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Data architecture strategy for data quality

IBM Journey to AI blog

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

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. For example, neptune.ai Is it fast and reliable enough for your workflow?

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Top 10 Reasons for Alation with Snowflake: Reduce Risk with Active Data Governance

Alation

In addition, Alation provides a quick preview and sample of the data to help data scientists and analysts with greater data quality insights. Alation’s deep data profiling helps data scientists and analysts get important data profiling insights. In Summary.

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Capital One’s data-centric solutions to banking business challenges

Snorkel AI

Model-ready data refers to a feature library. For example, where verified data is present, the latencies are quantified. It enables users to aggregate, compute, and transform data in some scripted way, thereby promoting feature engineering, innovation, and reuse of data. It is essentially a Python library.

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Capital One’s data-centric solutions to banking business challenges

Snorkel AI

Model-ready data refers to a feature library. For example, where verified data is present, the latencies are quantified. It enables users to aggregate, compute, and transform data in some scripted way, thereby promoting feature engineering, innovation, and reuse of data. It is essentially a Python library.