Alation

FEBRUARY 20, 2020

The rise of data lakes, IOT analytics, and big data pipelines has introduced a new world of fast, big data. How Data Catalogs Can Help. Data catalogs evolved as a key component of the data governance revolution by creating a bridge between the new world and old world of data governance.

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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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Alation

JANUARY 26, 2023

That’s why many organizations invest in technology to improve data processes, such as a machine learning data pipeline. However, data needs to be easily accessible, usable, and secure to be useful — yet the opposite is too often the case. How can data engineers address these challenges directly?

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Alation

MAY 16, 2023

But with the sheer amount of data continually increasing, how can a business make sense of it? Robust data pipelines. What is a Data Pipeline? A data pipeline is a series of processing steps that move data from its source to its destination. The answer?

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DagsHub

APRIL 7, 2024

Image generated with Midjourney In today’s fast-paced world of data science, building impactful machine learning models relies on much more than selecting the best algorithm for the job. A primer on ML workflows and pipelines Before exploring the tools, we first need to explain the difference between ML workflows and pipelines.

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Precisely

JANUARY 9, 2023

The best data observability tools use advanced technology to apply machine learning intelligence, watching for patterns in enterprise data and alerting data stewards whenever anomalies crop up.

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IBM Journey to AI blog

AUGUST 12, 2024

Instead, businesses tend to rely on advanced tools and strategies—namely artificial intelligence for IT operations (AIOps) and machine learning operations (MLOps)—to turn vast quantities of data into actionable insights that can improve IT decision-making and ultimately, the bottom line.

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AWS Machine Learning Blog

FEBRUARY 13, 2024

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. This provides an audit trail required for governance and compliance. Their task is to construct and oversee efficient data pipelines. Take “age” for instance.

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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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Dataconomy

FEBRUARY 23, 2023

This involves creating data validation rules, monitoring data quality, and implementing processes to correct any errors that are identified. Creating data pipelines and workflows Data engineers create data pipelines and workflows that enable data to be collected, processed, and analyzed efficiently.

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Precisely

SEPTEMBER 30, 2024

Key Takeaways By deploying technologies that can learn and improve over time, companies that embrace AI and machine learning can achieve significantly better results from their data quality initiatives. Here are five data quality best practices which business leaders should focus.

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DagsHub

OCTOBER 23, 2024

Unstructured data makes up 80% of the world's data and is growing. Managing unstructured data is essential for the success of machine learning (ML) projects. Without structure, data is difficult to analyze and extracting meaningful insights and patterns is challenging.

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Tableau

OCTOBER 8, 2021

Connecting directly to this semantic layer will help give customers access to critical business data in a safe, governed manner. This partnership makes data more accessible and trusted. Our continued investments in connectivity with Google technologies help ensure your data is secure, governed, and scalable.

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Pickl AI

NOVEMBER 4, 2024

Key components include data modelling, warehousing, pipelines, and integration. Effective data governance enhances quality and security throughout the data lifecycle. What is Data Engineering? They are crucial in ensuring data is readily available for analysis and reporting. from 2025 to 2030.

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Pickl AI

JULY 25, 2023

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Read more to know.

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Pickl AI

OCTOBER 10, 2023

In today’s fast-paced business environment, the significance of Data Observability cannot be overstated. Data Observability enables organizations to detect anomalies, troubleshoot issues, and maintain data pipelines effectively. How Are Data Quality and Data Observability Similar—and How Are They Different?

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Pickl AI

OCTOBER 11, 2023

What is Data Observability? It is the practice of monitoring, tracking, and ensuring data quality, reliability, and performance as it moves through an organization’s data pipelines and systems. Data quality tools help maintain high data quality standards. Tools Used in Data Observability?

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IBM Journey to AI blog

AUGUST 4, 2023

Data democratization instead refers to the simplification of all processes related to data, from storage architecture to data management to data security. It also requires an organization-wide data governance approach, from adopting new types of employee training to creating new policies for data storage.

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IBM Journey to AI blog

OCTOBER 12, 2023

They’re built on machine learning algorithms that create outputs based on an organization’s data or other third-party big data sources. And that makes sense.

