Features of Selenium:

• Multi-Browser Compatibility.
• Multiple Language Support.
• Speed and Performance.
• Handling of Dynamic Web Elements.
• Easy to Identify and use web elements.

What is Java?

Java is a programming language and computing platform first released by Sun Microsystems in 1995.
It is a high-level, class-based, object-oriented programming language designed to have as few implementation dependencies as possible.

Features of Java:

• Java is platform-independent
• Automatic Memory location
• Robust
• Multithreaded
• Object-oriented
• Secured
• Distributed

Prerequisites to learn Selenium and Java:

• Should have a basic understanding of Java or any other object-oriented programming language.
• Basic Knowledge of knowledge C#, Java, Perl, PHP, Python, and Ruby.
• Should have knowledge about source code management tools like GIT and SVN.
• Good knowledge of Selenium Webdriver for web testing.
• Good knowledge of Selenium Webdriver for web testing.Web Technologies like HTML, CSS, XML, XPATH, DOM and JavaScript.

How Long Does it Take to Learn Selenium and Java?

Typically, Selenium and Java training by DevOpsSchool will let you master the concepts in about 2 months if you dedicate 2 to 4 hours per week. The training takes place in such a way that you become proficient in almost all concepts of Selenium and Java, can easily add certification to your resume.

How to Learn Selenium and Java?

The first thing you should do before learning Selenium and Java is to ask yourself how you learn best.

Do you prefer online tutorials, or do you prefer online courses? Do you want to work on projects as part of a team, or are you comfortable learning at your own pace? By considering this question in advance, you will be able to better understand what kind of material you should be looking for.

If you are planning to learn Selenium and Java, go with DevOpsSchool institute. Will provide you with online and classroom training and certification course Programs by an expert. To get in-depth knowledge of Selenium and Java along with its various applications, check out our interactive, live-online training.

Here you can see the Agenda of the Selenium and Java Course:

This course covers the basics of Selenium and Java, their main features. You will develop a critical understanding of why Prometheus and Grafana are useful and how they can be combined with other web development frameworks.

However, if you want to find out more details, be sure to check out our Prometheus and Grafana course to learn more about this technology.

The post What is Selenium, Java, and How to learn Online? appeared first on Artificial Intelligence.

Software architecture, Java and machine learning are amongst the top programming skills ranked by software engineers as important to their role in 2021, according to new research from Degreed. 

Degreed global user data from February 2020 – February 2021 uncovered the skills ranked as most important in a role, for software engineers, product managers, and business analysts. The data highlights the transferability of skills in certain technology and product management roles both internally and externally. 

Data was analysed from millions of active users in the U.S. UK, LATAM, Germany, France, the Netherlands, India, APAC, and Australia, and New Zealand – in companies that include Capgemini, Visa, Ford, Mastercard, and Unilever.

For software engineers, the top 10 skills they rank as vital to doing their work well are Java, Python, Programming, Software Architecture, Machine Learning, Software Engineering, Artificial Intelligence, Software Testing, SQL, and Linux.

Programming languages are most popular across all software engineering roles regardless of sector. The order of the top 10 changes in different sectors but the skills listed remain the same. In finance, for example, more focus is placed on Java, while in media/telecoms the priority is Python.

The top 10 skills most frequently cited as important by product managers were:

• Product Management
• Product Strategy
• Leadership
• Product Planning
• New Product Development
• Product Lifecycle Management
• Project Management
• Product Marketing
• Strategic Partnerships
• Go-to-market Strategy

Across multiple industries, the top 10 list remains consistent (particularly within finance, manufacturing, and media/telecom).

The top 10 skills ranked by business analysts are:

• Business Analysis
• Microsoft Excel
• Data Analytics/Analysis
• Project Management
• Business Intelligence
• Agile
• Project Planning
• Agile Development
• Reporting
• Software Testing

“What’s compelling here is that the top 10 lists for each role do not change significantly as we look at what skills workers find crucial to their work, no matter their sector,” says David Kuntz, head of data science at Degreed.

