AWS Machine Learning Model: What is it?

Introduction

By incorporating and testing machine learning algorithms in their most important initiatives, businesses are investigating new AI possibilities. Machine learning includes applying statistical techniques to big data sets in order to draw conclusions. It enables businesses to increase operational effectiveness, produce more precise forecasts and predictions, analyze consumer behavior, and receive decision-making support. Although machine learning principles have been around for a while, their use has just lately become more widespread as a result of the massive increase in data output and the exponential rise in computing capacity. In a Gartner survey from 2021, 48% of CIOs reported that they have already implemented or intend to implement AI and machine learning technology in their enterprises.

AI and machine learning are only two of the latest technical advancements that cloud computing has been at the forefront of. Managed machine learning services are provided to clients by renowned cloud service providers including AWS, IBM, Google Cloud, and Azure. Machine learning models have been put into practice and tested in small initiatives by organizations. Discover what machine learning models are and how they are employed in the contemporary world by reading this article. You will also learn about the AWS certification training programs that can help people become experts in creating cloud-based machine learning solutions.

What is an AWS Machine Learning Model?

‍
Machine learning involves the development of algorithms and models that use computing devices to process large sets of data to identify patterns and arrive at inferences. We come across many use cases for machine learning models in our daily life. For example, machine learning models are used in cancer diagnosis in the medical field, fraud detection in the financial services industry, traffic predictions, self-driving cars, etc.

Implementation of machine learning solutions on the AWS cloud includes a few stages:

• Creating the data source
• Preparing the data for ML model
• Developing the model
• Training the model and  
• Evaluating or monitoring ML models

An AWS Machine Learning Model is a file trained to analyze data based on a pre-designed algorithm. The AWS machine learning engineer must prepare the data before feeding it to the ML model. The larger the input dataset, the higher the inference accuracy drawn from the machine learning model. If you are new to machine learning and want to understand how the ML pipeline works in the AWS cloud, sign up for the AWS Discovery Day, where you can learn Machine Learning basics.

Machine learning models developed for the AWS cloud platform can be broadly classified into three types: binary classification model, multiclass classification model, and regression model.

• With binary classification models, the outcome is one out of two predictions. Such models can be applied in cases such as spam detection in mails, predicting a customer's buying decision, identifying if the input is from a robot or not in a web browser, etc. For binary classification models, AWS ML solutions use the logistic regression algorithm.
   
• The outcome can be one out of more than two predictions in multiclass classification models. These models are used in cases such as identifying product types on the retailer websites, classifying movie genres on an entertainment website, etc. AWS ML services use multinominal logistic regression to train multiclass classification models.
   
• When a team requires predicting a numerical value through a machine learning model, they can go for regression models. Linear regression algorithms are used for training regression models in the AWS cloud. With regression models, AWS Machine Learning engineers can predict the temperature of a city on a future date, develop sales forecasts, etc.

‍

Training Machine Learning Models in AWS Cloud

‍

Training a machine learning model requires datasets that the ML algorithm processes to identify patterns. The Machine Learning Engineer must also provide the correct answer for a given case through the dataset. These answers are also termed target attributes. Since the quality and accuracy of the outcome of a machine learning model depends on the input dataset, the team needs to clean up, segregate and feed good data to the model. The larger the dataset fed to the ML model, the more accurate the predictions from the ML model.

It is also important for the team to choose the correct algorithm for the particular use case. Once the machine learning team identifies the algorithm, the team must implement it and test it before they decide on going ahead. Once the desired results are obtained from the ML model after testing, the team can continue cleaning up data and feeding datasets to the model to arrive at predictions or inferences.

If you are planning to start your career in machine learning and want to learn more about AWS services, check out our blog on building AWS Machine Learning models.

‍

AWS Machine Learning Training and Certification

‍

Machine learning solutions can be implemented by developers, solutions architects, data engineers, and other IT professionals who have basic knowledge of Python programming and AWS cloud concepts. Building a machine learning solution involves a step-by-step approach where it starts with identifying the business problem and choosing the correct approach for developing a solution.

Professionals who build ML solutions in the AWS cloud require AWS training from authorized instructors. With the AWS Machine Learning training program, the participants learn about ML pipelines, build ML models using Amazon SageMaker, identify the best practices for designing scalable, cost-optimized, and secure ML pipelines and get hands-on training in implementing ML solutions for real-life projects.

After completing the 4-days Machine Learning Pipeline on AWS training program, learners can take the MLS-C01 AWS Certified Machine Learning-Specialty examination to get certified. The examination validates the professional’s ability to design, build, train and manage machine learning solutions in the AWS cloud. The person taking up the exam must have at least two years of hands-on experience in developing machine learning solutions in AWS. Download the AWS certification path to understand the training programs and certifications better.

‍

Ready to ace your AWS certification exam? Boost your confidence and test your knowledge with Prepzo's comprehensive AWS Machine Learning practice tests.