AWS Machine Learning Blog

MAY 16, 2024

With Amazon EMR, which provides fully managed environments like Apache Hadoop and Spark, we were able to process data faster. SageMaker pipeline for training SageMaker Pipelines helps you define the steps required for ML services, such as preprocessing, training, and deployment, using the SDK.

AWS 117

AWS ML ML Deep Learning 117

Pickl AI

DECEMBER 25, 2024

Business Analytics requires business acumen; Data Science demands technical expertise in coding and ML. Big data platforms such as Apache Hadoop and Spark help handle massive datasets efficiently. Key Takeaways Business Analytics targets historical insights; Data Science excels in prediction and automation.

Data Science 52

Data Science Analytics Analytics Data Scientist 52

Mlearning.ai

MAY 23, 2023

Check out this course to build your skillset in Seaborn —  [link] Big Data Technologies Familiarity with big data technologies like Apache Hadoop, Apache Spark, or distributed computing frameworks is becoming increasingly important as the volume and complexity of data continue to grow. in these fields.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

DagsHub

OCTOBER 23, 2024

Managing unstructured data is essential for the success of machine learning (ML) projects. This article will discuss managing unstructured data for AI and ML projects. You will learn the following: Why unstructured data management is necessary for AI and ML projects. How to properly manage unstructured data.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

IBM Journey to AI blog

JANUARY 18, 2023

They defined it as : “ A data lakehouse is a new, open data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data. ”.

Data Lakes 52

Data Lakes Data Warehouse Azure Apache Hadoop 52

Mlearning.ai

JULY 10, 2023

In my 7 years of Data Science journey, I’ve been exposed to a number of different databases including but not limited to Oracle Database, MS SQL, MySQL, EDW, and Apache Hadoop.

SQL 52

SQL Database Apache Hadoop Data Science 52

Pickl AI

MAY 31, 2023

Proficiency in ML is understood when these are not just present in the aspirant in conceptual ways but also in terms of its applications in solving business problems. Machine Learning: Data Science aspirants need to have a good and concise understanding on Machine Learning algorithms including both supervised and unsupervised learning.

Data Science 52

Data Science Python Data Scientist Machine Learning 52