“A product manager in finance will broadly require the same skill set as a product manager in manufacturing. This means that the market for talented product managers, software engineers, and business analysts is not limited to an employers own sector or to a single department,” he says. 

“Employers should look further afield when struggling to find the right talent as many of the skills within roles are transferable across industries and business functions,” Kuntz says.

“They can look broadly for candidates, both internally and externally. This is particularly relevant for in-demand roles such as software engineering, where there is a well-known talent shortage.”

Kuntz says it is vital to regularly look at the skills people say that they need to effectively do their work, as this ties into their productivity and quality of work. 

“Especially as organisations look towards recovery post-pandemic,” he says.

The research found more than four in ten workers say that a lack of confidence in their skills means tasks take longer to complete and 22% say that their work is of a lower quality. 

“Spotting skills gaps early will help employers proactively upskill their workers to prevent this,” Kuntz adds.

The post Java, machine learning, AI amongst most important programming skills – report appeared first on Artificial Intelligence.

Java devs looking to explore or work in data science may need another language up their sleeves. Python and R are common Java alternatives for data science.

Java can handle large workloads, and even if it hits limitations, peripheral JVM languages such as Scala and Kotlin can pick up the slack. But in the world of data science, Java isn’t always the go-to platform.

The front end of data science has recently been dominated by the languages Python and R, says Vivek Ravisankar, CEO and co-founder of HackerRank, a developer skills platform. “Python and R are both open source and free to use, giving them both a rich ecosystem and plenty of support from academic communities.”

Java developers who plan to explore data science calculation may do well to learn a little Python and R.

R and Python basics

The R programming language has implicit benefits when it comes to data science. Developed by statisticians for statisticians, R was designed to make data analysis and statistics easier to do, said Maria Khalusova, developer advocate at JetBrains. R has a number of unique statistics packages, and its matrix calculation capabilities are quite strong compared to Java.

R is often praised for its rich ecosystem, specifically around data visualization and specialized statistical methods. It is popular among folks who started their careers in statistics and advanced analytics. R is a specialized language, however, and it has limitations.

As a general-purpose language, Python has an advantage over R, Khalusova said.

Python is more production-friendly, and it’s easier to learn — both for beginners and those who switch to it from other programming languages. That accessibility may be why Python has been able to grow its rich data science ecosystem so rapidly.

Python supports a number of advanced machine learning libraries and frameworks, such as scikit-learn and TensorFlow. Python is also backed by the mature SciPy stack, which includes NumPy, SciPy, Matplotlib and pandas. This makes it well-equipped for numerical and technical computing. Its appeal for data science is how quickly developers can get started with Python. “For data science experts looking to start writing application code, this is the most straightforward route,” said Simon Ritter, deputy CTO of Azul Systems, which develops Java runtimes.

Java’s role

While Java, as well as Kotlin and Scala, can be used for data science, it’s more likely to play a role behind the scenes, Ravisankar says.

“Java is not built for data science — most Java applications were built for web servers and large-scale distributed applications,” Ravisankar says. “Java is statically typed and strictly follows the object-oriented paradigm.”

In contrast, Python follows a multiprogramming paradigm, which makes it easy for developers to write concise code using syntactic sugar. Python was not built specifically for data science workloads, but it does include many features that make it easy to code against data science workloads such as read-eval-print loops, notebooks and math libraries.

The community and tools around Python and R have continued to grow, further cementing their lead in data science coding.

The post Why Java devs should switch to Python or R for data science appeared first on Artificial Intelligence.

Índice terá uma série de novos recursos que vai aprimorar classificações futuras

O índice Tiobe não apresentou grandes mudanças no mês de março, em relação a fevereiro, com os cinco primeiros lugares inalterados e a linguagem de programação C ainda na primeira posição. Com isso, a empresa aproveitou o relatório para anunciar novos recursos que serão adicionados ao índice em um futuro próximo.

