Data Science Dojo

OCTOBER 10, 2023

A Glimpse into the future : Want to be like a scientist who predicted the rise of machine learning back in 2010? These gatherings are not mere events; they’re a sneak peek into the future of AI, offering fresh insights and developments straight from the minds of experts across academia, industry, and government.

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IBM Journey to AI blog

AUGUST 23, 2023

Because of this, when we look to manage and govern the deployment of AI models, we must first focus on governing the data that the AI models are trained on. This data governance requires us to understand the origin, sensitivity, and lifecycle of all the data that we use. and watsonx.data.

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MAY 4, 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 require consider your data observability strategy. Is your data governance structure up to the task?

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Pickl AI

JULY 24, 2024

Summary: Data quality is a fundamental aspect of Machine Learning. Poor-quality data leads to biased and unreliable models, while high-quality data enables accurate predictions and insights. What is Data Quality in Machine Learning? What is Data Quality in Machine Learning?

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Dataversity

AUGUST 15, 2022

So, what can you do to ensure your data is up to par and […]. The post Data Trustability: The Bridge Between Data Quality and Data Observability appeared first on DATAVERSITY. You might not even make it out of the starting gate.

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JANUARY 9, 2023

Read the Report Improving Data Integrity and Trust through Transparency and Enrichment Read this report to learn how organizations are responding to trending topics in data integrity. 2023 will continue to see a major shift in organizations increasing their investment in business-first data governance programs.

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The MLOps Blog

JUNE 27, 2023

How to evaluate MLOps tools and platforms Like every software solution, evaluating MLOps (Machine Learning Operations) tools and platforms can be a complex task as it requires consideration of varying factors. A self-service infrastructure portal for infrastructure and governance.

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MAY 8, 2023

Reduce errors, save time, and cut costs with a proactive approach You need to make decisions based on accurate, consistent, and complete data to achieve the best results for your business goals. That’s where the Data Quality service of the Precisely Data Integrity Suite can help. How does it work for real-world use cases?

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IBM Journey to AI blog

APRIL 10, 2023

To further the above, organizations should have the right foundation that consists of a modern data governance approach and data architecture. It’s becoming critical that organizations should adopt a data architecture that supports AI governance.

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MARCH 7, 2024

By harnessing the power of machine learning and natural language processing, sophisticated systems can analyze and prioritize claims with unprecedented efficiency and timeliness. Yet experts warn that without proactive attention to data quality and data governance, AI projects could face considerable roadblocks.

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OCTOBER 16, 2023

Big data analytics, IoT, AI, and machine learning are revolutionizing the way businesses create value and competitive advantage. The broader access granted by data democratization amplifies both the importance and the challenges of maintaining data integrity. Secure data exchange takes on much greater importance.

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JUNE 6, 2023

That means finding and resolving data quality issues before they turn into actual problems in advanced analytics, C-level dashboards, or AI/ML models. Data Observability and the Holistic Approach to Data Integrity One exciting new application of AI for data management is data observability.

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DagsHub

AUGUST 23, 2024

Image generated with Midjourney Organizations increasingly rely on data to make business decisions, develop strategies, or even make data or machine learning models their key product. As such, the quality of their data can make or break the success of the company.

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JULY 18, 2024

While data fabric is not a standalone solution, critical capabilities that you can address today to prepare for a data fabric include automated data integration, metadata management, centralized data governance, and self-service access by consumers.

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IBM Journey to AI blog

JULY 13, 2023

This is the practice of creating, updating and consistently enforcing the processes, rules and standards that prevent errors, data loss, data corruption, mishandling of sensitive or regulated data, and data breaches. Data science tasks such as machine learning also greatly benefit from good data integrity.

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OCTOBER 10, 2024

Read our eBook Managing Risk & Compliance in the Age of Data Democratization This eBook describes a new approach to achieve the goal of making the data accessible within the organization while ensuring that proper governance is in place. Read Data democracy: Why now?

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OCTOBER 12, 2023

This includes understanding the impact of change within one data element on the various other data elements and compliance requirements throughout the organization. Creating data observability routines to inform key users of any changes or exceptions that crop up within the data, enabling a more proactive approach to compliance.

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IBM Journey to AI blog

DECEMBER 16, 2022

Because Alex can use a data catalog to search all data assets across the company, she has access to the most relevant and up-to-date information. She can search structured or unstructured data, visualizations and dashboards, machine learning models, and database connections. Protected and compliant data.

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MARCH 14, 2023

And the desire to leverage those technologies for analytics, machine learning, or business intelligence (BI) has grown exponentially as well. Read our eBook 4 Ways to Measure Data Quality and learn more about the variety of data and metrics that organizations can use to measure data quality.

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FEBRUARY 9, 2024

In 2024 organizations will increasingly turn to third-party data and spatial insights to augment their training and reference data for the most nuanced, coherent, and contextually relevant AI output. When it comes to AI outputs, results will only be as strong as the data that’s feeding them.

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SEPTEMBER 11, 2023

By 2025, 50% of data and analytics leaders will be using augmented MDM and active metadata to enhance their capabilities – demonstrating that beyond data quality, automation is also in demand for data governance, data catalog, and security solutions.

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FEBRUARY 2, 2023

This makes an executive’s confidence in the data paramount. And this confidence requires data quality controls that include machine-learning models to recognize whether the appropriate rules are applied to your data and flexible data governance tools that can adapt to different business scenarios.

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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.

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JUNE 5, 2023

Step 1: Identify and remediate data quality issues One capability that makes the Data Quality service unique is identifying and correcting issues without moving the data from the source environment. Machine learning-based intelligence helps you save even more time by recommending how to clean up the data.

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APRIL 15, 2025

Artificial intelligence (AI) and machine learning (ML) are transforming businesses at an unprecedented pace. And yet, many data leaders struggle to trust their AI-driven insights due to poor data observability. The survey pinpoints four core challenges that data leaders must tackle: 1.

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FEBRUARY 28, 2025

Data quality issues often present a significant challenge to data integrity. Inaccurate, non-standardized, and incomplete data diminishes the potential of business analytics, artificial intelligence, and machine learning, even in a best-case scenario.

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