Pickl AI

APRIL 14, 2023

With the exponential growth of data and increasing complexities of the ecosystem, organizations face the challenge of ensuring data security and compliance with regulations. Although Data Governance is not mandatory, it works with data quality and Master Data Management Tools.

Data Platform 52

Data Platform Data Integration Automation Data Ingestion 52

Pickl AI

AUGUST 1, 2023

The key sectors where Data Engineering has a major contribution include IT, Internet/eCommerce, and Banking & Insurance. Salary of a Data Engineer ranges between ₹ 3.1 Data Storage: Storing the collected data in various storage systems, such as relational databases, NoSQL databases, data lakes, or data warehouses.

Big Data 52

Big Data Data Analysis Data Scientist Data Ingestion 52

The MLOps Blog

MARCH 15, 2023

A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process. Data Ingestion : Involves raw data collection from origin and storage using architectures such as batch, streaming or event-driven.

ETL 59

ETL Categorization Automation Data Integration 59

ODSC - Open Data Science

SEPTEMBER 29, 2023

A 2019 survey by McKinsey on global data transformation revealed that 30 percent of total time spent by enterprise IT teams was spent on non-value-added tasks related to poor data quality and availability.

Big Data 52

Big Data ETL Data Science Data Ingestion 52

Unite.AI

DECEMBER 5, 2023

Traditional Data Warehouse Architecture Bottom Tier (Database Server): This tier is responsible for storing (a process known as data ingestion ) and retrieving data. The data ecosystem is connected to company-defined data sources that can ingest historical data after a specified period.

Metadata 162

Metadata Big Data ETL Data Ingestion 162

AWS Machine Learning Blog

NOVEMBER 15, 2023

Therefore, when the Principal team started tackling this project, they knew that ensuring the highest standard of data security such as regulatory compliance, data privacy, and data quality would be a non-negotiable, key requirement.

Data Ingestion 110

Data Ingestion Metadata NLP Data Scientist 110

The MLOps Blog

JUNE 27, 2023

Core features of end-to-end MLOps platforms End-to-end MLOps platforms combine a wide range of essential capabilities and tools, which should include: Data management and preprocessing : Provide capabilities for data ingestion, storage, and preprocessing, allowing you to efficiently manage and prepare data for training and evaluation.

Machine Learning 134

Machine Learning Metadata Data Quality Data Scientist 134

Snorkel AI

JANUARY 24, 2023

Data science and machine learning teams use Snorkel Flow’s programmatic labeling to intelligently capture knowledge from various sources such as previously labeled data (even when imperfect), heuristics from subject matter experts, business logic, and even the latest foundation models, then scale this knowledge to label large quantities of data.

Data Ingestion 52

Data Ingestion AI AI Machine Learning 52

Snorkel AI

JANUARY 24, 2023

Data science and machine learning teams use Snorkel Flow’s programmatic labeling to intelligently capture knowledge from various sources such as previously labeled data (even when imperfect), heuristics from subject matter experts, business logic, and even the latest foundation models, then scale this knowledge to label large quantities of data.

Data Ingestion 52

Data Ingestion AI AI Machine Learning 52

AWS Machine Learning Blog

APRIL 26, 2023

Ensuring data quality, governance, and security may slow down or stall ML projects. Data engineering – Identifies the data sources, sets up data ingestion and pipelines, and prepares data using Data Wrangler.

ML 88

ML Machine Learning Data Science Data Drift 88

The MLOps Blog

AUGUST 11, 2023

1 Data Ingestion (e.g., Apache Kafka, Amazon Kinesis) 2 Data Preprocessing (e.g., The next section delves into these architectural patterns, exploring how they are leveraged in machine learning pipelines to streamline data ingestion, processing, model training, and deployment.

ML 52

ML Machine Learning Data Ingestion Deep Learning 52

The MLOps Blog

MAY 9, 2023

The components comprise implementations of the manual workflow process you engage in for automatable steps, including: Data ingestion (extraction and versioning). Data validation (writing tests to check for data quality). Data preprocessing. Let’s briefly go over each of the components below.

ML 98

ML Machine Learning Metadata Automation 98

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.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69