O índice da comunidade de programação Tiobe é um indicador mensal da popularidade das linguagens de programação, baseadas no número de engenheiros qualificados em todo o mundo, cursos e fornecedores terceirizados. Motores de busca populares como Google, Bing, Yahoo !, Wikipedia, Amazon, YouTube e Baidu são usados ​​para calcular as classificações.

Assim como em fevereiro, a linguagem de programação C continua na primeira posição com 15,33%, enquanto o Java ostenta uma classificação de 10,45% para superar o Python em terceiro lugar (10,31%). As linguagens C++ e C# completam os cinco primeiros do ranking com classificações de 6,52% e 4,97%, respectivamente.

A Visual Basic conseguiu uma classificação de 4,85% ocupando o sexto lugar, sobre o JavaScript em sétimo (2,11%) e o PHP em oitavo (2,07%). Em comparação com fevereiro, as únicas mudanças ocorrem na parte inferior das dez primeiras posições. Nas últimas classificações, a linguagem Assembly (1,97%) subiu para obter o nono lugar do SQL (1,87%), que agora ocupa o décimo lugar.

Na relação anual, comparado com março de 2020, a linguagem Classic Visual Basic saltou da 18ª posição para a 12ª na lista mais recente. Durante este período, Delphi/Object Pascal subiu da 20ª para a 14ª posição, enquanto a linguagem de programação Groovy subiu do 36º lugar para o 15º. Entre março de 2020 e março de 2021, MATLAB caiu três pontos, caindo da 18ª para a 15ª posição no ranking. Durante o mesmo período, Swift deslizou do 13º para o 19º lugar.

Mudanças no índice

A empresa ressaltou a correção de um bug no índice, “devido ao algoritmo complexo usado, como a eliminação automática de outliers”. A soma de todas as porcentagens de todas as linguagens de programação nem sempre chegava a 100%, informou Paul Jansen, CEO da Tiobe Software, no relatório mensal do índice. Ele garante que a empresa corrigirá o bug o mais rápido possível.

Depois disso, a Tiobe irá disponibilizar as propriedades da linguagem de programação. “As propriedades que serão rastreadas são retiradas da Wikipedia. Exemplos são os paradigmas de linguagem (orientados a objetos, funcionais, e etc.) e sistemas de tipos (tipados estaticamente, tipados dinamicamente, tipados fracamente, e etc.). Também adicionaremos tendências para ver quais propriedades estão ganhando popularidade. A terceira mudança que faremos é adicionar logotipos legais aos idiomas para tornar as tabelas de idiomas um pouco mais atraentes”, disse Jansen.

The post Java supera Python na classificação de linguagem de programação da Tiobe appeared first on Artificial Intelligence.

The world is drooling over Artificial Intelligence. From research institutions to corporate houses, every organization aims to create AI-driven systems to build their enterprise. Machine Learning, or more commonly known as ML, is a sub-array of AI. With ML, you can teach the machines to behave like humans, i.e. develop brains in a machine. The result is automated machines that know-how and what is to be done. One commonly used place for AI & ML is Maps. Have you noticed that it shows you the route with the least traffic and the best route? That happens through ML along with other technologies.

Another hot thing in the technological sphere is Big Data and its management. Big data is a terminology utilized for data of all types. It incorporates structured, semi-structured, and unstructured data. Be it any type of organization, you will always have a lot of data related to operations, finance, marketing, manufacturing, sales, etc.

How you utilize and manage this data is the work of data scientists. Machines absorb the information that is further utilized and adopted in AI is all related to Big Data. Hence, to dive into AI, you will have to be accustomed to ML and Big data. Data science, ML, big data, and AI are all interlinked and synchronized.

If you are talking about turning a machine like a human, it requires you to feed it in the language that it understands. Yes, we are talking, i.e. programming languages. Some of the commonly practiced languages for ML and Decision science are Python, Java, etc. But Java is a language that one must never forget. If you know Java Outsourcing Company, you can hop on the bandwagon of ML with great ease. How will it happen? Read along to learn more.

