PandioML: The newcomer on the block and how it compares to Scikit Learn

Machine learning, algorithms, artificial intelligence – they’re buzzwords at this point. It seems that every article targeting both the developers and the general population is full of different terminology, tools, and methods that are hard to keep up with. Still, one thing has stayed pretty uniform when it comes to the world of machine learning, and that is Python.

The Pylon that is Python 

Python has consistently ranked as one of the most popular programming languages in the world, and it is also one of the oldest still in use. Python has been around for over two decades and has always been a high-end general-purpose programming language.

These days, Python’s most significant use case is developing neural networks for machine learning, creating detailed statistical functionalities and algorithms, and enabling innovative protocols that shape the technology of tomorrow. 

Now, we’ve mentioned that Python isn’t exactly the easiest thing to master, especially when you’re using it to create something brand new from scratch, which is why libraries are such a pillar of programming. 

What is Scikit Learn? 

In its most basic rendition, Scikit Learn is a popular library used to integrate machine learning and machine learning algorithms into various production systems. 

A more elaborate explanation of Scikit Learn is that it’s a library that contains everything needed for the construction, deployment, and modulation of algorithms used in machine learning applications in various fields, primarily oriented towards business practices. 

Scikit Learn is an immensely popular option and is widely praised for its simplicity, which lies in its general purpose. Python is complex, but with libraries such as this one, developing and deploying anything becomes much simpler. 

This library was initially developed in 2007 by David Cournapeau as a part of the “Summer of Code” project by Google. 

The project has then expanded massively, being joined by Matthieu Brucher, who used it as a part of his thesis. Due to this, the project has attracted massive backers, including INRIA, Google, Tinyclues, and even the Python Software Foundation themselves. 

In essence, this library provides a wide range of supervised and unsupervised learning algorithms through a consistent interface in Python.

To use it, you’ll need to enable and install the SciPy stack, which consists of:

  • NumPy – a base n-dimensional array package;
  • SciPy itself – an essential library for scientific computing;
  • SymPy – a symbolic mathematics stack;
  • IPython – the premier interactive console;
  • Pandas – used for data structuring, statistics, and analysis.

As this entire stack suggests, Scikit Learn is an extensive library that can be used for a wide range of things, but since it includes many different stacks, its learning curve is a bit steep. 

Why is Scikit Learn Important?

Development has always been a complicated thing, and that applies to any kind of development. However, when we’re not talking about “Hello World” bots and are instead referring to the development of complex machine learning algorithms, the development becomes much more complicated.

Python in itself is popular, but it’s popular because it’s a powerful programming language, not because it’s an easy one to learn or master. 

Without libraries, learning or operating Python would be a nightmare. The time it would take to execute different functions would be increased tenfold, the resources that could be spent developing would be wasted on menial tasks that are easily automated, and the turnover rate of any project would be slowed down to a snail’s pace. 

How Does Scikit Learn Provide Solutions for the Business Community?

These days, machine learning algorithms that enable artificial intelligence solutions can be found in everything from identification devices to extensive banking systems that handle billions of dollars in transactions per day. Technically and practically, machine learning and AI solutions developed and deployed by Python are present across all modern industries, especially due to the latest updates to Python itself. 

Scikit Learn is a jack-of-all-trades library for Python. It makes for a potent weapon in any company’s business intelligence arsenal when combined with Python’s integrated online and adaptive capabilities. 

Outside of scheduling, structuring, and statistics, Scikit Learn is used for things such as predictive analysis. Considering that it’s a pretty simple core idea and that the API is also very adaptive, you can seamlessly implement it within existing applications and frameworks to rework or improve upon existing algorithms through ML solutions. 

The Limitations of Scikit Learn

Before we start criticizing Scikit Learn – it’s important to note that even with its shortcomings, this is still one of the best known, well understood, and widespread stacks used for ML programming in Python. It’s a sophisticated stack, but it isn’t without its faults, especially when it comes to neural networks. 

Limited Statistic Functionalities 

One of the biggest shortcomings that Scikit Learn has is its lack of support for statistical functionalities. The worlds of data science and machine learning are deeply entwined. The more data there is, the more the machine learning model has to work with, and the better the machine learning model works, the more data it can collect.

A major problem with Scikit Learn is how it forms and delivers statistics – in a relatively simple, primitive, and limited way. Scikit Learn is less than ideal for any company looking to get any relevant data from their machine learning and improve their machine learning with any subsequent data. 

Primitive Neural Network Model

Scikit Learn was never built for developing neural networks – a key feature required for machine learning and artificial intelligence. Python excels in developing high-end neural networks, but since Scikit Learn doesn’t provide the necessary tools to produce such neural networks, it’s less than ideal for ML and AI applications that require such sophistication.

Now, while you can develop basic algorithms to predict things, do basic analysis, and only convey information – that’s practically all you can do with Scikit Learn when developing neural networks. 

Difficult to Learn

Scikit Learn is just as the name implies – a kit. It is a kit that consists of many different things, all of which have a different purpose. We’ve mentioned all the elements of Scikit Learn, and they will all require individual involvement and mastery.

