Which is Better for Job Opportunities, AWS or Azure?

 Amazon Web Services (AWS) is a cloud service platform from Amazon, which offers services in various domains such as compute, Object storage, content delivery and other functionality which helps the organizations to scale and grow their business.

Hence, AWS offering services are categorized into 3 types such as Infrastructure as a service (IaaS), software as a service (SaaS) and platform as a service (PaaS). Today it becomes the best cloud platform among currently available cloud platforms.

Azure is a cloud service platform by Microsoft, which offers services in various domains such as compute, storage, database, networking, developer tools and other functionality which help businesses to scale and grow.

Azure services are also broadly categorized as the platform as a service (PaaS), software as a service (SaaS) and Infrastructure as a service (IaaS). This can be used by developers and software employees to create, deploy and manage services and applications through the cloud and emerges as one of the biggest commercial cloud service providers.

There are several cloud computing technologies in the market. You will come across an overwhelming number of cloud service providers, hybrid solutions, products, services, features and more. There are two public cloud behemoths that have captured a majority of the market - Microsoft Azure and Amazon AWS. So, if you are planning to grow your career in public cloud computing, you need to choose between these two providers. Making a choice between these two providers is not an easy job! Both of these companies are leaders in their own rights, so none has an unprecedented edge over the other. It is important to understand both these technologies into detail, take into consideration multiple factors before making a decision. Both Amazon AWS and Microsoft Azure offer similar capabilities revolving around storage, security, and networking.

Amazon AWS is an already established leader in cloud computing, while Azure is catching up at a rapid pace. In terms of popularity, both AWS and Azure are competing on the same level. One core difference is that AWS operates more on an Infrastructure-as-a-Service (IaaS) level while Azure operates more on Platform-as-a-Service (PaaS) level.

It is not surprising when people say that the future is in cloud computing. Taking cloud computing at this stage is a great time to get prepared for this evolving industry. Businesses today are moving away from the traditional methods and approaches, so the role and demand of a cloud computing expert are going to rise in coming times. You can build a long and successful career and embrace the new opportunities arising in cloud computing. Both these cloud computing giants have their set of merits, you need to understand your skills and interests before choosing any one of them. It’s the best time for you to move your career in cloud computing and I would suggest you take a look at Technogeeks Cloud Computing programs by working IT professionals.

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How do I Prepare for Azure Certification?

There are specific examinations that candidates can take to get the certifications directly from Microsoft. These exams have moderate to a very high level of difficulty and require students to prepare thoroughly. Many of the certifications are based on specific roles and tasks related to Azure tools and cloud services. Some of the certifications include Azure Fundamentals, Microsoft Certified: Azure Administrator Associate (AZ:104), Microsoft Certified Azure Developer Associate (AZ:204), Microsoft Azure Architect Technologies (AZ:30), Microsoft Azure Architect Design (AZ-301) and more. Different exams check specific expertise related to cloud management and security for granting the certifications.

Every certification is meant to qualify you for different roles and has different prerequisites and associated exams. Though the preparation technique is similar for most certifications, some certifications require particular attention to certain topics and a modification in preparation techniques.

You can also look at the Microsoft Azure Documentation for getting the base concepts. Especially valuable for beginners, their page contains key insights in fundamental concepts like Agility, Elasticity, Fault Tolerance, High Availability, Scalability and other core features in any cloud network.

It also contains Azure-specific details like computation, security and storage in Azure. Furthermore, it provides a thorough grounding into creating an account in Azure, networking options, application storage, using PowerShell to automate Azure tasks and using the CLI for utilizing Azure services.

After brushing up on the exam fundamentals and acquiring the basics from Microsoft’s own documentation, it is beneficial to join some instructor-led course to obtain further expertise in the topics and for availing the mock-exams such courses usually provide.

Any standard course would provide a thorough grounding in the topics covered in the exam’s syllabus and would also prepare you better by hosting lab sessions where you can hone your practical skills. And I would recommend you to enrol in Technogeeks Microsoft Azure certification training program by working IT professionals.