Top Expertise to Develop For Machine Learning & Data Science

If you want to excel in any field, you first need to develop the skills. Here’s a list of all the skills required if you’re going to learn ML & data science.

Math: It is all about permutations and combination complemented with your calculation ability to be able to link yourself with machines.

Data Architecture: To be able to reach the core of any technology, you must have a broad idea of the data formats.

Software Structures: There is no ML without software, and a data engineer should be clear with concepts related to software and their working.

Programming & Languages: If you do not know anything about this, there is no ML for you. Programming languages are the essential requirement for one to be able to build a career in ML.

Differencing and Data Mining: If you have no clue about data, you are a zero. To be able to learn ML, data mining, and the ability to infer the information is crucial.

Java: Machine Learning & Data Science’s Future

Java is a technology that proves beneficial in varied arrays of development and ML. One of the critical things in ML & Data Science is algorithms. With Java’s available resources, one can efficiently work in various algorithms and even develop them.

It is a scalable language with many frameworks and libraries. In the current scenario, Java is amongst the most prominent languages in AI and ML. Some of the reasons why Java is an excellent alternative for a future in Data Science, Machine Learning, and finally, Artificial Intelligence are:

Pace of Execution

If you are arguing about the speed of coding and execution, Java takes the lead in it, which means faster ML & DS technologies. Its features of statically typing and compilation are what makes it super in execution. With a lesser run time than any other language, knowing Java means you are good to go in the ML industry.

Coding

Indentation in Java is not a must which makes it easier than Python or R. Also, coding in Java may require more lines, but it is easier than in other languages. If you are well-versed with coding, Java will be beneficial in ML and DS.

Learning Curve

Java has a lot of areas where one must work hard. The learning curve for Java and allied language is quicker and more comfortable than other languages in totality. Suppose you know a language better and efficiently. In that case, it means that you can enter the domain at a more accelerated pace than through any other language whose learning curve is typical of Java.

Salary Packages

Java has been in use for 30+ years. The future salaries of people who know Java are perceived to be higher than through any other language. We are not saying that you might not have a handsome amount in your hand if one knows Python. Instead, we are just focusing that with Java’s legacy in place, the salaries you get in your growth years are expected to be more for people who know Java.

Community

Java will complete three decades of existence and is still one of the most prevalent and popularized languages. It means that numerous people in the enterprise know the language and will provide you with support in requirements. Several people in DS and ML are working through Java. It is an additional benefit that you can avail of if you learn ML and DS with Java.

Varied Libraries

With Java, you have access to various libraries in Java for learning ML. To name a few, there are ADAMS, Mahaut, JavaML, WEKA, Deeplearning4j, etc.

We hope that now you know why one must learn Machine Learning and Data Science in Java. With its scalability, versatility, and balanced demand, you will always have to work with Java.

The post CAN JAVA BE USED FOR MACHINE LEARNING AND DATA SCIENCE? appeared first on Artificial Intelligence.

The world right now runs on a network of trillions of signals sent from billions of computer applications designed and maintained by thousands of people. It is hence safe to assume that life right now runs digitally.

It was not the same a couple of decades ago but. The people who had computers were the exception and now it is the opposite.

All these software, technologies, and applications are a result of brainstorming by many technology enthusiasts who constantly work to make human life simpler.

That being said, it is known that whenever the outcome is a machine that simplifies human life, the technology behind it is as complicated. Android app development services could be a great example of this as they run on thousands of different network nodes and create applications to run on mobile phones.

A Glance At Cloud-Native Application Development

The latest revolution in this field is cloud-native application development. But what is it and how has it become so important for developers in the technological sector? A series of tiny, autonomous, and loosely coupled services are cloud-native applications.

They are intended to have well-recognized market benefits such as the ability to integrate customer input for quality improvement quickly.

The creation of cloud-native applications is an approach to designing, running, and enhancing apps based on well-known cloud computing techniques and technologies. An IoT app development company chooses cloud-native apps because they are easy to build, have a faster process, and are also highly scalable. 

