Machine Learning: How to build a scalable machine structure

Complex techniques and being proficient and well-versed with each concept are all a part of Machine Learning. Here's an informative guide on constructing a scalable machine structure.

Machine Learning: How to build a scalable machine structure
Scalable Machine learning structure
Machine Learning: How to build a scalable machine structure

Machine Learning: How to build a scalable machine structure

In order to build a scalable machine structure, clarity about the term itself is very essential. Scalable Machine Learning Structure refers to the combination of Systems, Data Mining, Machine Learning, and Statistics, to analyze large amounts of data effectively.


By the term “scalable,” it means that your current architecture is capable of meeting the existing needs when the workload is highly increased all of a sudden. In order to meet the increased workload, you can scale up your systems (hardware + software combination), without compromising on the quality of the work.


Need of Scalability in Machine Learning

Machine learning is no longer a new term in today’s world. Everyone is aware of its wide-ranging applications and advantages in the modern era. The large amounts of data that it deals with cannot be directly fed into the systems for processing and doing image processing, playing games, or generating face recognition applications. One reason is the quantity of data and the second is the need for such high processing units to process it several times efficiently.

This is where Scalability comes into the picture. The increase in the quantity of internet, hardware, and DevOps has led to a huge rise in the need for machine learning algorithms which are one step ahead. Scalability has contributed to productivity, portability, enhanced creativity, minimized human involvement, and undoubtedly to cost reduction as well. Scalability can bring a huge difference between a good machine learning structure and a better one.


A Quick Glance at the Machine Learning Process

To begin with, we analyze and discuss the problem statement and its domain. This step involves analyzing the inputs and outputs for the solution that we are aiming for.


Next, we try collecting and storing the relevant data as per the problem we aim to solve. The amount of data to be collected depends on the difficulty of the problem.


Now modeling (training) is done once some analysis of the collected data is done. Statistical analysis of the data, handling the missing values, etc. needs to be done here. Once you get the best possible test case for your problem, then comes the testing and evaluation.


Although going back and forth is involved between testing and modeling to get the preferable results. Once that is done, then comes the final stage i.e. deploying. The process of evaluation, experimentation, and deployment is somewhat a process that occurs continuously but yields greater results.


How to construct a Scalable Machine Structure?

Now that we have good knowledge about the requirement for scalability, we can dive into the detailed discussion about building a scalable machine learning structure. There are a few simple steps that can make the entire process very convenient and easy-going for you.


Choosing the appropriate Language/Framework - There are various options available in the market for the right ML framework from which you can make your choice. There are several options available in languages such as Java, Python, C, or C++, etc. You can choose between any of the languages depending upon the key feature you want to maintain in your model and depending upon the abstraction you desire. Apart from the different languages, Caffe, Pytorch, MXNet, TensorFlow, and Keras are some popular deep learning frameworks in the market.


Selecting the Right Processors - You can make your decision between CPUs, GPUs (or Graphics Processing Units), TPUs commonly known as Tensor Processing Units, and ASICs i.e. Application Specified Integration Chips. Long story short, CPUs are processors that are scalar while GPUs are vector in nature and ASICs like TPUs are matrix processors. As per your usage, you can make your choice easily.


Collection of Data - As discussed earlier, the proper collection and storing of relevant data is very essential for the correct functioning of algorithms. Along with this, the input and output hardware are also very important for machine learning models. Input pipelining consists of extraction, transformation, and loading. Extraction relies on the I/O hardware, transformation relies on CPUs while loading is all about ASIC/GPUs.


Training/Modeling - It greatly affects the scaling process of the structures. It helps in enhancing the cost, time, and performance of our systems. Distributed Machine Learning has its own importance. Hadoop, Apache Spark, and Mahout are some of the famous frameworks for the same.


Resource Optimization and Monitoring - When we invest while training and aiming for scalability, it becomes very essential to make the optimum utilization of the resources with continuous monitoring on all the processes. With this, resource allocation can be done in the best way possible.


Deploying - It is the last step in which the model is put out for use in the real-world environment. You may opt for cloud web service providers like Amazon, Azure, etc at very reasonable rates.



Scaling may face certain challenges such as data management because of the interactiveness of the data. Model Training involves the application of mathematical computations and algorithms over the data again and again, which makes the entire process somewhat cumbersome. The process of testing and deployment in continuity also calls for a lot of time, money, and effort. But one cannot ignore the need for Scalability in machine learning models.


Our above discussion gives an overview of building a scalable machine structure. Thus, it is very vital that with the changing needs of the hour, our systems should also change effortlessly as per exigency.

You can also know about : Future of Machine Learning After a Few Years!