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Table of Contents
Machine Learning frameworks help overcome tedious tasks when experimenting, optimizing, or putting into production an Artificial Intelligence (AI).
When it comes to new trends in technology, new programming languages are often one of them.
One of the languages that are starting to gain more and more attention is Scala. While not yet popular, Scala appears to be gaining ground by offering a good balance between accessible Ruby syntax and robust Java support.
Here are some arguments to prove that Scala deserves to be considered.
It works with Java Virtual Machine
The reality of programming for businesses is that Java is the most popular.
Besides, large companies are reluctant to take the risk of redoing all already established programming. Scala doesn’t seem to be a problem here because it can still work with Java Virtual Machine.
Scala is, therefore, able to play, or rather operate, with the tools and parts already put in place by the company. Migration to Scala is therefore much less risky than you think.
Scala is also fully capable of operating with existing Java codes. While many claim this to be consistent, the reality is more complicated. But despite these problems, it is said that Scala performs better with Java than with other programming languages.
Some programmers also have this fear of not being good enough when it comes to changing programming languages. But since Scala uses JVM, there is no need to be afraid.
In general, Scala works with the equivalent of a Java program, so companies should not suffer if they decide to use Scala.
Scala also allows the use of most JVM libraries, which are often deeply integrated into the company’s codes. Therefore, Scala is not a hindrance for a company that uses Java extensively.
Can Scala be used for machine learning?
Today we will talk about the implementation of machine learning in Scala; i-e, can scala be used for machine learning. I’ll start by explaining how we got to such a life. So, our team has been using all the machine learning capabilities in Python for a long time. Why not, we thought, there are tons of libraries there; even Apache Spark is written in Scala!
Click here to get the low down on Practical Functional Programming for the JVM.
To be clear, dear reader, this article is not written to undermine Python’s reputation for machine learning. No, the main goal is to open the door to the world of machine learning in Scala, give a short overview of an alternative approach that has emerged from our experience, and tell what difficulties we encountered.
In practice, everything turned out to be not so happy: there are not so many libraries that implement classical machine learning algorithms, and those that exist are, often, OpenSource projects without the support of large vendors. Yes, of course, there is Spark MLib, but it is strongly tied to the Apache Hadoop ecosystem, and I really didn’t want to drag it into the microservice architecture.
A solution is needed to save the world and restore a peaceful sleep, and it is found!
What do you need?
When we selected an instrument for machine learning, we continued from the following standards:
· it should be simple;
· despite the simplicity, no one canceled the wide functionality;
· I really wanted to be able to develop models in a web interpreter, and not through the console or constant assemblies and compilations;
· availability of documentation is important;
· ideally, to have support, at least answering Github issues.
Is Scala better than Python?
Java is one of the most used languages globally; it is a language without great merits but not even flaws. Often it is the default choice; programming in Java is simple, finding Java developers is easy, the ecosystem is among the most mature.
Python is also one of the most used languages globally. Python’s creator has much stronger opinions than the Java community, and the language is therefore much less “standard”. Yes, it is a straightforward language to use, with weaknesses in scalability and viral libraries in certain niches such as data science. If it is a project in which you want to explore a new solution without necessarily pretending to create an application to be put into production, or if you want to use certain of its specific libraries, Python is the best choice.
Scala is a language created to try to match the OOP and FP paradigms. In many ways, it is an experimental or academic language. It owes its success to some frameworks developed with it, Spark and Akka, for example. Yes, it is a complicated language to learn and use, but it brings to JVM all the benefits of statically typed functional programming while remaining compatible with the Java ecosystem. If you want to develop parallel or competitive computing applications without giving up JVM, scala is one of the best choices.
The good news for me is that it gave me good motivation to be with Python. The bad news is that I don’t quite understand why?
Performance of the Python code itself. In general, Scala is faster than Python but varies from activity to activity. Plus, you have several options, including JITs like Numba, C extensions ( Cython ), or specialized libraries like Theano.
Click here to learn to build systems for data processing, machine learning and deep learning using Scala.
Scala vs. Python for machine learning
Python remains to be the most widespread language in its field. Python is an open-source programming language and is extensively used as a scripting and mechanization language. It has several features that make it popular among the many other tools available to developers. While controlling and fast, Python is also easy to acquire and practice. It claims well-organized high-level data constructions and a simple yet effective method to object-oriented programming.
Scala is a high-level language that is a combination of object-oriented and functional programming. The language is built on top of the Java Virtual Machine (JVM), and one of Scala’s fortes is the aptitude to interrelate with Java code easily.
Scala’s static types help developers sidestep viruses when developing compound claims. And the JVM runtime allows you to build high-performance systems with easy admittance to large sets of the public library.
