AWS Machine Learning Blog

FEBRUARY 21, 2025

The agent knowledge base stores Amazon Bedrock service documentation, while the cache knowledge base contains curated and verified question-answer pairs. For this example, you will ingest Amazon Bedrock documentation in the form of the User Guide PDF into the Amazon Bedrock knowledge base. This will be the primary dataset.

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Flipboard

FEBRUARY 19, 2025

Robust data processing pipeline A sophisticated data processing pipeline maintains Alfreds knowledge base and promotes high-quality responses. Amazon S3 serves as the central data lake, providing scalable object storage for raw documents with versioning and lifecycle management capabilities.

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Alation

AUGUST 4, 2022

And where data was available, the ability to access and interpret it proved problematic. Big data can grow too big fast. Left unchecked, data lakes became data swamps. Some data lake implementations required expensive ‘cleansing pumps’ to make them navigable again.

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ODSC - Open Data Science

FEBRUARY 24, 2023

Data Quality Now that you’ve learned more about your data and cleaned it up, it’s time to ensure the quality of your data is up to par. With these data exploration tools, you can determine if your data is accurate, consistent, and reliable.

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Pickl AI

DECEMBER 2, 2024

As organisations grapple with this vast amount of information, understanding the main components of Big Data becomes essential for leveraging its potential effectively. Key Takeaways Big Data originates from diverse sources, including IoT and social media. Data lakes and cloud storage provide scalable solutions for large datasets.

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Pickl AI

NOVEMBER 25, 2024

As organisations grapple with this vast amount of information, understanding the main components of Big Data becomes essential for leveraging its potential effectively. Key Takeaways Big Data originates from diverse sources, including IoT and social media. Data lakes and cloud storage provide scalable solutions for large datasets.

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Alation

JULY 19, 2022

For example, a new data scientist who is curious about which customers are most likely to be repeat buyers, might search for customer data only to discover an article documenting a previous project that answered their exact question. Modern data catalogs also facilitate data quality checks.

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Alation

APRIL 18, 2023

For any data user in an enterprise today, data profiling is a key tool for resolving data quality issues and building new data solutions. In this blog, we’ll cover the definition of data profiling, top use cases, and share important techniques and best practices for data profiling today.

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Alation

FEBRUARY 13, 2020

Figure 1 illustrates the typical metadata subjects contained in a data catalog. Figure 1 – Data Catalog Metadata Subjects. Datasets are the files and tables that data workers need to find and access. They may reside in a data lake, warehouse, master data repository, or any other shared data resource.

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IBM Journey to AI blog

JULY 27, 2023

Precisely conducted a study that found that within enterprises, data scientists spend 80% of their time cleaning, integrating and preparing data , dealing with many formats, including documents, images, and videos. Overall placing emphasis on establishing a trusted and integrated data platform for AI.

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IBM Journey to AI blog

JULY 3, 2024

As data types and applications evolve, you might need specialized NoSQL databases to handle diverse data structures and specific application requirements. Enterprises might also have petabytes, if not exabytes, of valuable proprietary data stored in their mainframe that needs to be unlocked for new insights and ML/AI models.

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DagsHub

OCTOBER 23, 2024

To combine the collected data, you can integrate different data producers into a data lake as a repository. A central repository for unstructured data is beneficial for tasks like analytics and data virtualization. Data Cleaning The next step is to clean the data after ingesting it into the data lake.

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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. The right tool can significantly enhance efficiency, scalability, and data quality.

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Flipboard

MAY 9, 2025

Additional considerations Though the potential of this approach is significant, there are several challenges to consider: Data quality High-quality, diverse input data is key to effective model performance. He is focused on big data, data lakes, streaming and batch analytics services, and generative AI technologies.

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Pickl AI

APRIL 21, 2025

Semi-Structured Data: Data that has some organizational properties but doesn’t fit a rigid database structure (like emails, XML files, or JSON data used by websites). Unstructured Data: Data with no predefined format (like text documents, social media posts, images, audio files, videos).

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IBM Journey to AI blog

OCTOBER 12, 2023

To optimize data analytics and AI workloads, organizations need a data store built on an open data lakehouse architecture. This type of architecture combines the performance and usability of a data warehouse with the flexibility and scalability of a data lake.

