Data Science Dojo

OCTOBER 10, 2023

Generative AI and Data Storytelling (Virtual event | 27th September – 2023) A virtual event on generative AI and data storytelling. The event is hosted by Data Science Dojo and will be held on September 27, 2023. The speaker is Andrew Madson, a data analytics leader and educator.

AI 243

AI AI Data Observability Artificial Intelligence 243

Data Science Dojo

NOVEMBER 15, 2023

IMPACT is a great opportunity to learn from experts in the field, network with other professionals, and stay up-to-date on the latest trends and developments in data and AI. This session will explore how LLMs can be used to solve real-world enterprise data challenges. The summit will be held on November 8th, 2023.

Machine Learning 195

Machine Learning Machine Learning AI AI 195

Data Science Connect

JULY 21, 2024

Astro enhances data pipeline development by offering features like dynamic scaling, real-time monitoring, and comprehensive data observability and governance. Astronomer provides a managed platform, Astro, for running Apache Airflow® at scale.

Data Pipeline 100

Data Pipeline Data Observability 100

ODSC - Open Data Science

JANUARY 9, 2024

Data engineers act as gatekeepers that ensure that internal data standards and policies stay consistent. Data Observability and Monitoring Data observability is the ability to monitor and troubleshoot data pipelines. So get your pass today, and keep yourself ahead of the curve.

Data Engineer 97

Data Engineer Data Engineering Data Engineering Data Engineering 97

ODSC - Open Data Science

MAY 16, 2023

The mesh approach allows for more streamlined data-driven decisions and processes within specific departments and puts responsibility in the hands of the people who actually use the information. AI in Data Observability Automation has steadily become more common in data management software, but it’ll reach new heights in 2023.

Database 98

Database Data Science Data Lakes Data Observability 98

ODSC - Open Data Science

FEBRUARY 14, 2023

Beyond Monitoring: The Rise of Data Observability Shane Murray Field | CTO | Monte Carlo This session addresses the problem of “data downtime” — periods of time when data is partial, erroneous, missing or otherwise inaccurate — and how to eliminate it in your data ecosystem with end-to-end data observability.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

IBM Data Science in Practice

NOVEMBER 28, 2022

With Address Verification Interface and IBM Multicloud Data Integration, organizations can jumpstart their data fabric journey with quality data that is integrated and enriched, governed and protected, and easily accessible to their organization.

Data Pipeline 130

Data Pipeline Data Quality Data Preparation Data Governance 130

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. Register here!

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

ODSC - Open Data Science

JULY 20, 2023

Video of the Week: Beyond Monitoring: The Rise of Data Observability Watch as Monte Carlo’s Shane Murray introduces “Data Observability” as the game-changing solution to the costly reality of broken data in advanced data teams.

Machine Learning 52

Machine Learning Machine Learning AI AI 52

The MLOps Blog

JUNE 27, 2023

With built-in components and integration with Google Cloud services, Vertex AI simplifies the end-to-end machine learning process, making it easier for data science teams to build and deploy models at scale. Metaflow Metaflow helps data scientists and machine learning engineers build, manage, and deploy data science projects.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

AWS Machine Learning Blog

FEBRUARY 15, 2024

On the one hand, analysts and domain experts have a very deep knowledge of the data in question and its interpretation, yet often lack the exposure to data science tooling and high-level programming languages such as Python. The predicted value indicates the expected value for our target metric based on the training data.

ML 123

ML ML Data Science AWS 123

IBM Journey to AI blog

DECEMBER 19, 2023

Anomalies are not inherently bad, but being aware of them, and having data to put them in context, is integral to understanding and protecting your business. The challenge for IT departments working in data science is making sense of expanding and ever-changing data points.

Machine Learning 82

Machine Learning Machine Learning Supervised Learning K-nearest Neighbors 82

Precisely

OCTOBER 10, 2024

They can ask questions and get meaningful data-driven answers. With data democratization, the availability of data and associated analysis tools extends far beyond the limited group of experts who have a data science background. To learn more about how your organization can get data democratization.

Data Quality 52

Data Quality Data Governance Analytics Analytics 52

IBM Journey to AI blog

JULY 13, 2023

Data science tasks such as machine learning also greatly benefit from good data integrity. When an underlying machine learning model is being trained on data records that are trustworthy and accurate, the better that model will be at making business predictions or automating tasks.

Data Quality 74

Data Quality Data Profiling Data Governance Machine Learning 74

DagsHub

AUGUST 23, 2024

Datafold is a tool focused on data observability and quality. It is particularly popular among data engineers as it integrates well with modern data pipelines (e.g., Source: [link] Monte Carlo is a code-free data observability platform that focuses on data reliability across data pipelines.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

Mlearning.ai

JUNE 21, 2023

Introduction The world of data science and machine learning (ML) is filled with an array of powerful tools and techniques. Among them, the Hidden Markov Chain (HMC) model stands out as a versatile and robust approach for analyzing sequential data. Observed variables are the data points that we have access to.

Data Scientist 52

Data Scientist Machine Learning Machine Learning ML 52

Heartbeat

MAY 30, 2023

It provides sensory data (observations) and rewards to the agent, and the agent acts in the environment based on its policy. Editor’s Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners.

Deep Learning 72

Deep Learning Deep Learning Machine Learning Machine Learning 72

Pickl AI

JUNE 7, 2024

This blog will delve into ETL Tools, exploring the top contenders and their roles in modern data integration. Let’s unlock the power of ETL Tools for seamless data handling. Also Read: Top 10 Data Science tools for 2024. It is a process for moving and managing data from various sources to a central data warehouse.

ETL 40

ETL Data Quality Data Pipeline Data Warehouse 40

ODSC - Open Data Science

DECEMBER 3, 2024

In its essence, data mesh helps with data observability  — another important element every organization should consider. With granular access controls, data lineage, and domain-specific audit logs, data catalogs allow engineers and developers to have a better view of their systems than before.

Data Pipeline 40

Data Pipeline Data Governance Data Analyst Data Observability 40

Pickl AI

JULY 8, 2024

Robust validation and monitoring frameworks enhance pipeline reliability and trustworthiness, safeguarding against data-driven decision-making risks. Must Read Blogs: Elevate Your Data Quality: Unleashing the Power of AI and ML for Scaling Operations. The Difference Between Data Observability And Data Quality.

Data Pipeline 52

Data Pipeline Data Quality Database Apache Kafka 52

Pickl AI

JULY 24, 2024

Bias Systematic errors introduced into the data due to collection methods, sampling techniques, or societal biases. Bias in data can result in unfair and discriminatory outcomes. Read More: Data Observability vs Data Quality Data Cleaning and Preprocessing Techniques This is a critical step in preparing data for analysis.

Data Quality 52

Data Quality Machine Learning Machine Learning Clean Data 52

The MLOps Blog

AUGUST 3, 2023

As you’ve been running the ML data platform team, how do you do that? How do you know whether the platform we are building, the tools we are providing to data science teams, or data teams are bringing value? If you can be data-driven, that is the best. Piotr: Sounds like something with data, right?

ML 52

ML ML Data Scientist Machine Learning 52

Pickl AI

MARCH 19, 2025

Learning these tools is crucial for building scalable data pipelines. offers Data Science courses covering these tools with a job guarantee for career growth. Introduction Imagine a world where data is a messy jungle, and we need smart tools to turn it into useful insights.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52