If an app is “cloud-native,” it is designed specifically to provide a seamless experience of creation and automated management through private, public, and hybrid clouds.

Hence, if Android app development services can build new applications faster, optimize existing ones, and connect them all through cloud-native computing, they will be delivering applications more rapidly like how the business demands in competitive times. But for this formula to work, the applications must be programmed using the right language since that is what guarantees the quality of the applications to be top-notch.

While there are so many programming languages out there, Java and Python are the most preferred for cloud-native apps because of the reasons listed below.

Java For Cloud Computing

Java has been in business for way too long to now suddenly be labeled obsolete simply because there are new and more creative languages. Even now Java development services are using Java to develop and maintain applications with cutting-edge technology due to its robustness, security enhancement, ease of use, and the ability to transfer to multiple platforms.

The reason for developers and businesses to choose Java-powered cloud-native application development was to build custom apps faster without compromising on the standards of quality required to sustain in the competitive market. It has been used to create Gmail, Hadoop platform, Confluence, etc.

Java as a programming language only adds to this goal. Java as a programming language is secure, portable, and stable and also ensures high-performance execution without consuming unnecessary time.

Java offers the powerful framework required to support the multi-cloud store, cloud computing, and reactive programming for updating and improving applications. A Java development company backs Java as the preferred language for the following reasons:

• Serverless architecture can be supported by Java.
• AOT (ahead-of-time) compilation and microframeworks are possible with Java.
• Big size distribution is also possible because of the flexibility of Java.
• A Java development company can also access reusable codes and is product-oriented to create custom applications.

Python For Cloud-Native Application Development

Python simplifies the production of web applications, APIs, academic programming, and data science. Python is regarded as an attractive programming language that supports growth opportunities in diversified fields.

Python is one of the few languages that can be used for manipulating and processing massive data sets that are highly suitable. Python is most suitable for cloud computing for neural networks, machine learning, and streaming analytics systems.

Features like the ease of learning, brisk and easy to use data structures, third-party modules, far-reaching support libraries, community development, and efficient production of applications make Python the first choice of every IoT development app company.

Python is also called the preferred language due to the successful applications it has already created. The most trending apps like Netflix, Pinterest, Reddit, Spotify, and Instagram have all been created using Python.

With a portfolio like this one to testify its efficiency, it is safe to assume that Python even after thirty years of being in existence has managed to keep up with the changing rules of application development and has also justified its place at the top by programming applications that are used worldwide by billions of people. A few more reasons to choose Python as a programming language are listed below:

• Python can be used to build all kinds of apps such as business applications, image and design applications, GUI-based desktop applications, scientific and computational applications.
• Python is efficient when cloud computing involves neural networking.
• It is easy to use while streaming analytics structures.
• Ease of integration for hybrid applications running on several operating systems.

Few Final Words

When it comes to cloud programming, in order to get better goods, it is important to use data-oriented languages rather than general-purpose ones. With the amount of technological development happening round the clock and the competition taking place on a global level, it has become very tough for companies to create applications that are not only unique but also efficient.

There is also the need to be the first one in innovation and development to survive in the ever-evolving tech industry. Ever since cloud computing has begun, building apps has become somewhat easier due to its speed.

However, building efficient and bug-free apps means using a robust programming language that does not compromise on the scalability and innovation of the app. Python and Java development services have proven to be two of the most preferred languages for cloud-native application development as they are easy to use, highly portable, and efficient.

The post Why Are Java And Python The Most Preferred For Cloud-Native Application Development? appeared first on Artificial Intelligence.

Tribuo provides standard machine learning functionality including algorithms for classification, clustering, anomaly detection, and regression. Tribuo also includes pipelines for loading and transforming data and provides a suite of evaluations for supported prediction tasks. Because Tribuo collects statistics on inputs, Tribuo can describe the range of each input, for example. It also names features, managing feature IDs and output IDs under the hood to avoid ID conflicts and confusion when chaining models, loading data, and featurizing inputs.

