What is the Impact of Machine Learning on Cloud Computing?

With the increasing demand for cloud computing in today’s modern world due to its massive benefits to businesses, it is important not to overlook its effect on different aspects. One such aspect that has a considerable impact on technology and our day-to-day lives is Machine Learning.

Machine learning is basically an algorithm where the machine changes itself from being entirely dependent on humans to take its own decisions. It uses data to build models which can be used for predictive analysis and decision making. 

Three key concepts in Machine Learning are Supervised Learning, Unsupervised Learning and Reinforcement Learning. In this blog, we’ll discuss how Cloud Computing fits into this picture?

To begin with, it is estimated that machine learning algorithms will power almost 95% of all customer interactions by 2025.” Machine Learning is gaining popularity day by day, with almost every application taking its help into account while making critical decisions that can have a huge impact on output. 

Already, more than 70% of the applications we use on our day to day activities utilize at least one form of AI which is just indicative of how these technologies are growing in dominance. Machine learning helps you find patterns based on the data fed into it, making your decision making more accurate than just depending on humans who are bound to make mistakes.

Why Cloud Computing?

What if we combine these two and apply Machine Learning in cloud computing? We’ll see a massive impact on the way we look at the world. 

Let us take an example of image recognition, which is done with the help of Deep Neural Networks (DNN). Ideally, they are one such technology that can change our lives if used properly. 

These neural networks help machines learn from data using multiple layers of processing nodes. Each layer identifies different features in input data and eventually sends them to the next layer and so on until output is generated. 

To make this happen, there is a considerable amount of pre-processing involved followed by hours of manual analysis which requires high computational power and storage. If you’re involved in the manual analysis, you need to devote time to this. A rather common approach we’ve seen conscious students take is to get an essay writer for their assignments so they can fully focus on the analysis.

However, what if we make this whole process cloud-based? What if we let the machine take over the analysis of data and help us make a final decision with its own learned model? This not only saves our time but also makes the output more accurate as compared to manual analysis. 

The machine does the learning on its own, which makes it truly universal. How’s that for a revolution?

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Machine Learning + Cloud Computing = Perfect Combo

The demand for Big Data and Machine learning has increased significantly in various verticals such as Banking & Financial Services, Web/Online services, Media & Entertainment etc. When looking from a broader perspective, these two technologies have a high potential to shape how businesses work. 

For example, developing an algorithm of sentiment analysis can be extremely useful for social media monitoring of firms, which will help them to build their brand image among potential customers. Also, many companies use machine learning algorithms to automate their business processes, analyze data, and provide intelligent insights. 

For example, some central banks have developed machine learning algorithms to detect fraud, while some airlines are using these algorithms to predict flight delays. Last but not least; Cloud computing is becoming more important for machine learning implementation by making it possible to access a vast amount of resources on-demand with minimum cost. Many businesses, especially startups, can easily manage large clusters of virtual machines they want to use for their Machine learning applications via cloud services like Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure, etc.

Cloud Computing Aspects Impacted by Machine Learning

We talked about how Machine Learning can transform our lives if we apply it in cloud computing. Now, let’s talk about what aspects of Cloud Computing will be impacted by this:

• Storage: With the widespread adoption of sensors and smart devices, a massive amount of data is generated every day. All this data has to be stored somewhere so that it can be used for training the model, which makes machine learning possible. In most cases, storing enormous amounts of data is almost impossible due to the limited storage space on an average device. In such situations, cloud computing comes into play where you don’t have to worry about storage at all as cloud providers take care of that aspect. This also helps with scaling up or down based on demand which ensures that you don’t end up spending more money than you need to.
• Processing: As mentioned earlier, machine learning is a very resource-intensive process, and for this, we need computational power, which can either be on-premise hardware or cloud computing machines with high processing capability. It all depends on the amount of data that needs to be processed and its size. There are cases where there is so much data that just training your model alone would take months before you can even use it for making decisions. This requires having multiple levels of parallelism to get things done quickly. Cloud computing allows you easy access to such resources, which can boost your productivity manifold. Students who are machine learning enthusiasts get most of their work taken care of by college paper.org reviews so this is also something you may want to consider if you feel pressed for time.
• Machine Learning Pipelines: Once we have set up our models using an ML framework like Tensorflow, multiple pipelines need to be set between the data collection process and model training, which makes it work effectively. We select our input features based on how they can help us make the most accurate predictions possible. The output of this step is a pre-trained machine learning model that needs fine-tuning before we finally use it for making decisions. This requires having lots of tools, libraries, sub-models, which need to go into lengthy setup processes with multiple iterations until you get your desired results. This is where cloud computing really shines as all these tools are available as APIs (Application Programming Interfaces) so they can easily fit into your workflow without wasting time or effort.
• Machine Learning Training: There is one more critical aspect of the Machine Learning process that requires a huge amount of processing power, and that’s model training. Many data scientists prefer to use GPUs (Graphics Processing Units) since it provides an excellent performance speed up due to their parallel nature of the calculation. However, running large clusters of GPUs is not an easy task in itself since you need to have high bandwidth between each node, which means you will need even more powerful CPUs if your goal is harnessing all their power. Cloud computing comes into play again when you pay for what you use, so there are no upfront costs or long term commitments if you don’t want them. You can quickly scale your cluster based on your current needs but be sure you won’t go beyond what your providers offer in terms of bandwidth.

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Final Words

As discussed before, if we make the whole process cloud-based, it can lead to tremendous advancement in almost every field of life. Even though there is still some time needed before things become a reality, this combination would be a game-changer in today’s world where everything is going digital.