Snorkel AI

DECEMBER 2, 2024

This reduces the reliance on manual data labeling and significantly speeds up the model training process. At its core, Snorkel Flow empowers data scientists and domain experts to encode their knowledge into labeling functions, which are then used to generate high-quality training datasets.

Data Ingestion 52

Data Ingestion Large Language Models LLM Machine Learning 52

ODSC - Open Data Science

JULY 26, 2024

You’ll explore data ingestion from multiple sources, preprocessing unstructured data into a normalized format that facilitates uniform chunking across various file types, and metadata extraction. You’ll also discuss loading processed data into destination storage. Sign me up! Are you intrigued?

Data Scientist 52

Data Scientist Robotics Metadata Data Science 52

AWS Machine Learning Blog

APRIL 26, 2023

Ensuring data quality, governance, and security may slow down or stall ML projects. The second is by using SageMaker to help data scientists and ML engineers build, train, and deploy custom ML models. We recognize that customers have different starting points.

ML 114

ML Machine Learning Data Science Data Drift 114

Snorkel AI

JANUARY 24, 2023

Users are able to rapidly improve training data quality and model performance using integrated error analysis to develop highly accurate and adaptable AI applications. Labeled data can be loaded back into Snowflake as structured data. Models built in Snorkel Flow can be registered on Snowflake as Snowpark UDFs.

Data Ingestion 52

Data Ingestion Machine Learning Data Science ML 52

Snorkel AI

JANUARY 24, 2023

Users are able to rapidly improve training data quality and model performance using integrated error analysis to develop highly accurate and adaptable AI applications. Labeled data can be loaded back into Snowflake as structured data. Models built in Snorkel Flow can be registered on Snowflake as Snowpark UDFs.

Data Ingestion 52

Data Ingestion Machine Learning Data Science ML 52

Pickl AI

AUGUST 1, 2023

It involves the design, development, and maintenance of systems, tools, and processes that enable the acquisition, storage, processing, and analysis of large volumes of data. Data Engineers work to build and maintain data pipelines, databases, and data warehouses that can handle the collection, storage, and retrieval of vast amounts of data.

Big Data 52

Big Data Data Analysis Data Scientist Data Science 52

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 Data Ingestion Automation 52

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

The MLOps Blog

MAY 9, 2023

The platform typically includes components for the ML ecosystem like data management, feature stores, experiment trackers, a model registry, a testing environment, model serving, and model management. Data validation (writing tests to check for data quality). Data preprocessing. CSV, Parquet, etc.)

ML 98

ML Machine Learning Metadata Data Science 98

The MLOps Blog

AUGUST 11, 2023

Getting a workflow ready which takes your data from its raw form to predictions while maintaining responsiveness and flexibility is the real deal. At that point, the Data Scientists or ML Engineers become curious and start looking for such implementations. 1 Data Ingestion (e.g.,

ML 52

ML Machine Learning Data Ingestion Deep Learning 52

AWS Machine Learning Blog

SEPTEMBER 18, 2024

Hosted on Amazon ECS with tasks run on Fargate, this platform streamlines the end-to-end ML workflow, from data ingestion to model deployment. An example direct acyclic graph (DAG) might automate data ingestion, processing, model training, and deployment tasks, ensuring that each step is run in the correct order and at the right time.

Machine Learning 119

Machine Learning Data Scientist ML Data Ingestion 119

Pickl AI

MAY 15, 2024

This guide unlocks the path from Data Analyst to Data Scientist Architect. Data Quality and Standardization The adage “garbage in, garbage out” holds true. Inconsistent data formats, missing values, and data bias can significantly impact the success of large-scale Data Science projects.

Data Scientist 52

Data Scientist Data Science Machine Learning Data Quality 52

The MLOps Blog

MARCH 21, 2023

From gathering and processing data to building models through experiments, deploying the best ones, and managing them at scale for continuous value in production—it’s a lot. As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69