Data Science Dojo

OCTOBER 10, 2023

AI conferences and events are organized to talk about the latest updates taking place, globally. Why must you attend AI conferences and events? Attending global AI-related virtual events and conferences isn’t just a box to check off; it’s a gateway to navigating through the dynamic currents of new technologies. billion by 2032.

AI 243

AI AI Data Observability Artificial Intelligence 243

Data Science Dojo

NOVEMBER 15, 2023

Here are nine of the top AI conferences happening in North America in 2023 and 2024 that you must attend: Top AI events and conferences in North America attend in 2023 Big Data and AI TORONTO 2023: Big Data and AI Toronto is the premier event for data professionals in Canada.

Machine Learning 195

Machine Learning Machine Learning AI AI 195

Precisely

DECEMBER 12, 2023

It includes streaming data from smart devices and IoT sensors, mobile trace data, and more. Data is the fuel that feeds digital transformation. But with all that data, there are new challenges that may prompt you to rethink your data observability strategy. Learn more here.

Data Observability 52

Data Observability Data Quality Data Pipeline Machine Learning 52

ODSC - Open Data Science

JANUARY 11, 2024

Getting Started with AI in High-Risk Industries, How to Become a Data Engineer, and Query-Driven Data Modeling How To Get Started With Building AI in High-Risk Industries This guide will get you started building AI in your organization with ease, axing unnecessary jargon and fluff, so you can start today.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

DagsHub

AUGUST 23, 2024

Data Quality Dimensions Data quality dimensions are the criteria that are used to evaluate and measure the quality of data. These include the following: Accuracy indicates how correctly data reflects the real-world entities or events it represents. Datafold is a tool focused on data observability and quality.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. This provides end-to-end support for data engineering and MLOps workflows.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

ODSC - Open Data Science

JANUARY 9, 2024

Now that we’re in 2024, it’s important to remember that data engineering is a critical discipline for any organization that wants to make the most of its data. These data professionals are responsible for building and maintaining the infrastructure that allows organizations to collect, store, process, and analyze data.

Data Engineering 97

Data Engineering Data Engineering Data Engineer Data Engineering 97