Now, getting the hang of one of them will be a pretty difficult task, let alone mastering all of them. Scikit Learn is a fantastic jack-of-all-trades solution for python development in machine learning. Still, it’s a master of none, and learning it is an arduous process that requires a background in deep machine learning or many, many hours spent studying how everything works. 

The Promise and Prospect of PandioML

While Scikit Learn might be one of the most popular options for Python Libraries, many other contenders are more specialized in different fields. There are plenty of Python libraries that are arguably better – one newcomer to consider is PandioML.

Since artificial intelligence and machine learning are the technologies of the future, it’s safe to assume that Python will be the programming language that enables them, as it is the most powerful, prominent, and popular out of the lot.

When it comes to libraries, PandioML is a lot like Scikit Learn – it provides a wide range of applications and solutions for machine learning models, only instead of offering a bundle of hard to use software, PandioML has a different approach. 

PandioML leverages the power of adaptive machine learning technology that is finding its way into everything from facial recognition software to your Spotify playlist. However, rather than enabling this technology for menial things, it has a keen focus on real-world applications. 

Instead of simply offering a large stack of solutions, PandioML provides all of them in a centralized manner that is easy to operate and immensely powerful. 

How Does PandioML Work?

As mentioned above, rather than providing a collection of tools for Python, such as Scikit Learn does, PandioML centralizes all the tools within one sole system, allowing for a far better rate of operation and more usability.

Other than that, PandioML has tailor-made solutions built into the centralized application that improves upon the traditional tools that Scikit Learn offers, allowing for faster and more robust use. 

Businesses are the main beneficiaries of PandioML’s power. They can use the algorithmic neural networks that can be created to fit almost any use case in a wide range of industries. 

Perhaps the most important thing that makes PandioML such a juggernaut is that it has all the functionalities of its competitors, just refined, and more adaptable and integratable.

Integration is one of the critical factors that go into ML and AI. Integrating a solution into existing infrastructures helps optimize them, reduces costs, and allows for a far easier learning process.

Library Algorithms Vs. Batch Processing

There are two ways that systems such as this handle machine learning – batch processing and library algorithms. Now, batch processing used to be the industry standard and still is the most commonly used. It entails delivering data to the machine learning model through something akin to an SQL system. 

Now, while that system functions fairly decently, it’s not the ideal solution for machine learning algorithms that require a different stream of data.

For machine learning applications, library algorithms reign supreme as the best possible solution. Coincidentally, PandioML uses library algorithms for their machine learning solutions, yet another edge they have over most of their competitors using dated batch processing applications. 

What Makes PandioML Better than Scikit Learn?

Scikit Learn is a fantastic stack tool and is both used and beloved by many developers that work with Python. Still, objectively, PandioML is a far superior option when it comes to machine learning operations. 

There are many reasons why PandioML is superior to Scikit Learn when it comes to machine learning and artificial intelligence – mainly due to its far faster speed, fantastic ease of use, remote microservice creation, and centralization. 

Increased Speed

One of the things that developers dread is the speed at which the code compiles and executes. When it comes to machine learning, actually teaching the machine something can take an excessive amount of time. That isn’t solely down to the library or delivery system you’re using – there is the data itself and an abundance of other things.

The algorithm that you use to deliver data to the machine is a critical aspect, and PandioML dwarfs Scikit Learn in this regard. Pandit has incredible speeds that allow for a far smoother, more responsive, and faster data delivery and machine learning process. 

Ease of Use

Scikit Learn was built for hardcore developers with a background in deep learning, machine learning, and Python. To get the most out of Scikit Learn, you’ll need to know a lot about Python and programming in general, especially when it comes to machine learning.

With PandioML, the situation is far simpler. Everything is far easier to operate and integrate into existing frameworks, and the operator needs little to no deep learning experience to deploy models. PandioML brings cutting-edge adaptive machine learning solutions to those programmers that are comfortable in Python even if they are not familiar with the math behind the models.

Remote Microservice Creation/Deployment

PandioML has a unique system that allows you to create and deploy ML microservices remotely, which is an edge that isn’t present in any of its competition. A third-party provider can provide your company with machine learning microservice solutions, seamlessly integrating them within your existing framework from a remote location. 

The ability to create microservices that are ready with production scripts (do not require a DevOps team) results in the ability to build and deploy quickly models that are deemed production ready.  


Centralization is often used as a negative term, but this is one of the few cases where it makes sense. Scikit Learn is a collection of different tools, all of which have different features and use cases, and require a different approach – PandioML has every one of those features, improved, and all in one single easy-to-use app. 

Final Thoughts

Scikit Learn is a powerful and popular library that has enthralled and enlisted thousands of developers to build machine learning models.  Its development has led to countless innovative ideas and predictive capabilities in the field of data science.  However, it does have limitations in terms of complexity and breadth of solutions that may be built with its library.  

PandioML is a new Python library that enables software developers and data scientists to build robust models leveraging over 40 different commonly used algorithms.  The library leverages adaptive machine learning, building models against a stream of data.  Not only are these models powerful in a real-time environment, they also can be a viable replacement for traditional machine learning. Users can build, train, and deploy with one pipeline in less than 30 minutes.  Use cases for PandioML include supervised learning, single and multiple output, concept drift detection, unsupervised learning, building pipelines, and deploying models. 

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