What will you learn from this course?
In this complete certification course, you will learn
How to build, execute, and examine different machine learning models through Amazon Web Services.
About different algorithms that are already built in Sage Maker i.e., Linear Learner, Principal Component Analysis, XG-Boost, and K-Nearest Neighbors.
How to explain and run picture and test labeling jobs in Sage Maker.
How to read, clean, and visualize data without using any type of code.
Learn how to automate your workflow without using any code through AWS Lambda and SageMaker framework.
What is AWS?
AWS is a company created by Amazon and it stands for Amazon Web Services. This firm provides the access to cloud computing platforms and APIs for governments, people, or businesses. AWS offers software tools and computer processing capacity through these web computing services.
An Amazon subsidiary called Amazon Web Services, Inc. (AWS) offers individuals, businesses, and governments pay-as-you-go cloud computing systems and APIs. Through AWS server farms, these cloud computing web solutions offer software tools and distributed computer processing capacity. One of these services, Amazon Elastic Compute Cloud, provides users with a virtual cluster of computers that is constantly accessible via the Internet. The majority of the features of a real computer are replicated by AWS' virtual machines, including hardware processing units (CPUs) and graphics processing units (GPUs), local/RAM memory, a choice of operating systems, networking, hard-disk/SSD storage, and pre-installed application software such as web servers, databases, and customer relationship management. For illustration, Amazon Elastic Computer Cloud is among the popular AWS cloud services that allow users to use virtual servers that are accessible around the clock.
What is AWS SageMaker?
A cloud-based machine-learning platform called Amazon SageMaker enables customers to construct, design, develop, test, and deploy machine-learning algorithms in a hosted environment that is prepared for production. Using AWS SageMaker has a number of advantages.
Machine learning has many applications and advantages. Advanced analytics for client data and the detection of security threats on the back end are two examples. Even for seasoned software developers, implementing ML models is challenging. Amazon SageMaker makes an effort to make the process simpler. Utilizing widely used algorithms and other resources expedites the machine-learning process.
What is meant by Machine Learning in Amazon SageMaker?
The process of machine learning is iterative. To process data gathering, special hardware and workflow tools are needed. A data science team often creates ML models in two processes or pipelines: training and inferencing. A computer is taught to operate in a particular way through data training, which is based on the identification of recurrent patterns in data sets. The data is then inferred from or trained to react to fresh data patterns.
Software development teams incorporate the final model within service or product application program interfaces after data scientists refine the machine learning model (APIs). Many businesses lack the resources to hire experts and allocate resources to AI development. Utilizing a combination of technologies, AWS SageMaker automates time-consuming manual operations while reducing hardware expenses and human error.
The AWS SageMaker toolkit includes parts for machine learning modeling. SageMaker templates abstract software functions. They offer a platform for creating, honing, maintaining, and implementing machine learning algorithms at scale on the Amazon cloud service.
How much does a specialist in machine learning for AWS Sagemaker make?
The compensation range for an AWS Sagemaker professional is between $84k and $173k annually, according to several sources. Highly lucrative employment opportunities are available in this industry for you. Through Brainmeasures training, you can obtain this position. Start learning now by enrolling in this course.
What prerequisites are there for this course?
Python proficiency is necessary.
Who may enroll in this course?
Developers interested in learning more about machine learning at Amazon
Newly qualified data scientists who want to further their careers and build their portfolios
experienced consultants eager to apply AI/ML on AWS to transform businesses.
Tech enthusiasts who are curious about computer science and artificial intelligence but are fresh to the topic and want to gain hands-on experience with AWS.
