standard now, there cannot be any proper time to start your career in data science. Both new and old companies are starting to realise the importance of this field and investing without limits on the right professionals. Even universities have branches who recruit scientists to help firms with their data or develop tools to process things better and faster.

Seize the opportunity, select the right institution and kick start your career. You will be dealing with real-world data sets and the scope for growth is limitless. Data science is indeed the next big thing.

The post Data Science: Essential Questions You Need To Answer Before Starting Your Career appeared first on Artificial Intelligence.

You may have heard that the field of data science is comparatively new. You may have also heard that there is a high demand for professionals and the trend is likely to continue for the next decade or so. All these are definitely tempting and but are not enough to make an informed decision. As with any other degree, you cannot randomly become a data scientist without knowing the full story. You must judge whether this is the right career path for you and know what all comes after you have graduated.

To ease your process of selection and decision making, this post compiles a set of essential questions that you should get answers to before starting your career in Data Science. These answers will clear the picture for you and help you to take the first step right while joining the top data science courses after 12^th.

What do you do as a data scientist?

A common misconception that goes around is that the work of a data scientist is only about coding. The statement, in fact, is only partially true as the role of a data scientist is yet to be properly defined. Companies hire data scientists under different roles but the core job remains the same: deal with and analyse a massive set of data that will provide value to the company’s business goals.

By analysing data, you will have to solve matters such as how to increase the current productivity, improve product quality and customer satisfaction, and reduce the manufacturing time and so on. To summarise, you will have to increase the business’ revenue using data.

How much coding should you learn?

While data science may not only be about coding, it is definitely an integral part. Coding comes in during the mathematical processes of dealing with large quantities of data that you simply cannot solve manually.

For instance, if a course is teaching you Python, try to learn this language to an expert level. Even if you face any other language during your job, the core principles of using them remain the same. So, you may not know five different languages but you can still land a top job.

Who hires data scientists and what is the pay like?

Data scientists are required everywhere. You can get into fields such as technology, marketing, financial services, corporate setting, government services, healthcare, gaming and so on. In fact, tech companies hire most of the data scientists (41 per cent) followed by marketing companies (almost 13 per cent). To give you a clearer idea, Google, Facebook, Amazon and Flipkart hire data scientists on a regular basis.

As the demand is high and the number of professionals available is low, the pay in this field is generally high. You can easily expect a six-figure annual income once you graduate and that can easily go up to seven within a matter of two years. Depending on your skills, you can get any amount you want.

What does the future look like?

Data science has already been named the top job in America for the year 2016. Statistics also suggest that during the decade 2014-2024, the field has an expected growth rate of 11-14 per cent. Also, almost 80 per cent of the data scientists out there suggest that there is indeed a shortage of professionals in this current field. All these establishes the fact that data science is, in fact, the next big career option.

But the field is also prone to automation as most of the task is ultimately done by machines. Algorithms can run bulk quantities of data through tools and produce faster results than any human possibly can. However, this does not mean that machines will replace data scientists totally. The courses will teach you to understand the automatic algorithms, better their technology and use your creativity to invent new techniques. If you learn to find optimal solutions for a problem at hand, you will never be searching for jobs.

To conclude

If you are fresh out of the 12^th standard now, there cannot be any proper time to start your career in data science. Both new and old companies are starting to realise the importance of this field and investing without limits on the right professionals. Even universities have branches who recruit scientists to help firms with their data or develop tools to process things better and faster.

Seize the opportunity, select the right institution and kick start your career. You will be dealing with real-world data sets and the scope for growth is limitless. Data science is indeed the next big thing.

The post Data Science: Essential Questions You Need To Answer Before Starting Your Career appeared first on Artificial Intelligence.