Not Only Python: Problems, Errors and Alternatives

Not Only Python: Problems, Errors and Alternatives

Artificial Intelligence (AI) and Machine learning (ML) are our daily companions, and it isn't easy to imagine life without algorithms and statistical models. Python is the first thing that springs to mind whenever we hear the term Machine Learning. These technologies have a history of aiding the tech world and a lengthy association.

Python is possibly the most delicate machine learning language, with48.24% of developers using it. Python is superior in all areas of scientific computing due to its low-level packages and well-designed high-level APIs, but it has some drawbacks when used for specific tasks. Therefore, Python alternatives were needed, and their demand was universal.

Multiple emerging languages now perform equally or outperform Python. Hence, it's no longer the only machine learning option. Scala, Julia, MQL5, and other languages that are not Python superset, can be used to develop and provide machine learning applications. Therefore, machine learning isn't limited to Python anymore.

In this article, we will explore the emergence of new machine learning languages, how they have eroded Python's market share, and the shift in the future of machine learning.

The Disadvantages of Python

Let's discuss a few of the drawbacks of Python that developers and data scientists commonly confront.

Performance & Speed

Python is fast for development but slows at computation. It uses an interpreter instead of a compiler as an interpreted language. Due to sequential code execution, it's slower than other data science programming languages. Python is slower than C, which has many useful tools and libraries. Unless you master vectorized code, you'll notice how slow it is.

Cython, a superset of Python that executes Python code compiled into C, gives you C's speed without adding more computing resources. Many programmers use Cython to write Python code as fast as C but more concise and readable.

Design Restrictions

Python's design limitations are a significant issue. Its dynamic typing causes this constraint. Dynamically typed code doesn't require declaring variable types. Python employs duck-typing, a confusing feature. According to the duck-typing principle, anything resembling a duck is a duck. In duck typing, an object's class or type is less important than its function. Duck Typing needs no type checking. You can also verify a process or attribute.

Inefficient Memory Consumption

Python uses a lot of memory due to its large structures. Besides, Python data types are flexible with memory consumption. Python isn't suitable for memory-intensive tasks. Python's flexible data types also cause memory consumption. As a result, memory management in Python becomes challenging as you create large and long-running Python systems.

Threading Deficiency

Python threading is less efficient than other languages. Even though Python is multithreaded, it executes code one thread at a time. Hence, multithreaded Python gives you single-threaded efficiency. Python has a Global Interpreter Lock (GIL), which only one thread can use concurrently. Only when Python uses a native library, many threads can run together. Also, multithreading is possible in Jython, a superset of Python, but not in Python.

Why Do Data Scientists Look for Python Alternatives?

Python is amazing. But it can't be the Flash and Incredible Hulk at the same time! Someone may need the Flash, and someone may need the Hulk to perform their tasks. Yes, the diversity in user requirements. Similarly, Python was not designed solely for mathematical and data science applications. It needs an external library like NumPy or Tensorflow, a library developed specifically for deep learning algorithms. Therefore, data-specific programming languages should be more advantageous than Python.

For instance, machine learning in business requires speed and scalability to reduce execution time. You cannot keep customers waiting. Hence, you need a speedier ML language to perform. The faster, the better.

Now, Python is fast, but is there anything faster than Python? Yes, it's Julia, also Scala!

And this is the type of circumstance in which the data scientists prefer to use a different programming language over Python.

Python Alternatives for Data Science

Several Python alternatives perform as well or better than Python in various application contexts. Here are some Python Data Science alternatives:

1. Julia

Julia is a brand-new, high-level dynamic programming language that is innovative, fast, and comfortable. However, it is a general-purpose language capable of writing various applications. Notably, a large portion of its package ecosystem and capabilities are geared at high-level numerical computing, which is perfect for machine learning.

Julia is built on multiple languages, including low-level programming languages such as C and high-level dynamic typing languages such as Python, R, and MATLAB. Julia reflects the optional typing, syntax, and features of these languages because Julia's goal was to combine their best elements and eliminate their shortcomings.

Julia's creators wanted a language that could handle Scientific computing, machine learning, data mining, large-scale linear algebra, and distributed and parallel computing as fast as C. However, they made something even more swift. Even though Python has gotten faster, Julia still beats it.

Julia simplifies the writing of the numerous mathematical symbols used in machine learning. It offers multiple GPU-programming packages, such as ArrayFire, which can let GPUs execute generic code. Each package has a unique programming model. For example, NVIDIA has CUDA.jl, AMD has AMDGPU.jland oneAPI.

Machine learning engineers can quickly deploy Julia across large clusters benefiting significantly from its potent tools, such as MLBase.jl, Flux.jl (deep learning), MLJ.j (general machine learning)  and Knet.jl (deep learning).Flux is a high-speed deep machine learning library that comes pre-installed with additional tools to maximize Julia's capability. Similarly, ScikitLearn.jl, TensorFlow.jl, and MXNet.jl, appropriate for ML applications, are also included.

Julia excels at solving complex computational issues. Thus, numerous prestigious institutions, including,Stanford, and Tokyo Metropolitan University, offer Julia courses. It also stands firm in the Python vs. Julia comparison.

Numerous industry executives like Logan Kilpatrick consider Julia the future of machine learning and data science. Read further on TDS: The Future of Machine Learning and why it looks a lot like Julia

The Main Features of Julia

Several of Julia's core features that enable high-efficiency data science capabilities:

• Julia is designed to be fast. It's 2-20 times faster than Python;
• Flexible and extensive library functions;
• Built-in package manager;
• PyCall and CCall packages Call Python directly;
• Manage other processes with shell-like capabilities;
• Developed for parallelism and distributed computation;
• Automatically generates efficient code for various argument types;
• Free and open-source with an MIT license.