Another key advantage of instructor-led training is the facility to set up on-demand classroom training. Here, you can arrange classroom training on your convenience such as a live class in person, attending live class remotely, asking questions to the instructor in person and many more.

The Microsoft Azure certification path can be an enriching journey and enable you to pursue a career in the cutting-edge world of the public cloud. With proper preparation and support and with the right instructors, clearing the exams should not be a challenge at all.

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Address:- 3rd Floor, Plot No 7, Common Wealth Society, Opposite Aundh, Pune, Maharashtra- 411007

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Why is Python considered a good language for AI and Machine Learning?

Python has enjoyed a steady rise to fame over recent years and is now jostling for the position of one of the most popular programming languages in the world. Favoured for applications ranging from web development to scripting and process automation, Python is quickly becoming the top choice among developers for artificial intelligence (AI), machine learning, and deep learning projects.

The high-level, robust programming language focuses on rapid application development. The core functionality has helped Python become one of the fastest-growing programming languages. Here are top reasons why developers and companies working on emerging technologies including AI, and ML tend to choose Python.

Simple code structure:

Python focuses on reducing the number of lines of code that you write to execute a function. The language simplifies the code and makes it readable. The combination of Python and AI will help in reducing the size of code that developers write. The language, if used with AI and ML algorithms, will make it easier to reduce the quantity of code.

Vast Community:

Python is well-known for its extremely vast community. Since its release, the community is contributing largely and which is the reason a Python user never feels abandoned with sudden changes. The constant upgrade by the developer community support makes Python one of the most suitable languages for machine learning applications. Large organisations such as Google, Amazon, Facebook, etc. are using this language.

Frameworks & Libraries:

One thing the developers like most about Python is the abundance of open source libraries and frameworks. The language has a great number of machine learning libraries and some of the prominent libraries are such as TensorFlow, Pytorch, Matplotlib, SciKit Learn, etc. Python has a collection as well as code stack of various open-source repositories in almost every domain such as Django for integrating web applications, pandas for machine learning, SciPy for scientific computing, Librosa for audio, OpenCV for images, NumPy for text, etc. Other commonly used libraries for artificial intelligence and machine learning are such as pyDatalog (Logic Programming engine in Python), PyML (the bilateral framework is written in Python that focuses on SVMs and other kernels methods), EasyAI (Simple Python engine for two-players games with AI), PyBrain (simple and effective algorithm for ML tasks), AIMA (Python implementation of algorithms), simple, etc.

If you are interested in learning AI with Python then I would recommend you to enrol in Tehcnogeeks Artificial Intelligence with Python the program under a working IT professional with hands-on projects.

What is The Hot Research Topics in Data Science?

 The hot research topics in data science: -

Artificial Intelligence:

AI has become the mainstream technology for both small and large businesses, and it will bloom in the next few years. At present, we are at the initial stage of using artificial intelligence, but in 2021, we will see more advanced applications of AI in all fields. The reason AI is growing rapidly is that it allows enterprises to improve their overall business processes, and provides a better way of handling both customer and client’s data.

Though utilizing AI will still remain a challenge for many, as exploring the advancement of this technology is not that simple. In 2021, we will find more advanced apps developed with AI, Machine learning, and other technologies that can improve the way we work. Another trend that will take over the market is automated machine learning that will be helpful in transforming data science with better data management. So, you might need specialized training for executing deep learning.

Natural Language Processing:

Natural Language Processing (NLP) has made its way firmly into Data Science after huge breakthroughs in Deep Learning research.

Data Science first began as an analysis of purely raw numbers since this was the easiest way to handle it and collect it in spreadsheets. If you needed to process any kind of text, it would usually need to be categorized or somehow converted into numbers.

Yet it’s quite challenging to compress a paragraph of text into a single number. Natural language and text contain so much rich data and information - we used to be missing out on it since we lacked the ability to represent that information as numbers.

Huge advancements in NLP through Deep Learning are fuelling the full-on integration of NLP into our regular Data Analysis. Neural Networks can now extract information from large bodies of text incredibly quickly. They’re able to classify text into different categories, determine sentiment about a text, and perform analysis on the similarity of text data. In the end, all of that information can be stored in a single feature vector of numbers.

