Analytics Vidhya

MARCH 21, 2023

Organizations are using various cloud platforms like Azure, GCP, etc., to store and analyze this data to get valuable business insights from it.

Azure 267

Azure Analytics Analytics Data Lakes 267

DataRobot Blog

MARCH 7, 2023

Organizations that want to prove the value of AI by developing, deploying, and managing machine learning models at scale can now do so quickly using the DataRobot AI Platform on Microsoft Azure. DataRobot is available on Azure as an AI Platform Single-Tenant SaaS, eliminating the time and cost of an on-premises implementation.

Azure 59

Azure Machine Learning Machine Learning AI 59

The MLOps Blog

JUNE 27, 2023

As you delve into the landscape of MLOps in 2023, you will find a plethora of tools and platforms that have gained traction and are shaping the way models are developed, deployed, and monitored. Open-source tools have gained significant traction due to their flexibility, community support, and adaptability to various workflows.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

ODSC - Open Data Science

APRIL 3, 2023

Companies have plenty of data at their disposal and are looking for people who can make sense of it and make deductions quickly and efficiently. We looked at over 25,000 job descriptions, and these are the data analytics platforms, tools, and skills that employers are looking for in 2023.

Analytics 52

Analytics Analytics Data Analyst SQL 52

DagsHub

DECEMBER 11, 2023

A complete overview revealing a diverse range of strengths and weaknesses for each data versioning tool. Adding new data to the storage requires pulling the existing data, then calculating the new hash before pushing back the whole data. However, these tools have functional gaps for more advanced data workflows.

Machine Learning 98

Machine Learning Machine Learning Data Lakes Database 98

The MLOps Blog

JANUARY 23, 2023

However, there are some key differences that we need to consider: Size and complexity of the data In machine learning, we are often working with much larger data. Basically, every machine learning project needs data. First of all, machine learning engineers and data scientists often use data from different data vendors.

ML 52

ML ML Data Lakes Machine Learning 52

Mlearning.ai

FEBRUARY 16, 2023

The solution was built on top of Amazon Web Services and is now available on Google Cloud and Microsoft Azure. Thus, the solution allows for scaling data workloads independently from one another and seamlessly handling data warehousing, data lakes , data sharing, and engineering. What does Snowflake do?

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52