A Tribuo model knows when it sees a feature for the first time, which is particularly useful when working with natural language processing. Models know what outputs are, with outputs being strongly typed. Developers do not need to wonder if a float is a probability, a regressed value, or a cluster ID. With Tribuo, each of these is a separate type; the model can describe types and ranges it knows about. Use of strongly typed inputs and outputs means Tribuo can track the model construction process, from the point data is loaded through train/test splits or dataset transformations to model training and evaluation. This tracking data is baked into all models and evaluations.

The Tribuo provenance system can generate a configuration that rebuilds the training pipeline to reproduce the model or evaluation. Also, a tweaked model can be built on new data or hyperparameters. Thus users always know what a Tribuo model is, where it came from, and how to create it.

Oracle sees Tribuo filling a gap in the marketplace for machine learning for enterprise applications. For example, whereas the Google-built TensorFlow library provides core algorithms for deep learning, Tribuo provides several machine learning algorithms, some of which are in TensorFlow and some of which are not, while also providing an interface to TensorFlow, said Oracle’s Adam Pocock, principal member of the Oracle Labs technical staff. And whereas the Apache Spark analytics engine is for large, distributed systems, Tribuo is for smaller computations that can fit on a single machine, Pocock said.

In addition to TensorFlow, Tribuo provides interfaces to XGBoost and the ONNX runtime, allowing models stored in the ONNX format or trained in TensorFlow and XGBoost to be deployed alongside native Tribuo models. Support for the ONNX model format allows deployment in Java of models trained using popular Python libraries such as PyTorch.

The post Oracle open-sources Java machine learning library appeared first on Artificial Intelligence.

There are plenty of new features to talk about in the world of Red Hat Runtimes. When I recently had the chance to speak with James Falkner, technical product manager for Red Hat Runtimes, he zeroed in on the Quarkus framework, or more specifically, the Red Hat branded build of Quarkus.

So, what’s Quarkus? It’s a Java framework developed with cloud-native integration as its top priority. If there’s a Kubernetes-based deployment target in an enterprise architecture, the microservices, containers and serverless applications will run faster and start up quicker with Quarkus when compared to an OpenJDK build that lacks the framework.

How the Quarkus framework changes the game

“Quarkus takes the design decisions made in early versions of Java and turns them on their head,” Falkner said. When Java was first released in 1996, Java virtual machines (JVMs) were deployed on big iron where a virtual machine could easily be dedicated 4 GB of RAM or more. And JVM startup times weren’t that important. Once a production server started, the JVM ran until it was time for its monthly scheduled maintenance. But today, that’s not how to optimize deployments to Kubernetes-based systems, such as OpenShift.

Memory usage and startup speed are of greater priority.  “If it takes 10 minutes to start up, that’s going to be a problem, especially if you’re looking at doing things like microservices or serverless,” Falkner said.

Java microservices need not be slow

Certainly the “Java is slow” criticism is one that has dogged the platform community for a long time. The Quarkus framework promises to be a powerful weapon to battle against that largely unjustified criticism. “Quarkus makes Java relevant in the age of Kubernetes and containers, whereas before it was dismissed as being too heavyweight,” Falkner said.

The Quarkus framework wasn’t the only topic of discussion.

The podcast also features Falkner’s insights on Open Liberty, JBoss EAP licensing and the role of Apache Tomcat and JBoss Undertow in the world of Red Hat Runtimes.

What is Red Hat up to in the middle tier? Listen to the podcast for more insight and opinions.

The post How Quarkus fits into the Red Hat Runtimes formula appeared first on Artificial Intelligence.

Microservices were supposed to make everything faster, but the reality for many Java developers is a new layer of complexity that can lead to performance problems. In parallel, developers are increasingly tasked with troubleshooting performance issues. For instance, a recent survey by JRebel found that 51% of Java developers are tasked with non-functional performance requirements during development.

The first step is to find those performance issues. Application performance monitoring tools identify issues during development and production, while service mesh solutions streamline inter-service communications and provide insight. Testing tools look at how microservices will function — or fail — at load, while profiling tools plug into the test environment to assess performance issues, such as memory leaks and threading issues.

