Machine Learning (ML), a subset of Artificial Intelligence (AI), aims to create systems/machines that can automatically learn from data patterns and through experience and improve continually at their predictions, without being explicitly programmed. Essentially, Machine Learning involves the study of algorithms and the development of computer programs that can access data and use it to train themselves.
However, applying machine learning algorithms (decision tree, logistic regression, linear regression, SVM, KNN, etc.) to massive amounts of data raised can be pretty challenging for ML practitioners. Since traditional ML libraries do not support the processing of massive datasets, new and innovative approaches were required.
Furthermore, ML was way beyond the reach of small and medium-sized enterprises – it was a costly affair to leverage and implement ML technologies and solutions in the business infrastructure. Enter – the cloud. You may ask why machine learning in the cloud?
Machine Learning applications can be enhanced and expanded when coupled with the cloud. The integration of Machine Learning in the cloud is termed as the “intelligent cloud.” While the cloud is primarily used for computing, networking, and storage, with Cloud Machine Learning, the capabilities of both the cloud and ML algorithms will increase significantly.
For instance, Machine Learning is intrinsically a time-consuming task, but with the cloud computing paradigm, ML tasks can be sped up to a great extent. Consequently, even popular Statistics tools such as R, Octave, and Python, too, transitioned into the cloud.
Today, most cloud providers offering ML capabilities, including the top-leaders in the cloud business – AWS, Google, and Microsoft – provide support for three types of predictions:
- Binary prediction – This type of ML prediction deals with “yes” or “no” responses. It is primarily used for fraud detection, recommendation engines, and order processing, to name a few.
- Category prediction – It this type of prediction, a dataset is observed and based on the gathered information from it, the dataset it placed under a specific category. For instance, insurance companies use category prediction to categorize different types of claims.
- Value prediction – This type of prediction finds patterns within the accumulated data by using learning models to show the quantitative measure of all the likely outcomes. Companies use it to predict a rough number of how many units of a product will sell in the near future (e.g., the next month). It allows them to shape their manufacturing plans accordingly.
What are the advantages of Machine Learning in the cloud?
Here are the 3 core advantages of Cloud Machine Learning:
- The cloud makes it possible for companies/enterprises to experiment with ML technologies and scale up as and when need as projects go into production and the demand increases.
- The pay-per-use model of cloud platforms presents an affordable solution for companies who wish to leverage ML capabilities for their business without spending a ton of money.
- With the cloud, you don’t require advanced Data Science skills to access and leverage various ML functionalities.
Applications of Machine Learning Algorithms using the Cloud
1. Cognitive Cloud
The cloud stores massive amounts of data which becomes the source of learning for ML algorithms. Since billions of people around the globe use cloud platforms to store data, it presents a wonderful opportunity for ML algorithms to leverage that data and learn from it. It other words, ML algorithms can shift the cloud paradigm from cloud computing to cognitive computing.
Cognitive computing pertains to technology platforms that are designed on the principles of AI and signal processing. It incorporates machine learning, natural language processing, speech/object recognition, human-computer interaction, and narrative generation. When infused with ML capabilities, the cloud becomes “Cognitive Cloud” that can make cognitive computing applications accessible for the common mass.
IBM Cognitive and Microsoft’s Azure Cognitive Services are excellent examples of this – these platforms allow you to develop intelligent apps without any hassle.
2. Chatbots and Smart Personal Assistants
Chatbots and personal assistants have taken over both the individual and business landscape. Smart virtual assistants like Siri, Alexa, and Cortana can perform an array of tasks for you and even interact with you like another human being. However developed they might be, chatbots and virtual assistants are still at their nascent stage. They are still evolving, still learning. Hence, it is natural for them to have limitations.
When integrated with the cloud, chatbots and smart personal assistants will have a vast pool of data at their disposal to learn from. As a result, their learning capabilities will get a considerable boost. With time, chatbots and personal assistants will evolve to completely do away with any form of human intervention or support.
3. IoT Cloud
IoT Cloud is a cloud platform specifically designed to store and process the data generated by the Internet of Things (IoT). Salesforce’s IoT Cloud is powered by Thunder – a “massively scalable real-time event processing engine.“
IoT Cloud can intake colossal amounts of data generated by connected devices, sensors, applications, websites, and customers and trigger actions for real-time responses. It can be used for various real-world scenarios. For instance, by connecting to personal devices at use, IoT could know the status of flights and rebooking flight tickets for passengers whose flights got delayed or cancelled.
4. Business Intelligence
Thanks to Machine Learning cloud computing, business intelligence (BI) services are also becoming increasingly intelligent. Cloud Machine Learning has two-fold benefits for BI. While the cloud platform can store vast volumes of customer and company data, ML algorithms can process and analyze that data to find innovative solutions.
With the customer data at hand, ML algorithms can help businesses gain a more in-depth and better understanding of their target audience – purchasing behavior, preferences, needs, pain points, etc. Accordingly, companies can create product development and marketing strategies to boost sales and increase ROI.
Another area where ML has a significant bearing is customer experience and satisfaction. As businesses understand their customers better, they create products that can address their pain points and needs. This leads to higher customer satisfaction. Also, ML algorithms can create intuitive recommendation engines and chatbots for better customer experience.
This is just one facet of how the combination of Machine Learning algorithms and cloud computing is improving the BI systems.
Today, many cloud services providers are offering AI capabilities via open-source AI-as-a-Service (AIaaS) platforms. This is a highly cost-effective model of deploying AI functionalities to businesses, particularly small and medium-sized firms that are restrained by financial limitations.
AIaaS offers customers a host of AI tools and functionalities required for AI/ML model building, intelligent automation, cognitive computing, and much more. Needless to say, AIaaS makes everything super-fast and efficient. The program that upGrad has launched can be of great help to understand Machine Learning and Cloud better. The program is an Advanced Certification Program in Machine Learning & Cloud with IIT Madras
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.
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