What is Machine learning?
Machine learning is the branch of artificial intelligence that forecasts and predicts based on available data. In the first stage, the model is trained and learned using past data then the output is predicted from the data.
Example of Machine learning
Machine learning algorithms are used to predict the price of houses in real estate. At first, the model is trained from the past year pricing and features data of houses for a particular city, then in the second stage, the prices of houses are predicted using that learned model for the future.
Supervised Machine Learning
Supervised ML uses techniques of statistical method regression and classification. In regression, models are learned from past patterns to predict new events for the future.
Unsupervised Machine Learning
Unsupervised Machine learning is used when the input data is not labelled which means features of input data are not clear. Unsupervised learning works on the mixed data to find out similar patterns. Unsupervised ML algorithms include clustering and association.
What is MLOps?
MLOps or Machine Learning Operation management is the general practice of Machine learning modelling in which ML models are deployed in the production stage for better reliability and efficiency output at the production stage.
What is Cloud Computing?
Cloud computing is defined as a service to unlimited computational power, resources and memory without the interference of a user in a secured environment. Large clouds networks are distributed on different distributed data centres.
How does Machine Learning work with cloud computing?
Machine Learning, when integrated with cloud computing, is called an intelligent cloud. Cloud computing has increased the capacity, efficiency and speed of Machine learning systems by many folds. Cloud provides ML unlimited resources to deploy their model for faster processors, use a large chunk of memory for training and learning, efficient computation, quick networking and fast processing.
What is MLOps in cloud computing?
MLOps gives a framework and rules for deployment of ML models, but many companies or business machines can not have the required capacity for training large amounts of data, deployment of the model on that data and processing capacity. Cloud computing provides MLOps a secured platform to perform an enormous amount of processing, large data training, deployment initially and at the production stage.
Cloud Computing Importance in ML?
Larger amounts of data, the need for fast processing, dynamic systems and quick analytics make businesses move toward cloud computing. The resources needed to deploy ML models single-handedly by the businesses as data and processing speed is increasing over time. In the near future, every business will require to shift their work on the cloud due to large resources, security and fast processing of ML model deployment, training and production.
Used cases of ML in clouds?
Major companies which are pioneers in cloud computers and providing services are AWS Amazon Web Services, Google clouds, IBM Cloud, Ali Baba and Microsoft Azure:
Microsoft created Microsoft Azure for machine learning and data analytics. Some of their ML related products are
- Azure Cognitive Search for mobile and web applications
- Azure Machine Learning create and deploy ML models on the cloud
- Azure Cognitive Service create different innovative cognitive services
- Azure Data bricks provide big data Apache-Spark analytics
- Azure Bot Service design smart, learned and intelligent bot that can be learned further
AWS Amazon web service:
AWS provides different services related to ML in clouds, such as
- Amazon Sage Maker for training and fast deployment of ML models
- Amazon Augmented Artificial Intelligence for human review of ML models
- Amazon Personalize for personal recommendations of the ML system
- AWS Deep Learning AMI’s provide greater AI Deep Learning solutions
- Amazon Polly convert text into speech using AI ML algorithms
The Google Cloud started in 2008 and provided greater infrastructure of cloud and AI services to companies. Google Cloud provides ML related products are described below:
- Google AutoML is used for automatic deployment and testing of appropriate ML
- Google Speech-to-Text convert speech to text and it converts about 120-languages
- Google Text-to-Speech convert text to speech
- Google AI Platform used for designing, training, testing and managing ML models
- Google Natural Language is used for analyzing, training and classifying text
8 major benefits of Machine learning in cloud computing?
There are enormous benefits of ML in clouds and in short, cloud computing provides ML algorithms with larger capacity, fast computing power and larger storage of the system to solve bigger problems. Some of the benefits are discussed below
- Power to Business Intelligence (BI): Business intelligence uses cloud computing to analyze real-time data and make future predictions with fast processing speed and higher computer resources.
- 0 Industrial Revolution/Automation: The fourth Industrial Revolution is based on automation principles by using thousands of IoT internet-based devices and their data analytics in seconds for plant operation and production. This larger amount of data and higher computation power in bigger industries can only be handled using clouds for processing in seconds.
- Powering Capability of Virtual Assistant: Previous Chatbots which worked on the server data of companies faced difficulties when a user asked some questions whose answers were not in the limited capacity of servers. The cloud provides chatbots unlimited storage and processing power to answer anything users ask.
- Efficient and Cheap Deployment and Testing: Cloud provides a large number of machine learning tools for model deployment, they are more manageable, interactive and operate in seconds so the testing becomes easy to deploy and check results or change the model.
- Experimentation for Business Improvement: Clouds have different model management and features that give businesses the opportunity of experiments and try different state of the art models for their system. They can compare the performances by running them parallelly and making decisions.
- Enormous Processing Power with GPU: Graphical Processing Units on clouds provide businesses with a larger processing capacity that is possible using their infrastructure.
- Scalability of Business: Companies can scale the same system in future on clouds to improve the performance. There is no requirement for any hardware or changes in their infrastructure.
- Security of User Data: The security of user data is essential for businesses and the cloud provides larger layers of security in their system such that financial institutions and governments are also using them.
ML algorithms using resources are starting to solve more significant problems of the world. For example, the world map identifies accurate patterns and predictions of populations, poverty, climate change effects, carbonization, and the destruction of green fields. This powerful combination of ML life cycle and cloud will soon change the future of health, finance, banking, and industry. However, there is still some resistance from companies on security, system update, and reliability of cloud computing that will be solved over time.