Data Scientist and DataOps - Data Science Current

Top Companies to work for if you are a data scientist

Data Science 101

APRIL 12, 2019

LinkedIn’s 2017 report had put Data Scientist as the second fastest growing profession and it’s number one on 2019’s list of most promising jobs. There are three main reasons why data science has been rated as a top job according to research. 3 1010 Data. Checkout: 1010 Data Careers. #4 Checkout: Dataiku Careers.

Data Scientist

Data Scientist Data Science DataOps Hadoop

DataOps vs. DevOps: What’s the Difference?

Alation

AUGUST 3, 2021

DataOps and DevOps are two distinctly different pursuits. But where DevOps focuses on product development, DataOps aims to reduce the time from data need to data success. At its best, DataOps shortens the cycle time for analytics and aligns with business goals. What is DataOps? What is DevOps?

DataOps

DataOps Data Pipeline Data Analyst Analytics

What Is DataOps? Definition, Principles, and Benefits

Alation

SEPTEMBER 28, 2022

What exactly is DataOps ? The term has been used a lot more of late, especially in the data analytics industry, as we’ve seen it expand over the past few years to keep pace with new regulations, like the GDPR and CCPA. In essence, DataOps is a practice that helps organizations manage and govern data more effectively.

DataOps

DataOps Data Pipeline Data Quality Analytics

Webinars

How to Achieve High-Accuracy Results When Using LLMs

MORE WEBINARS

What Do Data Scientists Do? A Guide to AI Maturity, Challenges, and Solutions

DataRobot Blog

SEPTEMBER 13, 2022

According to IDC , 83% of CEOs want their organizations to be more data-driven. Data scientists could be your key to unlocking the potential of the Information Revolution—but what do data scientists do? What Do Data Scientists Do? Data scientists drive business outcomes. Download Now.

Data Scientist

Data Scientist ML ML AI

The Audience for Data Catalogs and Data Intelligence

Alation

JUNE 21, 2022

The audience grew to include data scientists (who were even more scarce and expensive) and their supporting resources (e.g., ML and DataOps teams). After that came data governance , privacy, and compliance staff. Power business users and other non-purely-analytic data citizens came after that.

DataOps

DataOps Data Scientist Data Quality Data Pipeline

Unlocking the Power of AI with Implemented Machine Learning Ops Projects

Becoming Human

MAY 11, 2023

Some of the main challenges include: Lack of Integration: MLOps projects require close collaboration between data scientists, software developers, and IT operations teams. DevOps and DataOps: DevOps and DataOps are related approaches that emphasize collaboration between software developers and IT operations teams.

Machine Learning

Machine Learning Machine Learning DataOps Cloud Computing

How HR Tech Company Sense Scaled their ML Operations using Iguazio

Iguazio

JANUARY 16, 2024

Since AI is a central pillar of their value offering, Sense has invested heavily in a robust engineering organization including a large number of data and AI professionals. This includes a data team, an analytics team, DevOps, AI/ML, and a data science team. Gennaro Frazzingaro, Head of AI/ML at Sense.

ML

ML ML DataOps Data Scientist

AIOps vs. MLOps: Harnessing big data for “smarter” ITOPs

IBM Journey to AI blog

AUGUST 12, 2024

It helps companies streamline and automate the end-to-end ML lifecycle, which includes data collection, model creation (built on data sources from the software development lifecycle), model deployment, model orchestration, health monitoring and data governance processes.

Big Data

Big Data Big Data ML ML

How Sense Uses Iguazio as a Key Component of Their ML Stack

Iguazio

JANUARY 16, 2024

Since AI is a central pillar of their value offering, Sense has invested heavily in a robust engineering organization, including a large number of data and data science professionals. This includes a data team, an analytics team, DevOps, AI/ML, and a data science team. Gennaro Frazzingaro, Head of AI/ML at Sense.

ML

ML ML DataOps Data Scientist

Alation 2023.1: Easing Self-Service for the Modern Data Stack with Databricks and dbt Labs

Alation

APRIL 4, 2023

Integrating helpful metadata into user workflows gives all people, from data scientists to analysts , the context they need to use data more effectively. The Benefits and Challenges of the Modern Data Stack Why are such integrations needed? Before a data user leverages any data set, they need to be able to learn about it.

