Towards AI

MARCH 3, 2025

Today, Im sharing everything I learned about the five best cloud platforms for hosting AI applications. AWS is like that overachieving student who excels at everything. Complex pricing structure (seriously, who can predict AWS bills?).Steeper Join thousands of data leaders on the AI newsletter. Lets dive in!

Data Lakes 57

Data Lakes AI AI AWS 57

Precisely

JANUARY 19, 2023

Yet mainframes weren’t designed to integrate easily with modern distributed computing platforms. Cloud computing, object-oriented programming, open source software, and microservices came about long after mainframes had established themselves as a mature and highly dependable platform for business applications.

AWS 52

AWS Cloud Computing Data Pipeline Big Data 52

Precisely

JANUARY 18, 2024

Yet mainframes weren’t initially designed to integrate easily with modern distributed computing platforms. Cloud computing, object-oriented programming, open source software, and microservices came about long after mainframes had established themselves as a mature and highly dependable platform for business applications.

AWS 52

AWS Artificial Intelligence Artificial Intelligence Cloud Computing 52

Precisely

JANUARY 25, 2024

Many organizations adopt a long-term approach, leveraging the relative strengths of both mainframe and cloud systems. This integrated strategy keeps a wide range of IT options open, blending the reliability of mainframes with the innovation of cloud computing.

Data Governance 69

Data Governance Database Cloud Data Data Lakes 69

Pickl AI

APRIL 6, 2023

Consequently, here is an overview of the essential requirements that you need to have to get a job as an Azure Data Engineer. In-depth knowledge of distributed systems like Hadoop and Spart, along with computing platforms like Azure and AWS. Data Warehousing concepts and knowledge should be strong.

Azure 52

Azure Data Engineering Data Engineer Data Engineering 52

Pickl AI

NOVEMBER 4, 2024

Role of Data Engineers in the Data Ecosystem Data Engineers play a crucial role in the data ecosystem by bridging the gap between raw data and actionable insights. They are responsible for building and maintaining data architectures, which include databases, data warehouses, and data lakes.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

The MLOps Blog

JUNE 27, 2023

For example, if you use AWS, you may prefer Amazon SageMaker as an MLOps platform that integrates with other AWS services. SageMaker Studio offers built-in algorithms, automated model tuning, and seamless integration with AWS services, making it a powerful platform for developing and deploying machine learning solutions at scale.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

AWS Machine Learning Blog

JANUARY 26, 2024

We also discuss common security concerns that can undermine trust in AI, as identified by the Open Worldwide Application Security Project (OWASP) Top 10 for LLM Applications , and show ways you can use AWS to increase your security posture and confidence while innovating with generative AI.

AWS 137

AWS ML ML AI 137

AWS Machine Learning Blog

SEPTEMBER 12, 2024

Organizations that want to build their own models or want granular control are choosing Amazon Web Services (AWS) because we are helping customers use the cloud more efficiently and leverage more powerful, price-performant AWS capabilities such as petabyte-scale networking capability, hyperscale clustering, and the right tools to help you build.

AWS 94

AWS AI AI Clustering 94

Flipboard

FEBRUARY 19, 2025

In the age of generative artificial intelligence (AI), data isnt just kingits the entire kingdom. The success of any RAG implementation fundamentally depends on the quality, accessibility, and organization of its underlying data foundation. The following diagram shows the architecture for Alfreds RAG implementation.

AWS 81

AWS Cloud Computing Natural Language Processing Data Lakes 81