Disadvantages of Scala:
Due to the mixture of practical and object-oriented nature in this language, it is occasionally difficult to understand its type.
There are not enough Scala developers yet.
Python and Scala for Machine Learning and Data Science
Python has measured the most popular language for data science today – not only because it is easy to learn and use but also because of its widespread libraries and outlines. For schemes in data science and machine learning, Python can offer a wide assortment of useful collections – SciPy, NumPy, Matplolib, Pandas. For more complex deep learning projects, you can use Python libraries such as Keras, Pytorch, and TensorFlow.
On the other hand, it’s valuable knowledge and using Scala for machine learning just for the sake of Apache Spark. Scala is used in combination with Apache Spark to work with large quantities of data known as Big Data.
When we look at a Scala program, it can be defined as a collection of objects that interact by calling each other’s methods. Let’s now take a quick look at what class, object, method, and instance variables mean.
Object – objects have states and behavior. An object is an instance of a class.
Class: A class can be defined as a template/outline that describes the behavior/states associated with the class.
Methods. A method is basically a behavior. A class can contain many methods. In the methods that logic is written, data is manipulated, and all actions are performed.
Fields: Each object has its own sole set of example variables called fields. The state of the object is produced by the values allocated to these fields.
Closure: A closure is a function whose return value depends on the value of one or more variables declared outside of that function.
Traits: A trait includes method and field definitions, which are then be recycled by mixing them into classes. Traits are used to define the types of objects by specifying the signature of the supported methods.
Nobody is born learned, and as in any other language, in Python, it is necessary to start from less to more, little by little, to fully understand its syntax and operation, getting to understand and handle this programming language like a charm.
We will analyze its basic syntax; remember that Python offers simplicity and clarity so that we do not get messy, and any first-time programmer can use it without great complications.
When we talk about syntax in Python, we refer to the correct use and order of the words we use to communicate as in all languages. Therefore, in Python, it is also necessary to meet certain requirements when expressing ourselves.
In this way, the interpreter’s misunderstanding is avoided, so the first thing to do is learn certain words in English, their meaning, and the proper use within the language.
Which programming language is best for machine learning?
Python is one of the most popular programming languages of recent years. Its clear syntax and readability make it the perfect coding language for beginners. It is fair to think that Python’s ease of learning was the essential potential behind its wide use. But this raises a discriminating question: When is Python not the right answer? In other words, what are the clear signs that Python is not the right language to learn and/or use?
Click here to get better acquainted with Machine Learning with Python.
What is Python, and what is it for (programming Machine Learning and AI)
Python is increasingly read as a language directly or indirectly instrumental to machine learning, essentially a language “for” machine learning. Most machine learning courses have been written using the Python language, and coding education has adopted Python as the language to learn, with extensive use in small computer progressions such as Raspberry Pi (AMD) others.
This trend, coupled with the plethora of large companies using or displaying the usability of their products with Python, suggests it is the future’s leading programming language. Basically a kind of Swiss army knife. We want to answer the question in this first part: what does not serve as such a “tool”? Let’s analyze the various fields of application below.
Python for website programming and development
Python is more than capable of enabling large-scale web development. Instagram is the largest site running Django, which is a Python web framework – a backend. This is no small feat, as Instagram engineer Zekun Li explains: “We started using Python right from the start for its simplicity, but over the years, we had to do a lot of hacks to keep it simple as we scaled the architecture. “.
Before seeing the position in detail, a small sign:
Each of these languages is the “best programming language of 2021”.
How is it possible?
Each programming language has exact features, making it faultless for solving certain circumstances but inappropriate for others.
Which programming language is best for machine learning?
In short, no language can be the best, always, on every occasion. That’s why it would be perfect to know them all, these languages. 😉
Start your programming languages learning journey here.
Ruby is an open-source programming language, absorbed on ease and output. Ruby’s syntax is simple and stylish: it is easy to write the language with natural reading.
Swift is an object-oriented programming language. The Swift language was industrialized by Apple and is aimed at computer programmers of Apple systems in its numerous versions (macOS, iOS, watchOS…).
Go is a programming language developed by Google, supported by Google and the independent developer community as it is an open-source project. It is a simple language to write (as simple as Python) and at the same time very efficient (as efficient as C ++).
C / C ++
C and C ++ are historical programming languages: developed in the 70s, they are among the most used languages in the history of computer science.
C # (pronounced C Sharp, sounds like “see sharp” – see sharp) is an object-oriented programming language. Advanced by Microsoft, it presents itself as an opposing language of Java.
PHP is an understood scripting language with a simple and widely used syntax. PHP ropes both an authoritative and an object-oriented method.
Python is a simple programming language to study, has easily clear code, and is very versatile. It is, in fact, a high-level multi-paradigm language appropriate for object-oriented programming, mechanical and practical programming.
