ML and Cloud: A Better Love Story than Twilight!

Its been a few years now, people have been talking about machine learning, as it promises a wide range of benefits that can affect every aspect of human life. Efforts are being made to develop machine learning to a point where no human intervention is required. This piece of artificial intelligence, which uses machine learning models to learn from data, is seen as the bright future of science — the next stage of the evolution of machine learning automation.

Combined with the power of cloud computing, machine learning is even more rewarding. This integration is called an intelligent cloud image.

Let’s find out, What is Machine Learning?

In short — Machine learning (ML) is about the study of algorithms that have the ability to learn through algorithms and, in turn, make predictions against patterns of data. This is an excellent alternative to developing standard program algorithms and making data-driven motivations or decisions that will improve over time without human intervention and additional programming.

Benefits of ML in Cloud Computing

• The cloud pay-as-you-go model is useful for explosive AI or machine learning workloads.
• The cloud makes it easy for companies to test and measure machine learning capabilities as projects go into production.
• The cloud provides access to intellectual abilities without the need for advanced skills in AI or data science.
• AWS, Azure, and GCP offer many machine learning options that do not require in-depth knowledge of AI, machine learning theory, or team of data scientists.

There is no need to use a cloud provider to create machine learning solutions. After all, there are abundance of open-source machine learning frameworks, such as TensorFlow, MXNet, and CNTK, that companies can run on their hardware. However, companies developing sophisticated machine learning models at home may run into difficulties in measuring their workloads because real-world models usually require large compute clusters.

Bringing machine learning capabilities to enterprise applications are some barriers and high on many fronts.

Integrating ML abilities into enterprise applications has its own challenges. The specialized skills required to create, train, and deploy machine learning models and computational and special-purpose hardware, not to forget the high cost of labor, development, and infrastructure.

The leading public cloud platforms aim to simplify machine learning capabilities for enterprises to solve business problems without the technical burden. As AWS CEO Andy Jazzy highlighted in his 2017 re:Invent keynote addressed, his company needs to “solve the problem of accessibility of everyday developers and scientists” to enable AI and machine learning in the company.

Perhaps most importantly, advanced skills in artificial intelligence or data science make the cloud accessible to people without the need for it — skills are scarce and in short supply. A study by Tech Pro Research found that just 28% of companies have some experience in AI or machine learning, and 42% say their company’s IT staff lack the skills necessary to implement and support AI and machine learning.

ML’s Impact on Cloud

Cloud computing provides two basic prerequisites for running an AI system efficiently- scalable and cost-effective resources (mainly computing and storage) and processing power to suppress large amounts of data. First, it provides scalable, low-cost computing, and second, it is a great way to store and process large volumes of data. Therefore, combining the cloud with machine learning benefits both of these categories. The impact of machine learning on the cloud is the largest of the following:

1 — Chatbots (Personal Assistance)

Chatbots have become popular as a customer support service and personal assistant. Apple’s Siri, Microsoft Cortana, and Google Assistant are examples of this. These are built using NLP and ML technologies. These personal assistants have limitations in their abilities. The responses of these assistants are pre-planned and generalized. Imagine what would happen if chatbots and virtual assistants collaborated with the cloud. They have access to a massive amount of data. There will not be much difference in human and mechanical interactions.

With the cloud’s mass data, machine learning capabilities, and its cognitive computing feature, as mentioned above, personal help can transform any human interaction. The fantasies of owning computer systems, such as sci-fi or superhero movies, become a reality.

Such skills can be of great help in running big businesses. Imagine a system where you can access all the information about your past transactions, analyze current sales, and make predictions about future profits! Additionally, it tells you where the function went wrong and what can be done to fix it. Control is still in the hands of man. All systems process all information and provide possible solutions.

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2 — Cognitive Computing

A large amount of data is stored in the cloud. These data serve as a source for machine learning algorithms. Billions of people use the cloud for networking, storage, and data sharing. By using its own capability, ML algorithms can learn from the information and get better over time. These applications can perform cognitive processes and predict outcomes. Artificial intelligence in cognitive computing has led big players in research technology to excel in cloud computing. Some examples are AWS, IBM Watson, and Microsoft Cognitive AI.

The evolution of cognitive computing is at an early stage and can only be performed with not-so-important and straightforward tasks. But with technological advances in AI and machine learning, these systems will find applications in banking and finance, healthcare, marketing, and many other vital sectors.

3 — Business Intelligence (BI)

Business intelligence services are also becoming smarter by applying machine learning in cloud computing. Machine Learning and Cloud Computing help business intelligence companies by manipulating real-time data, analyzing it, and making future predictions. It enables you to create an interactive dashboard that displays data from different dimensions in one place. The collaboration of machine learning algorithms with cloud computing helps to improve the current state of business intelligence systems.

Businesses want their BI to be active, not just crushing numbers. Predictions for current trends and recommendations for action should be developed by the BI to make things easier for leaders. Machine Learning Business Intelligence can help you achieve that goal.

4 — IoT

The possibilities for IoT are endless. From self-driving cars to smart homes to real-time crash forecasts, IoT is working on combining everything into one web. Once the links and interconnected, a large amount of data is generated. IoT works best with machine learning, stored in the cloud.

The Internet of Things is to get better as time passes. With machine learning, computer users can detect and fix problems before they do. Warnings about any malfunctioning device may be issued before defective pieces can affect the entire system. Also, some processes can be automated based on previous actions, which eliminates the inconvenience in the service.

5- AIaaS

AI is being offered as a platform by cloud providers via open source platforms. It provides a set of AI tools for the functionality that users need. AIaaS is said to be a delivery model that provides quick and cost-effective AI solutions rather than consulting many AI professionals to complete a task.

AI as a platform service, simplifies the process of intelligent automation for users who do not want to get into trouble with functionality. This will further enhance the capabilities of cloud computing and increase the demand for the cloud instead.

Conclusion

ML technologies and cloud computing are changing the world. However, this is only the beginning and it will take some time to become fully operational and to be used in important sectors such as healthcare, business and banking. Machine learning makes it easy to manipulate data in the cloud. With a series of artificial intelligence research on cloud computing, cloud computing is becoming more and more intelligent. Machine learning will become more important in the cloud, and every cloud will use machine learning.