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ODSC - Open Data Science

JUNE 19, 2023

In particular, its progress depends on the availability of related technologies that make the handling of huge volumes of data possible. These technologies include the following: Data governance and management  — It is crucial to have a solid data management system and governance practices to ensure data accuracy, consistency, and security.

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phData

MAY 28, 2024

In this post, we will explore the unique features of each tool, helping you understand their strengths and guiding you on how to choose the right solution for your data needs. The primary advantage of Snowflake Cortex is its ability to democratize AI by making powerful machine learning tools accessible to a broader range of users.

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Tableau

OCTOBER 8, 2021

Connecting directly to this semantic layer will help give customers access to critical business data in a safe, governed manner. This partnership makes data more accessible and trusted. Our continued investments in connectivity with Google technologies help ensure your data is secure, governed, and scalable. .

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The MLOps Blog

MARCH 20, 2023

Long-term ML project involves developing and sustaining applications or systems that leverage machine learning models, algorithms, and techniques. However, in scenarios where dataset versioning solutions are leveraged, there can still be various challenges experienced by ML/AI/Data teams.

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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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Pickl AI

OCTOBER 17, 2024

Automation Automation plays a pivotal role in streamlining ETL processes, reducing the need for manual intervention, and ensuring consistent data availability. By automating key tasks, organisations can enhance efficiency and accuracy, ultimately improving the quality of their data pipelines.

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Alation

APRIL 14, 2022

Semantics, context, and how data is tracked and used mean even more as you stretch to reach post-migration goals. This is why, when data moves, it’s imperative for organizations to prioritize data discovery. Data discovery is also critical for data governance , which, when ineffective, can actually hinder organizational growth.

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Alation

JUNE 21, 2022

The product concept back then went something like: In a world where enterprises have numerous sources of data, let’s make a thing that helps people find the best data asset to answer their question based on what other users were using. And to determine “best,” we’d ingest log files and leverage machine learning.

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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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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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ODSC - Open Data Science

FEBRUARY 5, 2025

This May, were heading to Boston for ODSC East 2025, where data scientists, AI engineers, and industry leaders will gather to explore the latest advancements in AI, machine learning, and data engineering. The wait is almost over!

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Pickl AI

OCTOBER 10, 2024

Summary: Data transformation tools streamline data processing by automating the conversion of raw data into usable formats. These tools enhance efficiency, improve data quality, and support Advanced Analytics like Machine Learning. Aggregation : Combining multiple data points into a single summary (e.g.,

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ODSC - Open Data Science

APRIL 26, 2023

For example, a data scientist would be a good fit for a team that is in charge of handling large swaths of data and creating actionable insights from them. In another industry what matters is being able to predict behaviors in the medium and short terms, and this is where a machine learning engineer might come to play.

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Precisely

MAY 4, 2023

A broken data pipeline might bring operational systems to a halt, or it could cause executive dashboards to fail, reporting inaccurate KPIs to top management. Is your data governance structure up to the task? Read What Is Data Observability? Complexity leads to risk.

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phData

MARCH 22, 2023

As companies continue to adopt machine learning (ML) in their workflows, the demand for scalable and efficient tools has increased. Transitioning work to Snowpark allows for faster ML deployment, easier scaling, and robust data pipeline development. To make things super simple, we’ll use Hex for each inference mode.

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DataRobot Blog

OCTOBER 3, 2017

Additionally, Alation and Paxata announced the new data exploration capabilities of Paxata in the Alation Data Catalog, where users can find trusted data assets and, with a single click, work with their data in Paxata’s Self-Service Data Prep Application. 3) Data professionals come in all shapes and forms.

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phData

FEBRUARY 14, 2023

Insurance companies often face challenges with data silos and inconsistencies among their legacy systems. To address these issues, they need a centralized and integrated data platform that serves as a single source of truth, preferably with strong data governance capabilities.

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Precisely

FEBRUARY 2, 2023

Pipelines must have robust data integration capabilities that integrate data from multiple data silos, including the extensive list of applications used throughout the organization, databases and even mainframes. This makes an executive’s confidence in the data paramount.

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