Machine Learning Frameworks at Your Fingertips

Machine Learning is undoubtedly the hottest technology and is being used in almost every domain, starting from healthcare, security, defense, entertainment, recommendation systems, and many more.    

 

But starting machine learning is a mess as it comprises heavy pre-computations necessary for the model's surroundings, as well as it is a bit confusing because every model requires different surroundings, which is impossible for someone to generate.

 

Machine Learning Frameworks provide us with a one-stop solution to this problem as they facilitate the developer with all the required surroundings or interfaces.

 

Let us have a deeper look into what a framework provides us and why it is the prerequisite for starting machine learning.

 

Also Read | 11 Real-World Applications of Machine Learning

 

Table of Contents:

 

• What is Machine Learning Framework

• Why Machine Learning Frameworks?

• Types of Machine Learning Frameworks

• Selecting a framework

• Some popular frameworks

• Conclusion

 

What is Machine Learning Framework:

 

As described earlier, a machine learning framework refers to an interface or a pre-designed environment that helps in the easy deployment, implementation, training, or testing of any machine learning model. A Machine Learning Framework provides us with all the pre-computations a machine learning model needs and an interface that makes it easier for the developer to edit the model.

 

A machine learning framework can be seen as an interface, tool, or a library, but what matters is how it helps us.

 

It not only aligns the machine learning algorithms, but also it helps in speeding up the process of generating new deep learning models or editing them. Machine Learning Frameworks are very compatible because of which they are the go-through push before diving into deep learning models.

 

Why Machine Learning Frameworks?

 

Every machine learning model consists of different aspects that need different prerequisites, such as computing space, running space, computation requirements, etc.

 

A machine learning framework provides a basis on which every model can be started, every model can adapt, and moreover, every model can be built. A machine learning framework is required because it provides a clear, concise way of defining machine learning models using a collection of pre-built, optimized components.

 

A Machine Learning Framework facilitates the model with multiple advantages such as:

 

1. Improving its performance 

 

• Frameworks generally have an environment that helps the models yield better outputs, especially at the initial stages.

 

2. Optimizing its inbuilt functions

 

• Many models make a call for inbuilt library functions that are already pre-computed in the frameworks and hence make the functioning smoother.

 

3. Problem generation

 

• It suggests actions which could lead to new and informative experiences.

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Working of Machine Learning Framework

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Also Read | Machine Learning: Advantages and Disadvantages

 

Types of Machine Learning Frameworks

 

There are numerous characteristics on the basis of which a framework can be classified, such as on the basis of generation, it can be classified into 

 

1. Pre-fabricated

2. Modified or on the basis of use, they can be classified into 

3. Training

4. Intermediary

5. Deployment. 

 

But most widely, frameworks are classified on the basis of use, and we’ll now see what we mean by the three different types of frameworks.

 

1. Training Frameworks

 

Training frameworks are generally used by beginners to dive into the deep sea of machine learning. It provides all the basic facilities needed by a basic model so that it can be tested and enhanced.

 

Training frameworks, as the name suggests, are used for training purposes in which when we start a network, we have to back-propagate it for every computation and then forth-propagate it for the output of the computations.

 

For these reasons, these frameworks are pretty heavy because of the pre-computation they have to hold as well as finding the best path for propagating in and out of the network.

 

2. Intermediary Frameworks

 

Unlike the training frameworks, these frameworks provide the developer the mode in which they want to deploy their model. 

 

Intermediary frameworks are basically the junctions where models are made for a specifically low sample space like testing or when the developer does not want heavy computations on his/her model.

 

3. Deployment Frameworks

 

These are the most widely used frameworks, with nearly 90% of the community utilizing them.

 

The reasons for the immense love it has in the community are the benefits it provides developers over the other frameworks, such as:

 

• It has the best efficiency as it does not back-propagate the network, whereas it takes it from the deployed model.

• not as heavy as training and intermediary frameworks.

• heavily compatible.

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Types of Machine Learning Frameworks

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Selecting a Framework

 

When you dive into machine learning and start your implementation, frameworks become your necessity, and selecting the support system for your model becomes the highest priority.

 

Although selecting a framework is not as complex as it looks, this becomes the threshold for most beginners where they either take their chances or leave.

 

There are several measures to keep in mind while selecting a suitable framework, such as:

 

• Discovering your needs

 

Needs by any model refers to its description only, where we have to be specific about the usage, requirements, deployment, affordability, and many more factors of our model.