2. Scala

What Is Scala?

Scala is a high-level programming language that supports both object-oriented and functional programming techniques. Martin Oderskycreated it, and it was formally launched in June 2004. Scala has grown in popularity among developers and is making its way across today's technologies.

Scala is aJava Virtual Machine (JVM)language compatible with Java applications and libraries. Because it lacks the concept of fundamental data, it's frequently regarded as a form of static language. Its multi-paradigm, multi-core architecture is complex.

Apache Spark is a powerful and quick tool for real-time streaming and big data processing. ScalaSpark is helpful for complex mapping, ETL, and large data processing tasks. Scala lets users implement new Spark features by understanding the code. Scala's compatibility with Java allows programmers to grasp object-oriented concepts quickly. Check out more about the collaborative filtering-based recommendation system with Spark-ML and Scala.

the spark MLlib package discusses both high-quality algorithms and excellent performance in Spark, making it a scalable machine learning library. Classification, regression, clustering, collaborative filtering, and dimensionality reduction are all included in Spark's MLlib machine learning library. Furthermore, Breeze, Spire, Saddle, and Scalalabare a few other ML libraries that help create robust data-processing applications.

Apache Kafka, an open-source distributed event streaming platform, accompanied by Spark Since Python is popular, PySpark is also popular. What executes slowly in Scala Spark crashes inevitably in PySpark. In addition, you can use the TensorFlow Scala library to create an adaptable, high-performance serving system for machine learning models.

Scala's static types help with complex applications. Its JVM and JavaScript runtimes enable high-performance systems with easy access to libraries. Also, it outperforms Python and Scala. Also, it stands firm ground in the python vs. Scala comparison.

The main features of Scala

Several of Scala's core features that enable high-efficiency data science capabilities:

• Scala is fast, 10 times faster than Python;
• Got an advanced type inference mechanism;
• Case classes and pattern matching;
• It has concise but readable syntax known as expressive;
• Provides higher-order function;
• Use Singleton object instead of static variables;
• Code using OOP, FP, or in a hybrid style;
• Scala source code is compiled into ".class" files that execute on the Java Virtual Machine (JVM).

3. MQL5

What is MQL5?

MQL5is a specialized high-level object-oriented programming language that offers advanced data analysis and machine learning capabilities. It is built on the widely-used and well-known programming language C++, known for its speed and versatility.

MQL5 is not an everyday ML language like Python, Julia, or Scala. It is specially built for financial markets to perform monitoring financial instruments. The language's core is similar to others, but it has unique features. MQL5 supports integers, booleans, literals, strings, dates, times, and enums. It has structures and classes as complex data types.

This MQL5 language reference lists functions, operations, reserved words, etc. Besides, it uses integers, booleans, literals, strings, dates, and times. It has complex structures and classes. Furthermore, the reference includes standard Libraryclass definitions for trading strategies, control panels, custom visuals, and file access.

Also, MQL5 has1 500+ libraries with source codes to employ in the new application development. You can use the ALGLIB library, which contains extensive numerical analysis functions. Similarly, TimeSeries library for working with time series, Fuzzy library for developing fuzzy models, and diverse libraries.

MQL5 is a powerful programming language that lets you build real-time systems and provides visual decision assistance. MQL5 supports enumerations, structures, classes, and events. By increasing the number of primary embedded types, MQL5 programs can communicate with DLL.

The syntax of MQL5 is similar to C++, which makes it simple to transform programs written in different programming languages intoMQL5 applications. Hence you get identical efficiency as C++ while working with data analytics, AI, or financial instruments as trading robots.

The main features of MQL5
Several of MQL5's core features that enable high-efficiency data science capabilities:

• MQL5 is built on C++. So it's as fast as C++ and faster than Python;
• Specially created for financial market instruments and analytics;
• Completely event-driven;
• 1 500+ source code libraries;
• Predefined standard constants and enumerations. Service structures store information;
• Change the color scheme, create control panels, add custom symbols, and export price charts from an MQL5 program;
• 12 new drawing styles, 512 buffers, and direct value calculation with indexing from past to future;
• Debug Expert Advisors on both charts and in the multi-currency tester.

Which Python ML Alternative You Should Pick?

A programming language is similar to driving a race car, where the car is essential, but the driver is even more so. Similarly, as a data scientist, you are the driver of these unique sports cars. If you are the one who drives them

Every data scientist or developer hastheir requirementsand specifications for a particular technology. And the optimal technology selection is contingent upon the perspective and features you require. Hence, it's challenging to reach a final verdict.

If you're looking to do multi-processing, the mathematical appearance that requires speed, you can choose Julia. It's the quickest of the three. On the other hand, Scala should be the better choice if you're considering large-scale projects due to its ability to handle them. And if you're interested in financial instruments, MQL5 is the best bet.

Similarly, each programming language has its own distinct set of inherent characteristics and specialties. As a result, selecting one that meets your requirements and specifications is the best course of action.

Summary

Machine Learning is an ongoing process that is in a state of constant evolution. Over time, it arose alongside the Standard ML (SML) language and then gained popularity with Python. And nowadays, there are a growing number of ML languages with diverse interests.

In a nutshell, Python is an extraordinary language for machine learning. However, Python is not a data science-specific language, and there are modern languages that can perform specific tasks more efficiently than it.

Therefore, newer languages are gaining market share and growth in the machine learning industry. Hopefully, within a few years, they will also achieve significant popularity.