As a result, NLP becomes a powerful tool in Data Science. Huge datastores of text, not just one-word answers but full-on paragraphs, can be transformed into numerical data for standard analysis. We’re now able to explore datasets that are far more complex.

Data Science has become one of the growing fields in all industries, especially the IT industry. Thus, businesses adopting data science techniques and technologies must stay up-to-date with the latest trends. If you are looking to learn data science and want to work on data science trends then I would recommend you to take a look in Technogeeks Data Science program by working IT professionals.

Is it Worth Learning Data Science in 2021?

Yes, it is worth learning, in recent times, Data Science, an emerging technology, has perhaps been the most discussed term in the Information and Technology industry. Data Science, Machine Learning, and Analytics is considered to be among the hottest career paths. The demand for skilled data science practitioners in industry, academia, and the government is rapidly growing. The ongoing “data rush” is therefore attracting so many professionals with diverse backgrounds such as physics, mathematics, statistics, economics, and engineering.

With the amount of data that is being generated and the evolution in the field of Analytics, Data Science has turned out to be a necessity for companies. Today, more and more organizations, ranging from small scale to large enterprises, have realized the importance of Data Science and are implementing its capabilities to successfully scale up their business to new heights. There is an intense lack of more than 1 million Data Scientists in the approach to 2020. Some of the roles associated with Data Science are as follows:

·        Data Scientist

·        Data Engineer

·        Data Analyst

·        Machine Learning Engineer

·        Statistician.

Data science is the amalgamation of your specialized aptitudes, analytical bent of mind and sharp business keenness making it the job role for the exceptionally skilled and driven having enormous growth opportunities.

We live in a digital world, everything is data-driven. There is data science in business, accounting, education, science, engineering, healthcare, technology, energy sector, government, and so on. So, having the ability to work with data is an essential skill.

You will lead a various group of tech talent, business managers, chiefs, and settle on significant level executive decisions that will legitimately influence the fortunes of the organization putting you directly in the driver’s seat.

By learning the foundations of data science, you can increase your odds of getting a job in the data science field. The question to ask yourself is this: When the opportunity in data science comes knocking at your door, will you be ready? If you have been putting data science off, this is the time to start your journey, do not delay.

There are web-based training institutes that give quality training, profoundly experienced mentors, educational programs in a state of harmony with the industry goals, real industry projects, certifications, and job assistance. And I would suggest you take a look in Technogeeks Data Science program which has well-designed courses which are taught by industry experts and provide complete knowledge about this field.

Best Training Courses For Learning RPA, Salesforce, And Data Science

 If you live in Pune and want to learn Robotic Process Automation or RPA, you should find an
institute that offers this course and join the best RPA training in Pune. Top technical training
centres and institutions offer the RPA certification courses to learners and students who want to
gain knowledge on RPA concepts and the tools associated with it. Apart from theory classes, the
training centres also impart real-life practical training to students. The learners get an RPA
certificate on completion of the course and the institutes from where they do the course helps
them in getting a job with a good salary. The top training centres offer the facility of flexible
learning so the students can join the classes as per a schedule that suits them.

Learning Salesforce can be beneficial for students who want a career with a good scope in the
future. The institutes of Salesforce training in Pune teach the basic and advanced concepts of
Salesforce with practical knowledge that helps students after they complete the course and start
working. The Salesforce courses are job oriented and there is no shortage of job opportunities for
people who join this course. The course would benefit both beginners and advanced learners.
Employees of IT companies and people who work in MNCs can also benefit by learning
Salesforce. Working professionals can attend Salesforce classes easily as the training centres
organize separate sessions for them.

Data Science is another popular course that attracts a large number of students. The data science
Pune institutions have a policy of flexible timing. The fee for this course is affordable and most
courses of data science have no special condition on the qualification of a student who wants to
learn the subject. Thus, the course is open to all, and it offers a very good opportunity to know
more about the fascinating topic of data science and develop a career in this field. Joining this
course can be beneficial for automation engineers, robotics engineers, data analysts, hardware
developers, quality analysts, and other professionals whose work involve data science in some or
other way.