 

More specifically, we have to be sure about three questions about our model, i.e.,

 

1. Whether the model will be used for deep learning or classical machine learning algorithms,

2. What is the preferred language for our model?

3. What kind of hardware, software, and cloud services are needed by the model?

 

• Type of learning to implement

 

Another important measure for choosing our machine learning framework is the clarity, or the vision, behind the model.

 

Although deep learning algorithms are getting most of the attention nowadays, classic machine learning algorithms can prove to be a better option in many cases.

 

Deep learning algorithms can work with unstructured data whereas classical learning algorithms can only handle structured data.

 

Deep learning frameworks specifically have  support for coding neural networks which has been the sensation lately, and TenserFlow is the most commonly used framework falling under this category.

 

Classical learning algorithms are better in different kinds of optimizations, and statistical analysis. The most popular classic machine learning framework is Scikit-learn.

 

• Experimentation or large-scale deployment

 

In the early stages of development, AI engineers are likely to experiment to find what works and what doesn't work on small data sets. However, when you want to run a model in production against an entire data set, it makes sense to look at a framework that supports a distributed architecture, like Apache Spark's MLlib or H20.

 

In heavy lifting, scalability becomes the biggest concern.

 

• Parameter optimization

 

Another key consideration when choosing a machine learning framework is parameter optimization.

 

Every algorithm has its own time and space complexity, which in turn changes the overall parameters required by the model.

 

• Framework alternatives

 

One final question enterprises need to ask is whether a machine learning framework is even the best approach for a particular class of problems.

 

Also Read | How to select the best regression techniques in machine learning?

 

Some popular frameworks

 

There is an ocean of machine learning frameworks when we look into it but there are several frameworks that have outshined every other alternative.

 

Let us have a look at some of the most popularly used frameworks:

 

• TenserFlow

 

Based on JavaScript, TenserFlow is an open source machine learning framework with extensive APIs. TenserFlow can be used via script tags or via installation through npm.

 

It has enormous community support for every technical  aspect related to its usage or debugging.

 

• Shogun

 

Written in C++ (open-source and free), Shogun is useful for designing algorithms and data structures, especially in the fields of education and research. 

 

It also provides connectivity with other ML libraries, including LibLinear, LibSVM, and several others. 

 

It is heavily compatible as it supports many languages like, R, Python, Java, MatLab, C++ and other languages.

 

• Keras

 

Designed by François Chollet, Keras is an open-source framework that is written in Python and runs efficiently on CPUs and GPUs. 

 

Keras stands out in implementing neural networks.

 

• MXNet

 

MXNet is a choice of all Deep Learning developers. It supports scalability for a wide range of GPUs and programming languages. 

 

MXNet is customizable and portable and can wield algorithms that require long & short-term memory networks and convolutional neural networks. 

 

Its application spreads from transportation to healthcare systems to manufacturing and in various other fields.

 

• CNTK

 

Microsoft-owned CNTK is an open source Machine Learning Framework which is used to describe a neural network as a sequence of computational development steps by using directed graphs.

 

CNTK is best suited for large-scale, multi-dimensional, or sparse data sets from Python, C++, and BrainScript.

 

It also allows users to merge and view different models.

 

Also Read | 10 Applications of Machine Learning in Social Media

 

Conclusion

 

So far we have seen what we mean by Machine Learning Frameworks, what their needs are and how they meet our needs.

 

Now, let us have a glimpse  of what we have read.

 

• Every machine learning model consists of different aspects that need different prerequisites, such as computing space, running space, computation requirements, etc.

• A machine learning framework provides a basis on which every model can be started, every model can adapt, and moreover, every model can be built.

• As described earlier, a machine learning framework refers to an interface or a pre-designed environment that helps in the easy deployment, implementation, training, or testing of any machine learning model.

• A machine learning framework can be seen as an interface, tool, or a library.

• Training frameworks are generally used by beginners to dive into the deep sea of machine learning. It provides all the basic facilities needed by a basic model so that it can be tested and enhanced.

• Unlike the training frameworks, these frameworks provide the developer the mode in which they want to deploy their model. 

 

These are the most widely used frameworks, with nearly 90% of the community utilizing them.

 

We can see that in the growing favoritism of the technical community in machine learning, frameworks are the key for any individual or organization to prove their mettle.

 

We can also notice that without a framework, we cannot implement our machine learning model, irrespective of its size and mode of learning.

 

As frameworks are the building blocks for any model, we also come to the fact that choosing the right framework for our model is the most important thing in the process.

 

Moreover, a framework acts as a support system for our model, which has pre-coded modules for our models to test, enhance, and optimize their work easily without any extra effort by the developer.

 

This is all that makes a machine learning framework a necessity in machine learning or deep learning, one of the tech-sensations in today’s world.