Machine Learing with Cloud

Machine Learing with Cloud

Introduction

In the technology-driven times, some of the most powerful technologies are machine learning and cloud computing. Cloud computing technology has existed for quite some time. Even though machine learning technology is comparatively new, it has been able to capture the attention of the global audience. In case you are unaware of machine learning tools, you must expand your knowledge of this technology-based concept. This promising technology has the potential to transform human life and existing technological infrastructure to a substantial degree.

Machine learning is the application of AI technology that enables systems to improve and learn from experience automatically. Thus, there is no need to program this technology explicitly. The machine learning process starts with observations. After the capturing of a pattern in the data, it is possible to make suitable decisions. The impact of machine learning on cloud computing could be significant. In fact, this innovative technological concept could revolutionize the existing cloud computing technology.

The machine learning concept has the ability to learn from data. By integrating this technology-based concept with the cloud computing approach, revolutionary changes can take place in the technological infrastructure. The amalgamation of machine learning with cloud computing can give rise to an “intelligent cloud.”

What is Machine Learning with Cloud Computing?

DMachine learning 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.

Currently, with machine learning services becoming the providing of various public clouds, it has become both affordable and accessible. Amazon Web Services, Google Cloud Platform, and Microsoft Azure, all provide ML with such ease of use that it no longer needs a team of data scientists to execute.

The coming together of cloud computing and machine learning has given rise to “the intelligent cloud” since ML has given rise to different new cloud services.

Machine learning and cloud computing technologies are transforming the world we live in. But this is just the beginning and this will take some time to be completely functional and to be used in important sectors like business, healthcare, and banking. Machine learning is making data more accessible to handle over the cloud. With continuous artificial intelligence research in cloud computing, cloud computing will become more and more intelligent. Machine learning will become so essential in the cloud that every cloud will be using machine learning.

Objective of this Training

A subset of artificial intelligence (AI), machine learning (ML) is the area of computational science that focuses on analyzing and interpreting patterns and structures in data to enable learning, reasoning, and decision making outside of human interaction. Simply put, machine learning allows the user to feed a computer algorithm an immense amount of data and have the computer analyze and make data-driven recommendations and decisions based on only the input data. If any corrections are identified, the algorithm can incorporate that information to improve its future decision making.

Machine learning has applications in all types of industries, including manufacturing, retail, healthcare and life sciences, travel and hospitality, financial services, and energy, feedstock, and utilities.

 Predictive maintenance and condition monitoring.

 Upselling and cross-channel marketing.

 Healthcare and life sciences. Disease identification and risk satisfaction.

 Travel and hospitality. Dynamic pricing.

 Cloud Solution and Analysis.

 Financial services. Risk analytics and regulation.

 Energy, Energy & demand and supply optimization.

Learning Objectives

 Distinction between artificial intelligence, machine learning, data science, and statistical analysis.

 The various types of machine learning with real-world examples such as regression, classification, decision trees, and deep learning. We’ll even train a self-driving car!

 How to evaluate and frame business problems for potential machine learning applications.

 Brief survey of available tools, datasets, and resources common in the machine learning space.

 Limitations, potential pitfalls, and ethical considerations around machine learning.

Eligibility Required

 BE/BTech. (All Streams)

 BCA, BSc (CS/IT) Degree

 PGDCA, MCA, ME /MTech

On completion of this training, there will be great opportunities for you to work as -

 Product Delivery Engineer

 Machine Learning Engineer

 Data Scientist

 Embedded Software Engineer

 Product Data Engineer

 M L Engineer – Azur/Cloud