Early stage analysis and optimisation are also important. Seeing how code is interacting with other services fits in well with DevOps, helping developers see the impact their work will have in production.

Eight Common Microservices Performance Problems

That early stage analysis also helps developers identify likely performance culprits. But, as we show in the following examples, not all microservices performance issues are equal: some are easy to fix, while others require more effort.

1. N+1 Problems

Also known as an N+1 Select Problem or N+1 Query, this occurs when a service requests a list from a database that returns a reference to a number of rows or objects (N), then individually requests each of those N items.

Fortunately, the solution can be as simple as changing a fetch type.

2. Performance Antipatterns

The N+1 problem is one example of antipatterns. Performance antipatterns typically centre around inefficient or superfluous queries that compound at load or scale. These performance antipatterns can occur for a variety of reasons, and only appear in specific instances, but can result in poor performance or even cascading failure.

Examples include adding timeout and retry functions. While theoretically a good idea, if the service being called is very slow and always triggers a timeout, the retry puts extra stress on an already overloaded system, exacerbating latency issues.

3. Synchronous Requests

Calling a service synchronously in the wrong situation can cause significant performance bottlenecks, both for individual services and for the combined application.

To solve this, use asynchronous requests, whereby a service can make a request to another service and return immediately while that request is fulfilled, thus allowing for more concurrent work. However, developers need to ensure that the receiving service can fulfill requests quickly, and scale to handle a high volume of requests as needed.

4. Overactive Services

Is a microservice receiving too many requests to handle? Throttling requests or using fixed connection limits on a service by service basis can help receiving services keep up with demand. Throttling also prevents overactive services from starving out less active, but equally important services.

There is a trade-off — throttling will slow down the application — but it is better than the application failing altogether.

5. Third-Party Requests

Sometimes, a third-party service or API can cause significant issues for an application, such as unacceptable latency. This is why it’s essential for developers to understand the limitations of a third-party, particularly at-scale.

Questions to ask include: can they keep up with anticipated demand and maintain performance? Is the SLA compatible with the application’s own?

Proactive best-practice steps also help, such as caching, pre-fetching, or using resiliency patterns to prevent services from causing cascading failures.

6. Application Ceilings

Even properly configured and optimised services can have performance ceilings. If all requests are deemed necessary and optimised, yet overloading of the service is still happening, then load balancing across additional containers should improve scalability. It is also worth looking at autoscaling, to dynamically adjust to incoming request load by adding and removing containers as necessary.

However, make sure to implement a maximum container count and have a strategy for defending against Distributed Denial of Service (DDoS) attacks, especially if the application is in a public cloud.

In addition, clustering technology, and potentially moving some services to NoSQL solutions to achieve higher scale than possible with an RDBMS. However, be prepared to deal with eventual consistency and compensating operations if ACID-like transactions are needed across services.

7. Data Stores

Microservices give developers the flexibility to use multiple data stores within an application, but choosing the wrong database type can have major performance (and monetary) consequences.
Select data stores for microservices on a service-by-service level, making sure that each one is suited to the job.

For handling lots of fast-changing unstructured data, it may be better to use a scalable or schema-less NoQSL data store.

For cases where data atomicity, consistency, isolation, and durability are needed, a RDBMS should be used.

8. Database Calls

When a service requests data from multiple databases, each of those databases has the capacity to hold up that request. If this happens frequently, caching that information in a single, easily accessible place — rather than relying on multiple databases — can relieve the problem. Universal rules can be set, such as time limits and prevention of excessive calls.

Memory caching is used in high performance and distributed systems to store arbitrary data for rapid local access. Some database systems offer native in-memory caching.

Final Thoughts

Clearly, addressing performance issues in Java-based microservices is not a simple, one-off task. Given the growth and increasing dependence on them, however, it is too big an issue to ignore.

Now is the time to put in place a robust strategy to solve problems today and mitigate them in the future.

