Why is cloud computing important for machine learning?

Artificial intelligence and cloud computing have merged to improve the lives of millions. There are many platforms that provide various products for Machine Learning ranging from natural language processing, service bots, and even deep learning. So in this article, let’s see why cloud computing has become so important in machine learning these days.

AI in cloud computing is the combination of artificial intelligence with cloud-based computing environments, making intuitive, connected experiences possible. Some of the examples like Siri, Amazon Alexa, and Google Home combine a seamless flow of artificial intelligence technology and cloud-based computing resources to enable users to make purchases, adjust a smart thermostat, or hear a favourite song instantly.

Machine learning – a branch of AI is really about the study of algorithms that have the ability to learn through patterns and, based on that, make predictions against patterns of data. It’s a better alternative to leveraging static program instructions and instead making data-driven predictions or decisions that will improve over time without human intervention and additional programming.

One of the concerns, as machine learning becomes more affordable through the use of cloud platforms, is that the technology will be misapplied. This already seems to be a pattern, as cloud providers promote machine learning as having wide value. However, that value won’t be realized if machine learning is applied to systems that can’t benefit from making predictions based on patterns found in data.

• The cloud’s pay-per-use model is good for bursty AI or machine learning workloads.
• The cloud makes it easy for enterprises to experiment with machine learning capabilities and scale up as projects go into production and demand increases.
• The cloud makes intelligent capabilities accessible without requiring advanced skills in artificial intelligence or data science.

You don’t need to use a cloud provider to build a machine learning solution. After all, there are plenty of open source machine learning frameworks, such as TensorFlow, MXNet, and CNTK that companies can run on their own hardware. However, companies building sophisticated machine learning models in-house are likely to run into issues scaling their workloads, because training real-world models typically requires large compute clusters.

The barriers to entry for bringing machine learning capabilities to enterprise applications are high on many fronts. The specialized skills required to build, train, and deploy machine learning models and the computational and special-purpose hardware requirements add up to higher costs for labor, development, and infrastructure.

These are problems that cloud computing can solve and the leading public cloud platforms are on a mission to make it easier for companies to leverage machine learning capabilities to solve business problems without the full tech burden.

Machine Learning is not rocket science! But it may appear like it for smaller inexperienced companies that are not familiar with the demands and requirements of a machine learning model. But for these companies, Cloud Computing comes to the rescue. In fact, most companies these days use some sort of cloud computing web services to use Machine Learning for a fee so that they can focus on their core business and not spend many finances on cultivating their own machine learning infrastructure from scratch.

Machine Learning is the most important technology in these times. Naturally, all companies these days want to use Machine Learning to improve their business. Machine Learning and Data Analytics are used by companies to better understand their target audience, automate some of their production, create better products according to market demand, etc. All of these things in return increase the profitability of a company which in turn gives them an edge over their competitors. After all, the bottom line in most cases is profit!

However, for a long time in the past, companies needed to invest a lot of money in Machine Learning to get this profit. Machine Learning required a lot of infrastructures, programmers who were familiar with ML, and data analytics were expensive and there was very little data available to feed these machine learning algorithms! While this was not that big a deal for large multinational corporations, it was very difficult for small and mid-level companies. But the popularity and advancement of cloud services have made everything much easier. Now companies can access Machine Learning algorithms and technologies from a third-party vendor, made a few changes according to their custom requirements are start getting the benefits with a much smaller initial investment.

This is why Cloud Computing is so important in Machine Learning! This is the solution for many smaller and mid-level companies that don’t want to build, test, and implement their own machine learning algorithms from scratch. These companies can focus on their core business and obtain value addition from Machine Learning without needing to become experts. So they get increasing profits while decreasing their risk of investment which means it’s a win-win situation for all!

As more cloud services providers and businesses realize the potential of Machine Learning in the cloud, it will spur the demand for Cloud Machine Learning platforms. While ML makes cloud computing much more enhanced, efficient, and scalable, the cloud platform expands the horizon for ML applications. Thus, both are intricately interrelated, and when combined into a symbiotic relationship, the business connotations can be tremendous.

Source: cloudacademy.com, techbeacon.com