Machine Learning and Cloud Computing - Javatpoint
In this technology-driven time, Machine Learning and Cloud Computing are the most powerful technologies worldwide. Both these technologies play a crucial role for small and big organizations to grow their businesses.
Machine Learning helps users make predictions and develop algorithms that can automatically learn by using historical data. However, various machine learning algorithms such as Linear Regression, Logistic Regression, SVM, Decision Tree, Naïve Bayes, K-Means, random forest, Gradient Boosting algorithms, etc., require a massive amount of storage that become pretty challenging for a data scientist as well as machine learning professionals. Cloud computing becomes a game-changer for deploying machine learning models in such situations. Cloud computing helps to enhance and expand machine learning applications. The combination of machine learning and cloud computing is also known as intelligent Cloud.
This article will discuss machine learning and cloud computing, the advantages of ML using the Cloud, applications of ML algorithms using Cloud, and much more. So, let's start with a quick introduction to Machine Learning and Cloud computing.
Machine Learning is an Artificial Intelligence (AI) application that allows machines to learn and improve from experience automatically. Machine Learning can be classified as follows:
The primary aim of Machine Learning is to provide the capability to computers learn automatically without human intervention or assistance and adjust actions accordingly.
Cloud computing is defined as the outsourcing technology of computer software, which enables us to access applications and data remotely. It does not require any software installation and storage in your computer hard drive. Only you have to sign up to enjoy the services online.
Cloud computing is mainly categorized into three types as follows:
Although cloud computing and machine learning are emerging technologies, machine learning is comparatively new. Both technologies play important roles in companies' growth, but they become more powerful together. Machine learning makes intelligent machines or software, and on the other hand, cloud computing provides storage and security to access these applications.
The main connection between machine learning and cloud computing is resource demand. Machine learning requires a lot of processing power, data storage, and many servers simultaneously to work on an algorithm. Then Cloud computing plays a significant role in providing new servers with pre-defined data and changing resources over the Cloud (internet). Using cloud computing, you can spin up any number of servers you want, work on the algorithm, then destroy the machines again when complete.
Cloud Computing is primarily used for computation purposes, machine learning needs a lot of computational power to create sample data, and not everyone has access to many strong machines. Machine learning finds (sometimes) task scheduling and storage in cloud computing.
Although machine learning and cloud computing have their advantages individually, together, they have 3 core advantages as follows:
There are so many cloud service providers that offer lots of ML technologies for everyone without having prior knowledge of AI and ML.
Although there are so many cloud computing platforms available on the internet, few of them are most popular for machine learning. Let's discuss them in detail.
Amazon Web Services (AWS) is one of the most popular cloud computing platforms for Machine Learning, developed by Amazon in 2006. There are so many products provided by AWS as follows:
Microsoft Azure is also a popular cloud computing platform offered by Microsoft in 2010. It is popular among data scientists and machine learning professionals for data analytics requirements.
There are some Microsoft Azure products available for machine learning as follows:
Google Cloud or Google Cloud Platform is a cloud computing platform that is a subsidiary of Tech Giant Google developed in 2008. It provides its infrastructure to customers for developing machine learning models overcloud.
There are a few Google Cloud products available for machine learning as follows:
IBM Cloud (formerly known as Bluemix) is also one of IBM's most popular open-source cloud computing platforms. It includes various cloud delivery models that are public, private, and hybrid models.
There are a few IBM Cloud products available for machine learning as follows:
IBM Watson Studio: This product helps develop, run, and manage machine learning and Artificial Intelligent models.
IBM Watson Natural Language Understanding: It helps us analyze and classify text in NLP.
IBM Watson Speech-to-Text: As the name suggests, this product is responsible for converting speech or voice instructions into text format.
IBM Watson Assistant: This product is used for creating and managing the personal virtual assistant.
IBM Watson Visual Recognition: it helps machine learning search visual images and classify them.
IBM Watson Text-to-Speech: This product is responsible for converting text or written instructions into voice format.
We have discussed various cloud computing platforms used in machine learning. These cloud platforms offer machine learning capabilities and provide support for three types of predictions as follows:
Binary Prediction:
In this type of machine learning prediction, we get responses either as true or false. Binary predictions are useful for credit card fraud detections, order processing, recommendation systems, etc.
Category Prediction:
These machine learning predictions are responsible for categorizing a dataset based on experience. 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. It helps to predict the future sale of products in a manufacturing industry.
Cognitive computing is a special type of technology that works on the principle of artificial intelligence and signal processing to reflect human actions. In cognitive computing, a large amount of data is used to train a machine-learning algorithm. When cloud and machine learning technologies are used together, it is called cognitive Cloud, which can be used to access cognitive computing applications.