The post Eight Common Microservices Performance Problems (and how to Solve Them) appeared first on Artificial Intelligence.

AI programming is an elevation of technology that has brought efficiency and optimum benefits to different company’s operations and people’s lives. AI has brought another level of smart technology to different industries and the prospects of its potential still grow with the expectation that it would reach human intelligence. This is because developers are willing to explore, experiment, and implement their capabilities to satisfy more of the human and organization necessities. After all, necessity is the mother of invention.

Therefore, here is the list of top 5 programming languages that are in trend for AI developments in 2020.

Python

Python is an Interpreted language which in lay man’s terms means that it does not need to be compiled into machine language instruction before execution and can be used by the developer directly to run the program. This makes it comprehensive enough for the language to be interpreted by an emulator or a virtual machine on top of the native machine language which is what the hardware understands.

Python offers the least code among others and is in fact 1/5 the number compared to other OOP languages. No wonder it is one of the most popular in the market today.

Python has Prebuilt Libraries like Numpy for scientific computation, Scipy for advanced computing, and Pybrain for machine learning (Python Machine Learning) making it one of the best languages For AI.

R

R is a multi-paradigm language that can be called a procedural one, much like Python is. It can also support object-oriented programming, but it is not known for that feature as much as Python is.

R is considered to be a statistical workhorse, more so than Python. Once you start learning, you will understand that statistics form the base of machine learning and AI too. This means that you will need something which can suit your needs, and R is just that. R is considered to be similar to SAS and SPSS, which are other common statistical software. It is well suited for data analysis, visualization, and statistics in general. However, it is less flexible compared to Python but is more specialized too.

R is an open-source language too. This does not simply mean that it is free to use, for you – it also implies that you will have a lot of support when you start to use it. R has a vast community of users, so there is no dearth of help from expert practitioners if you ever need any.

Java

One of the best things about Java is Java Virtual Machine Technology. This technology allows developers to build a single app version that will run on all Java-enabled computing platforms. Major strengths of this programming language are as following: maintainability; portability; transparency.

AI is closely connected with search algorithms, genetic programming, and the use of artificial neural networks. Java in the artificial intelligence sphere may be more than useful. Programming AI in Java has many benefits: easy use, debugging ease, simplified work with large-scale projects, facilitated visualization, better user interaction. Another reason for programming AI in Java is the incorporation of Swing and SWT (the Standard Widget Toolkit). These features make graphics and interfaces look appealing and sophisticated.

Another reason for using Java in AI programming is the vast amount of tutorials on the Internet. Just type “how to program artificial intelligence in Java” and you’ll get a lot of pages to choose from. Java is versatile. It’s used for making multi-robot systems, sensor networks, and machine learning suites.

Scala

Just like Java, Scala belongs to the JVM family. Scala is a fairly new language in the AI space but it’s finding quite a bit of recognition recently in many corporations and startups.

It has a lot to offer in terms of convenience which is why developers enjoy working with it. Also, ScalaNLP, DeepLearning4j, etc are all tools and libraries that make the AI development process a bit easier with Scala. It’s good for projects that need scalability and combines the strengths of Functional and Imperative programming models to act as a powerful tool that helps build highly concurrent applications while reaping the benefits of an OO approach at the same time.

Scala provides good concurrency support which helps with projects involving real-time parallelized analytics. It has a good open source community when it comes to statistical learning, information theory, and Artificial Intelligence in general.

Rust

The most loved language of all is Rust, an open-source programming language that was hatched by tech pioneers at Mozilla in 2010.

In fact, Rust has been voted the most-loved language for the past four years in Stack Overflow’s annual developer surveys as solves pain points present in many other languages, providing a solid step forward with a limited number of downsides.

Rust concepts are also being used in Microsoft’s recently open-sourced Project Verona, an experimental language for safe infrastructure programming that could help Microsoft securely retain legacy C and C# code.
Mozilla Research describes Rust as a “systems programming language that focuses on speed, memory safety, and parallelism”.

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