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.

Data Lakes 64

Data Lakes Data Analysis Data Analysis Big Data 64

Alation

SEPTEMBER 23, 2021

Active Governance – Active data governance creates usage-based assignments, which prioritize and delegate curation duties. It also allows for deeper analytics and visibility into people, data, and documentation. It also catalogs datasets and operations that includes data preparation features and functions.

Data Preparation 52

Data Preparation SQL Data Governance Data Analysis 52

phData

SEPTEMBER 28, 2023

Dataflows represent a cloud-based technology designed for data preparation and transformation purposes. Dataflows have different connectors to retrieve data, including databases, Excel files, APIs, and other similar sources, along with data manipulations that are performed using Online Power Query Editor.

Power BI 78

Power BI Data Preparation Machine Learning Machine Learning 78

The MLOps Blog

JUNE 27, 2023

User support arrangements Consider the availability and quality of support from the provider or vendor, including documentation, tutorials, forums, customer service, etc. Check out the Kubeflow documentation. Metaflow Metaflow helps data scientists and machine learning engineers build, manage, and deploy data science projects.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

IBM Journey to AI blog

JULY 17, 2023

These encoder-only architecture models are fast and effective for many enterprise NLP tasks, such as classifying customer feedback and extracting information from large documents. While they require task-specific labeled data for fine tuning, they also offer clients the best cost performance trade-off for non-generative use cases.

AI 74

AI AI Machine Learning Machine Learning 74

Pickl AI

OCTOBER 10, 2024

Informatica’s AI-powered automation helps streamline data pipelines and improve operational efficiency. Common use cases include integrating data across hybrid cloud environments, managing data lakes, and enabling real-time analytics for Business Intelligence platforms.

Data Quality 52

Data Quality AWS Machine Learning Machine Learning 52

The MLOps Blog

OCTOBER 20, 2023

References : Links to internal or external documentation with background information or specific information used within the analysis presented in the notebook. Data to explore: Outline the tables or datasets you’re exploring/analyzing and reference their sources or link their data catalog entries. documentation.

SQL 52

SQL Database Data Scientist Python 52

Pickl AI

SEPTEMBER 5, 2024

Storage Solutions: Secure and scalable storage options like Azure Blob Storage and Azure Data Lake Storage. Key features and benefits of Azure for Data Science include: Scalability: Easily scale resources up or down based on demand, ideal for handling large datasets and complex computations.

Azure 52

Azure Data Scientist Machine Learning Machine Learning 52

Snorkel AI

MARCH 2, 2023

In terms of technology: generating code snippets, code translation, and automated documentation. In financial services: summary of financial documents, entity extraction. Data preparation, train and tune, deploy and monitor. We have data pipelines and data preparation. It can cover the gamut.

Machine Learning 52

Machine Learning Machine Learning Data Preparation AI 52

Snorkel AI

MARCH 2, 2023

In terms of technology: generating code snippets, code translation, and automated documentation. In financial services: summary of financial documents, entity extraction. Data preparation, train and tune, deploy and monitor. We have data pipelines and data preparation. It can cover the gamut.

Machine Learning 52

Machine Learning Machine Learning Data Preparation AI 52

phData

DECEMBER 4, 2023

For greater detail, see the Snowflake documentation. If you answer “yes” to any of these questions, you will need cloud storage, such as Amazon AWS’s S3, Azure Data Lake Storage or GCP’s Google Storage. Knowing this, you want to have data prepared in a way to optimize your load.

Clustering 98

Clustering Database SQL Data Pipeline 98

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.

AWS 140

AWS ML ML AI 140

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.

ML 98

ML ML Machine Learning Machine Learning 98