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Iguazio

JUNE 25, 2023

Homogeneity, Completeness, and V-measure - Measures different aspects of cluster quality, such as the extent to which each cluster contains only samples from a single class (homogeneity), the extent to which all samples from the same class are assigned to the same cluster (completeness), and their harmonic mean (V-measure).

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Alation

FEBRUARY 17, 2023

How do you get executives to understand the value of data governance? First, document your successes of good data, and how it happened. Share stories of data in good times and in bad (pictures help!). Some data seems more analytical, while other is operational (external facing). Seeing is believing!

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DagsHub

NOVEMBER 11, 2024

So, we must understand the different unstructured data types and effectively process them to uncover hidden patterns. Textual Data Textual data is one of the most common forms of unstructured data and can be in the format of documents, social media posts, emails, web pages, customer reviews, or conversation logs.

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Pickl AI

JANUARY 28, 2025

External Data Sources: These can be market research data, social media feeds, or third-party databases that provide additional insights. Data can be structured (e.g., documents and images). The diversity of data sources allows organizations to create a comprehensive view of their operations and market conditions.

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phData

SEPTEMBER 20, 2023

Traditional data management approaches often involve centralizing data in a data warehouse or data lake, leading to challenges like data silos, data ownership issues, and data access and processing bottlenecks. What are the Advantages and Disadvantages of Data Mesh?

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Alation

SEPTEMBER 23, 2021

Behavioral intelligence, embedded in the catalog, learns from user behavior to enforce best practices through features like data quality flags, which help folks stay compliant as they use data. Active Governance – Active data governance creates usage-based assignments, which prioritize and delegate curation duties.

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Pickl AI

APRIL 9, 2025

This includes operations like data validation, data cleansing, data aggregation, and data normalization. The goal is to ensure that the data is consistent and ready for analysis. Loading : Storing the transformed data in a target system like a data warehouse, data lake, or even a database.

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ODSC - Open Data Science

OCTOBER 15, 2023

Cloudera Cloudera is a cloud-based platform that provides businesses with the tools they need to manage and analyze data. They offer a variety of services, including data warehousing, data lakes, and machine learning. ArangoDB ArangoDB is a company that provides a database platform for graph and document data.

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Alation

MARCH 21, 2023

Having been in business for over 50 years, ARC had accumulated a massive amount of data that was stored in siloed, on-premises servers across its 7 business domains. Using Alation, ARC automated the data curation and cataloging process. “So

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The MLOps Blog

MARCH 21, 2023

Olalekan said that most of the random people they talked to initially wanted a platform to handle data quality better, but after the survey, he found out that this was the fifth most crucial need. And when the platform automates the entire process, it’ll likely produce and deploy a bad-quality model.

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DagsHub

FEBRUARY 29, 2024

Only once you form a clear definition and understanding of the business problem , goals, and the necessity of machine learning should you move forward to the next stage of data preparation. In large ML organizations, there is typically a dedicated team for all the above aspects of data preparation.

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phData

MAY 6, 2024

Data Quality Next, dive into the details of your data. This means bringing together one or more of: Behavioral data like website visits, purchases, engagement with emails, and ads. Store this data in a customer data platform or data lake. What needs are they addressing?

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AWS Machine Learning Blog

JANUARY 26, 2024

Accelerate your security and AI/ML learning with best practices guidance, training, and certification AWS also curates recommendations from Best Practices for Security, Identity, & Compliance and AWS Security Documentation to help you identify ways to secure your training, development, testing, and operational environments.

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The MLOps Blog

MAY 9, 2023

The pipelines are interoperable to build a working system: Data (input) pipeline (data acquisition and feature management steps) This pipeline transports raw data from one location to another. Model/training pipeline This pipeline trains one or more models on the training data with preset hyperparameters.

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AWS Machine Learning Blog

OCTOBER 24, 2024

Large language models (LLMs) are very large deep-learning models that are pre-trained on vast amounts of data. One model can perform completely different tasks such as answering questions, summarizing documents, translating languages, and completing sentences. Data must be preprocessed to enable semantic search during inference.

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