Data Science Dojo

MARCH 24, 2023

Look no further than MLOps – the future of ML deployment. Machine Learning (ML) has become an increasingly valuable tool for businesses and organizations to gain insights and make data-driven decisions. However, deploying and maintaining ML models can be a complex and time-consuming process.

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IBM Journey to AI blog

SEPTEMBER 5, 2024

The best way to build a strong foundation for data success is through effective data governance. Access to high-quality data can help organizations start successful products, defend against digital attacks, understand failures and pivot toward success.

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ODSC - Open Data Science

APRIL 28, 2023

Be sure to check out her talk, “ Power trusted AI/ML Outcomes with Data Integrity ,” there! Due to the tsunami of data available to organizations today, artificial intelligence (AI) and machine learning (ML) are increasingly important to businesses seeking competitive advantage through digital transformation.

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Dataversity

MARCH 14, 2022

The post When It Comes to Data Quality, Businesses Get Out What They Put In appeared first on DATAVERSITY. The stakes are high, so you search the web and find the most revered chicken parmesan recipe around. At the grocery store, it is immediately clear that some ingredients are much more […].

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Pickl AI

OCTOBER 18, 2023

How to Scale Your Data Quality Operations with AI and ML: In the fast-paced digital landscape of today, data has become the cornerstone of success for organizations across the globe. Every day, companies generate and collect vast amounts of data, ranging from customer information to market trends.

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AWS Machine Learning Blog

APRIL 26, 2023

Do you need help to move your organization’s Machine Learning (ML) journey from pilot to production? Most executives think ML can apply to any business decision, but on average only half of the ML projects make it to production. Challenges Customers may face several challenges when implementing machine learning (ML) solutions.

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Precisely

AUGUST 29, 2024

Key Takeaways Data quality ensures your data is accurate, complete, reliable, and up to date – powering AI conclusions that reduce costs and increase revenue and compliance. Data observability continuously monitors data pipelines and alerts you to errors and anomalies. What does “quality” data mean, exactly?

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Precisely

FEBRUARY 26, 2025

If pain points like these ring true for you, theres great news weve just announced significant enhancements to our Precisely Data Integrity Suite that directly target these challenges! Then, youll be ready to unlock new efficiencies and move forward with confident data-driven decision-making.

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Data Science Dojo

AUGUST 24, 2023

ML models have grown significantly in recent years, and businesses increasingly rely on them to automate and optimize their operations. However, managing ML models can be challenging, especially as models become more complex and require more resources to train and deploy. What is MLOps?

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Dataversity

APRIL 1, 2024

This reliance has spurred a significant shift across industries, driven by advancements in artificial intelligence (AI) and machine learning (ML), which thrive on comprehensive, high-quality data.

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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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Precisely

FEBRUARY 12, 2024

Here are some of the key trends and challenges facing telecommunications companies today: The growth of AI and machine learning: Telecom companies use artificial intelligence and machine learning (AI/ML) for predictive analytics and network troubleshooting. This shortfall in effective data governance inhibits visibility and transparency.

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Pickl AI

OCTOBER 19, 2023

Data quality plays a significant role in helping organizations strategize their policies that can keep them ahead of the crowd. Hence, companies need to adopt the right strategies that can help them filter the relevant data from the unwanted ones and get accurate and precise output.

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DagsHub

AUGUST 23, 2024

As such, the quality of their data can make or break the success of the company. This article will guide you through the concept of a data quality framework, its essential components, and how to implement it effectively within your organization. What is a data quality framework?

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Dataversity

JANUARY 13, 2025

Data has become a driving force behind change and innovation in 2025, fundamentally altering how businesses operate. Across sectors, organizations are using advancements in artificial intelligence (AI), machine learning (ML), and data-sharing technologies to improve decision-making, foster collaboration, and uncover new opportunities.

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Alation

AUGUST 5, 2021

What is Data Quality? Data quality is defined as: the degree to which data meets a company’s expectations of accuracy, validity, completeness, and consistency. By tracking data quality , a business can pinpoint potential issues harming quality, and ensure that shared data is fit to be used for a given purpose.

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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. and Pandas or Apache Spark DataFrames.

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Alation

SEPTEMBER 7, 2021

In the previous blog , we discussed how Alation provides a platform for data scientists and analysts to complete projects and analysis at speed. In this blog we will discuss how Alation helps minimize risk with active data governance. So why are organizations not able to scale governance? Meet Governance Requirements.

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Alation

JUNE 2, 2022

In part one of this series, I discussed how data management challenges have evolved and how data governance and security have to play in such challenges, with an eye to cloud migration and drift over time. Governing and Tracking ML/AI: The Rise of XAI. However, governance processes are equally important.