Cognitive Cloud is considered as a self-learning process that performs human-like tasks without human intervention. It uses various machine learning algorithms such as neural networks, pattern recognition, Natural language processing, data mining, etc., to perform human-like actions. It can be applicable in several industries such as retail, logistics, banking & finance, power & energy, cyber security, healthcare, education, and many more.
Business intelligence primarily focuses on improving and making better decisions making for businesses. Machine learning is a process of automated decision making, and on the other end, business intelligence is used to understand, organize and improve that decision making. Further, cloud computing deals with a large amount of data used to train machine learning models; hence business intelligence becomes important to store raw data. Further, this unstructured data is transformed into a structured format using manipulation, transformation, and classification techniques. These structured data sets are referred to as data warehouses.
Business analysts work on exploring structured data sets using some data visualization techniques. These techniques are used to create visual dashboards, which help in understanding information to others. The panels help to analyze and understand past performance and are used to adapt future strategies to improve KPIs (Key Business Indicators).
Internet of Things (IoT) is a platform that offers cloud facilities, including data storage and processing through Internet. Recently, cloud-based ML models are getting popular. It starts with invoking input data from the client end, processes machine learning algorithms using artificial neural network (ANN) over cloud servers, and returns with output to the client again. During this scenario, the client's sensitive information can be stored on the server, raising privacy issues and making users reluctant to use the services.
Cloud computing is the easiest method to process bulk data packages generated through IoT over the internet. It is generally used for real-time project scenarios as an event processing engine. It worked as a part of the collaboration and was used to store IoT data and can be accessed remotely. E.g., when IoT is integrated with personal devices, it can fetch the booking status of your bus and train reservations and rebook these tickets for passengers whose trains got delayed or canceled.
Personal virtual assistant becomes mandate for developing an organization's business as it provides support to their customer like a human. Nowadays, all industries such as banking, healthcare, education, infrastructure, etc., are implementing these chatbots or personal virtual assistants in their businesses to perform multiple tasks.
Although they are still in their developing phase and require more improvement, they still reduce the burden to resolve common customer problems using some frequently asked questions. Cortana, SIRI, and Alexa are such most popular chatbots.
Nowadays, all big cloud companies are providing AI facilities using AI-as-a-service platforms. Open-source AI functionalities are quite cheaper when deployed in cloud. These services provide Artificial Intelligence and machine learning functionalities, and build the capacity of cognitive computations and make the system more intelligent. It helps to make the system relatively fast and efficient.
Machine Learning with cloud computing is very crucial for next-generation technologies. The demand for machine learning is continuously increasing with cloud computing as it offers an ideal environment for machine learning models having a large amount of data. Further, it can be used to train new systems, identify the pattern, and make predictions. The Cloud offers a scalable, on-demand environment to collect, store, curate, and process data.
As well, all cloud service providers realize the importance of machine learning in the Cloud; it is increasing the demand of Cloud based ML models to small, mid, and large organizations. Machine learning and cloud computing are mutually exclusive to one another. If machine learning helps cloud computing to make more enhanced, efficient, and scalable, then on the other end, cloud computing also expands the horizon for machine learning applications. Hence, we can say Ml and cloud computing are intricately interrelated and used together; they can also give tremendous results.
Splunk
SPSS
Swagger
Transact-SQL
Tumblr
ReactJS
Regex
Reinforcement Learning
R Programming
RxJS
React Native
Python Design Patterns
Python Pillow
Python Turtle
Keras
Aptitude
Reasoning
Verbal Ability
Interview Questions
Company Questions
Artificial Intelligence
AWS
Selenium
Cloud Computing
Hadoop
ReactJS
Data Science
Angular 7
Blockchain
Git
Machine Learning
DevOps
DBMS
Data Structures
DAA
Operating System
Computer Network
Compiler Design
Computer Organization
Discrete Mathematics
Ethical Hacking
Computer Graphics
Software Engineering
Web Technology
Cyber Security
Automata
C Programming
C++
Java
.Net
Python
Programs
Control System
Data Mining
Data Warehouse
JavaTpoint offers too many high quality services. Mail us on [email protected], to get more information about given services.
JavaTpoint offers college campus training on Core Java, Advance Java, .Net, Android, Hadoop, PHP, Web Technology and Python. Please mail your requirement at [email protected]
Duration: 1 week to 2 week
Website Development
Android Development
Website Designing
Digital Marketing
Summer Training
Industrial Training
College Campus Training
Address: G-13, 2nd Floor, Sec-3
Noida, UP, 201301, India
Contact No: 0120-4256464, 9990449935
© Copyright 2011-2021 www.javatpoint.com. All rights reserved. Developed by JavaTpoint.