DataOps

DataOps Data Engineering Data Engineer Data Engineering

9 data governance strategies that will unlock the potential of your business data

IBM Journey to AI blog

SEPTEMBER 5, 2024

Automated classification : Classifies data based on predefined categories, such as personal identifiable information (PII), financial data, intellectual property or confidential information. DevOps and DataOps are practices that emphasize developing a collaborative culture.

Data Governance

Data Governance Data Quality DataOps Artificial Intelligence

How GPT helped expand our marketing team’s capacity

Snorkel AI

AUGUST 29, 2023

I had previously followed a similar development pattern working with other libraries as a data scientist. Sometimes it would return a list of tweets with the specified tone followed by the text, such as “Funny: With Snowflake and Snorkel AI, you can label your data faster than you can say ‘DataOps’!

DataOps

DataOps Python AI AI

Data Catalog: Part of the Solution – or Part of the Problem?

Alation

DECEMBER 13, 2022

People come to the data catalog to find trusted data, understand it, and use it wisely. Today a modern catalog hosts a wide range of users (like business leaders, data scientists and engineers) and supports an even wider set of use cases (like data governance , self-service , and cloud migration ). Data governance.

DataOps

DataOps Data Governance Data Silos Data Scientist

Is your model good? A deep dive into Amazon SageMaker Canvas advanced metrics

AWS Machine Learning Blog

JULY 31, 2023

It also enables you to evaluate the models using advanced metrics as if you were a data scientist. We explain the metrics and show techniques to deal with data to obtain better model performance. For a column impact of 25%, Canvas weighs the prediction as 25% for the column and 75% for the other columns.

ML

ML ML Data Preparation Machine Learning

How GPT helped expand our marketing team’s capacity

Snorkel AI

AUGUST 29, 2023

I had previously followed a similar development pattern working with other libraries as a data scientist. Sometimes it would return a list of tweets with the specified tone followed by the text, such as “Funny: With Snowflake and Snorkel AI, you can label your data faster than you can say ‘DataOps’!

DataOps

DataOps Python Artificial Intelligence Artificial Intelligence

How GPT helped expand our marketing team’s capacity

Snorkel AI

AUGUST 29, 2023

I had previously followed a similar development pattern working with other libraries as a data scientist. Sometimes it would return a list of tweets with the specified tone followed by the text, such as “Funny: With Snowflake and Snorkel AI, you can label your data faster than you can say ‘DataOps’!

DataOps

DataOps Python Artificial Intelligence Artificial Intelligence

What Is Data Observability and Why You Need It?

Precisely

DECEMBER 12, 2023

For some time now, data observabilit y has been an important factor in software engineering, but its application within the realm of data stewardship is a relatively new phenomenon. Data observability is a foundational element of data operations (DataOps).

Data Observability

Data Observability Data Quality Data Pipeline Machine Learning

Demystifying Data Mesh

Precisely

JULY 15, 2024

Data domain teams have a better understanding of the data and their unique use cases, making them better positioned to enhance the value of their data and make it available for data teams. With this approach, demands on each team are more manageable, and analysts can quickly get the data they need.

Data Governance

Data Governance DataOps Data Silos Data Pipeline

Data Science Current

Top Companies to work for if you are a data scientist

DataOps vs. DevOps: What’s the Difference?

Webinars

Trending Sources

What Is DataOps? Definition, Principles, and Benefits

Webinars

What Do Data Scientists Do? A Guide to AI Maturity, Challenges, and Solutions

The Audience for Data Catalogs and Data Intelligence

Unlocking the Power of AI with Implemented Machine Learning Ops Projects

How HR Tech Company Sense Scaled their ML Operations using Iguazio

AIOps vs. MLOps: Harnessing big data for “smarter” ITOPs

How Sense Uses Iguazio as a Key Component of Their ML Stack

Alation 2023.1: Easing Self-Service for the Modern Data Stack with Databricks and dbt Labs

9 data governance strategies that will unlock the potential of your business data

How GPT helped expand our marketing team’s capacity

Data Catalog: Part of the Solution – or Part of the Problem?

Is your model good? A deep dive into Amazon SageMaker Canvas advanced metrics

How GPT helped expand our marketing team’s capacity

How GPT helped expand our marketing team’s capacity

What Is Data Observability and Why You Need It?

Demystifying Data Mesh

Stay Connected