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AWS Machine Learning Blog

NOVEMBER 7, 2024

Here you also have the data sources, processing pipelines, vector stores, and data governance mechanisms that allow tenants to securely discover, access, andthe data they need for their specific use case. At this point, you need to consider the use case and data isolation requirements.

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Precisely

DECEMBER 20, 2022

Read more > Read our eBook 4 Keys to Improving Data Quality This eBook will guide you as to how to overcome the root problems of data quality but also clarifying the roles of data quality management and data governance in resolving them. Data governance provides the answer.

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Dataversity

DECEMBER 20, 2022

The post Data Quality Best Practices to Discover the Hidden Potential of Dirty Data in Health Care appeared first on DATAVERSITY. Health plans will […].

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Precisely

OCTOBER 17, 2024

They overcame early data challenges by using Precisely Points of Interest (POI) data – which leverages market-leading geo addressing solutions. As a result, City Forward has over 30 ML models with an average accuracy of 95% – enabling a foundation for highly granular, AI-driven insights for smarter investment decisions.

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Alation

JULY 19, 2021

This requires a metadata management solution to enable data search & discovery and data governance, both of which empower access to both the metadata and the underlying data to those who need it. In today’s world, metadata management best practices call for a data catalog. Administrative information.

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Precisely

MARCH 7, 2024

Yet experts warn that without proactive attention to data quality and data governance, AI projects could face considerable roadblocks. Data Quality and Data Governance Insurance carriers cannot effectively leverage artificial intelligence without first having a clear data strategy in place.

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Precisely

AUGUST 3, 2023

They reported facing challenges to the success of their data programs — including cost (50%), lack of effective data management tools (45%), poor data literacy/program adoption (41%), and skills shortages (36%) as well as poor data quality (36%).

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Dataversity

MARCH 17, 2022

That’s why, aside from its people, data is the most important thing an organization owns. Data must be the first stop on the journey to implementing artificial […]. The post How to Prepare Data for AI and ML appeared first on DATAVERSITY.

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AWS Machine Learning Blog

NOVEMBER 16, 2023

Amazon SageMaker provides purpose-built tools for machine learning operations (MLOps) to help automate and standardize processes across the ML lifecycle. In this post, we describe how Philips partnered with AWS to develop AI ToolSuite—a scalable, secure, and compliant ML platform on SageMaker.

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Precisely

SEPTEMBER 26, 2024

Key Takeaways: Trusted AI requires data integrity. For AI-ready data, focus on comprehensive data integration, data quality and governance, and data enrichment. Building data literacy across your organization empowers teams to make better use of AI tools. The stakes are very high.

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Pickl AI

OCTOBER 17, 2024

Best Practices for ETL Efficiency Maximising efficiency in ETL (Extract, Transform, Load) processes is crucial for organisations seeking to harness the power of data. Implementing best practices can improve performance, reduce costs, and improve data quality. It also makes predictions for the future of ETL processes.

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phData

AUGUST 5, 2024

Who should have access to sensitive data? How can my analysts discover where data is located? All of these questions describe a concept known as data governance. The Snowflake AI Data Cloud has built an entire blanket of features called Horizon, which tackles all of these questions and more.

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IBM Journey to AI blog

DECEMBER 16, 2022

An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.

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Alation

JUNE 21, 2022

Over time, we called the “thing” a data catalog , blending the Google-style, AI/ML-based relevancy with more Yahoo-style manual curation and wikis. Thus was born the data catalog. In our early days, “people” largely meant data analysts and business analysts. ML and DataOps teams). Privacy staff wanted tagging.

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Precisely

SEPTEMBER 11, 2023

It provides a unique ability to automate or accelerate user tasks, resulting in benefits like: improved efficiency greater productivity reduced dependence on manual labor Let’s look at AI-enabled data quality solutions as an example. Problem: “We’re unsure about the quality of our existing data and how to improve it!”

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Alation

SEPTEMBER 30, 2022

This June, Snowflake recognized Alation as its data governance partner of the year for the second year in a row, and Eckerson , IDC , BARC , Dresner , and Constellation all released reports just this summer naming Alation a data catalog leader. Everything and Everyone: The Catalog is the platform for Data Intelligence.

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IBM Journey to AI blog

DECEMBER 20, 2023

This calls for the organization to also make important decisions regarding data, talent and technology: A well-crafted strategy will provide a clear plan for managing, analyzing and leveraging data for AI initiatives. Identify potential partners and vendors Find companies in the AI and ML space that have worked within your industry.

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Precisely

JULY 18, 2024

Data management recommendations and data products emerge dynamically from the fabric through automation, activation, and AI/ML analysis of metadata. As data grows exponentially, so do the complexities of managing and leveraging it to fuel AI and analytics.

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