Java secures second place on the dais of the best programming languages 2021.
Not that it’s a surprise:
Java is extremely popular, thanks to the topographies that make it one of the most stable, complete, and reliable languages for building multifaceted systems – LinkedIn is written in Java, for example.
Scala for data engineers
In the first half of this year, I built a team of data engineers and needed to choose a programming language. Scala seemed like a perfect choice at the time—we experimented with Spark initially, but we ran into some obstacles. I tried to investigate some ideas in this field, but none of them were constructive. The ideas in the articles I read are either biased, outdated or both. I am writing this article to help someone encounter the same situation as me and need to clarify or evaluate Scala’s role in building a team of data science or data engineers. This article will discuss the main parts of the Scala ecosystem from data and people’s perspectives. I grabbed time series data on GitHub to help analysis, and at the same time, tried to solve some of my previous concerns. I also contacted and solicited the opinions of some professionals who lead the Scala group, including Martin Odersky. They all contributed their time very generously and were very happy to share their ideas. I will present some things fairly, but I need to affirm that they are my personal opinions. I have not made any significant contributions to the Scala system and open-source data, but I have always used the Haskell language before Scala. I recruited and trained a team of six people at DataScience Inc., a data science company in Los Angeles, to successfully use the Scala language alone to handle things.
Scala is very dependent on the Java system. The single most important thing for Java last year was that Oracle told Google. The Java system and the entire industry are very excited about the need for a license to create a compatible API implementation. Although Google has won the righteousness because the disappointing Federal Court decided on API copyright, the core issue has not been resolved.
Functional programming Python vs. Scala
Functional programming (Functional Programming) is a programming style; it is relative to the imperative programming style, the common object-oriented programming is the imperative programming style.
Instruction-based programming is concept-oriented to computer hardware, with variables (corresponding to storage units), task declarations (acquisition and storage instructions), languages (memory references and arithmetic operations), and control declarations (jump statements).
The function here is really a function in arithmetic, that is, the charting of sovereign variables to dependent variables. The value of a function is only determined by the function parameters’ value and does not depend on other states.
Functional programming is a programming paradigm with a high degree of abstraction. Therefore, as long as the input of any function is certain, the output is certain.
In functional languages, a function, as a first-class citizen, can be defined anywhere, inside or outside the function, as a parameter or return value of the function, combining functions, or assigning functions to variables. Functional programming in the severe sense incomes that variable variables, projects, loops, and other authoritative control constructions are not used for programming.
Specific to programming languages, Scala (static language) and Python (dynamic language) support functional programming styles. Still, they are not purely functional, which means that they support both imperative style and functional style. Java is basically an imperative style, but since Java 8 introduced lambda expressions, it also partially supports the functional style. They are characterized by supporting a certain function as the parameter of the above functions.
Scala big data frameworks
Although the use of Java in big data is more general, and Python is also gaining impetus, Scala has always had a steadfast position. We are acquainted with Spark, Kafka, and Flink, all of which are industrialized by Scala.
Therefore, mastering Scala can learn the source code of big data components and greatly improve the efficiency of big data development.
This is why Scala’s salary level has been so far ahead.
According to the global programming language salary statistics in 2019, the top-ranked Scala is undoubtedly a language with excellent job requirements and income. Of course, there is a difference between income and region. For example, in the United States, Scala has the highest income, which can reach 143k US dollars, followed by Clojure (139k US dollars), Go (136k US dollars), Erlang (135k US dollars), Objective-C (132k US dollars) USD)
The data is based on Stack Overflow’s 2019 survey report on programming language salary position designers.
Why is there such a high salary?
This is probably due to the characteristics of Scala:
Elegance: This is the first issue that framework designers should consider. The users of the framework are application development programmers. Whether the API is elegant or not directly affects the user experience.
Fast speed: Scala language has the strong expressive ability, one line of code is worth multiple lines of Java, and the development speed is fast; Scala is statically compiled, so it is much faster than JRuby and Groovy.
Can be combined into the Hadoop: Hadoop is now the de facto normal for big data. Spark is not to substitute Hadoop but to recover the Hadoop ecosystem. Most of the JVM language may think of Java, but Java’s API is too unpleasant, or it is too hard to tool an elegant API.
This is why the source code of many big data components is developed in Scala.
After mastering Scala, we can quickly develop Flink, Spark, and other big data projects. The development efficiency has been greatly improved and using functional programming, and the code will be more concise and cool.
Disclaimer: Whilst every effort has been made to ensure that the information published on this website is accurate, the author and owners of this website take no responsibility for any loss or damage suffered as a result of relience upon the information contained therein. Furthermore the bulk of the information is derived from information in 2018 and use therefore is at your on risk. In addition you should consult professional advice if required.