Knowledge Hut

MARCH 19, 2024

With its extensive range of cloud services, Amazon Web Services (AWS) has completely changed the way businesses run. The AWS case studies comprehensively explain how companies or organizations have used Amazon Web Services (AWS) to solve problems, boost productivity, and accomplish objectives.

AWS 52

AWS Amazon Web Services Hospitality Cloud Computing 52

Knowledge Hut

DECEMBER 26, 2023

They are required to have deep knowledge of distributed systems and computer science. Building data systems and pipelines Data pipelines refer to the design systems used to capture, clean, transform and route data to different destination systems, which data scientists can later use to analyze and gain information.

Data Engineering 98

Data Engineering Data Engineer Engineering Pipeline-centric 98

ProjectPro

JANUARY 19, 2022

Data Engineering refers to creating practical designs for systems that can extract, keep, and inspect data at a large scale. In 2017, Gartner predicted that 85%of the data-based projects would fail and deliver the desired results. Ability to demonstrate expertise in database management systems. What is Data Engineering?

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

ProjectPro

JULY 23, 2021

AWS (Amazon Web Services) is the world’s leading and widely used cloud platform, with over 200 fully featured services available from data centers worldwide. Amazon Web Services was launched in July 2002 from the existing Amazon cloud platform with the initial purpose of managing online retail transactions.

AWS 52

AWS Project Amazon Web Services Cloud Computing 52

ProjectPro

JUNE 26, 2021

Here is a list of them: Use Deep learning models on the company's data to derive solutions that promote business growth. Leverage machine learning libraries in Python like Pandas, Numpy, Keras, PyTorch, TensorFlow to apply Deep learning and Natural Language Processing on huge amounts of data.

Project 40

Project Deep Learning Datasets Medical 40

ProjectPro

JANUARY 24, 2023

To train a model using Amazon SageMaker, a training job has to be created which includes several key pieces of information: The location of training data: This can be an S3 bucket or on a local file system that is accessible to the SageMaker training instances. Amazon launched SageMaker in November 2017. PREVIOUS NEXT <

Building 52

Building Algorithm Machine Learning AWS 52