Amazon Web Services

1. Amazon Web Services

Welcome back! Let's talk about Amazon Web Services.

2. AWS and the market

AWS stands for Amazon Web Services. AWS launched in 2006, two years before Google Cloud and four years before Microsoft Azure. Being the first mover on the market provided them with a strong advantage, allowing them to develop a breadth of services: they offer more than 175 services in areas such as computing, storage, analytics, security and enterprise applications, and even machine learning. As of the end of 2023, AWS was the market leader for cloud computing services, with 31% of the total market share.

3. AWS professional cloud services

Regarding applications for professional use, AWS offers Simple Storage Service, or S3, for file storage,

4. AWS professional cloud services

Elastic Compute Cloud, or EC2, for computation,

5. AWS professional cloud services

and Relational Database Service, or RDS, for databases.

6. AWS professional data services

On the data front, some examples of the AWS offering include Redshift, a fully managed, petabyte-scale data warehouse service;

7. AWS professional data services

Kinesis, a real-time data and analytics service;

8. AWS professional data services

and SageMaker, a machine-learning platform for predictive analytics.

9. AWS customers

Over the years, AWS has had numerous high-profile customers across different industries, such as Disney, Verizon, and Deloitte.

10. AWS case study

How about an AWS case study? NerdWallet helps customers make financial decisions, using data science and machine learning to provide personalized financial recommendations. NerdWallet wanted to optimize deployment cost and time: it was taking too long to go from concept to launching into production. The faster models are deployed, the faster the engineering team can iterate and improve the product. NerdWallet was already using compute and container services from Amazon. They decided to add Amazon SageMaker, Amazon's cloud machine learning platform, enabling building, training, and deploying models in one place. With SageMaker, NerdWallet could seamlessly integrate their AWS compute instances to reduce development time and cost.

11. AWS case study

Launching models now takes days instead of months, and data scientists can focus more on strategy and other projects. Training costs have also been reduced by 75% because SageMaker uses on-demand instances rather than running one continuously. NerdWallet has fully modernized its data science engineering practices to remain competitive and continue innovating. You can find more examples of cloud in practice at AWS at the link below!

12. Let's practice!

Let's ensure you clearly understand AWS's services and position before moving on to Microsoft Azure!

Create Your Free Account

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.