Snorkel AI

JUNE 22, 2023

Jacomo Corbo is a Partner and Chief Scientist, and Bryan Richardson is an Associate Partner and Senior Data Scientist, for QuantumBlack AI by McKinsey. They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022.

Data Quality 52

Data Quality Automation Data Scientist ML 52

DataRobot Blog

MAY 10, 2022

In this post, we will dive deeper into the first component of managing model risk, and look at opportunities at how automation provided by DataRobot brings about efficiencies in the development and implementation of models. . With this definition of model risk, how do we ensure the models we build are technically correct?

Automation 64

Automation Machine Learning Data Quality Algorithm 64

Marktechpost

JUNE 16, 2024

Go to Definition: This feature lets users right-click on any Python variable or function to access its definition. This facilitates seamless navigation through the codebase, allowing users to locate and understand variable or function definitions quickly. This visual aid helps developers quickly identify and correct mistakes.

Data Ingestion 116

Data Ingestion Python Automation Data Scientist 116

AWS Machine Learning Blog

FEBRUARY 27, 2024

The SageMaker project template includes seed code corresponding to each step of the build and deploy pipelines (we discuss these steps in more detail later in this post) as well as the pipeline definition—the recipe for how the steps should be run. Workflow B corresponds to model quality drift checks.

Machine Learning 129

Machine Learning DevOps Data Scientist Data Quality 129

IBM Journey to AI blog

DECEMBER 4, 2023

The software provides an integrated and unified platform for disparate business processes such as supply chain management and human resources , providing a holistic view of an organization’s operations and breaking down data silos. Using automation , Oracle can simplify routine tasks to increase operational efficiency.

Data Quality 226

Data Quality Automation 226

Marktechpost

FEBRUARY 23, 2025

This article offers a measured exploration of AI agents, examining their definition, evolution, types, real-world applications, and technical architecture. Defining AI Agents At its simplest, an AI agent is an autonomous software entity capable of perceiving its surroundings, processing data, and taking action to achieve specified goals.

Natural Language Processing 54

Natural Language Processing Machine Learning AI AI 54

AWS Machine Learning Blog

NOVEMBER 15, 2024

You define a denied topic by providing a natural language definition of the topic along with a few optional example phrases of the topic. If you are planning on using automated model evaluation for toxicity, start by defining what constitutes toxic content for your specific application. Therefore, consider risk management here.

Responsible AI 103

Responsible AI Prompt Engineer Prompt Engineering AI 103

Marktechpost

MAY 8, 2024

From basic driver assistance to fully autonomous vehicles(AVs) capable of navigating without human intervention, the progression is evident through the SAE Levels of vehicle automation. Different definitions of safety exist, from risk reduction to minimizing harm from unwanted outcomes.

Deep Learning 120

Deep Learning Neural Network Data Scarcity Natural Language Processing 120

Unite.AI

MAY 3, 2024

Prolific was created by researchers for researchers, aiming to offer a superior method for obtaining high-quality human data and input for cutting-edge research. Today, over 35,000 researchers from academia and industry rely on Prolific AI to collect definitive human data and feedback.

Data Quality 130

Data Quality AI Research AI Researcher AI Development 130

ODSC - Open Data Science

SEPTEMBER 27, 2023

Understanding Data Lakes A data lake is a centralized repository that stores structured, semi-structured, and unstructured data in its raw format. Unlike traditional data warehouses or relational databases, data lakes accept data from a variety of sources, without the need for prior data transformation or schema definition.

Big Data 98

Big Data Metadata ETL Data Science 98

Pickl AI

AUGUST 30, 2024

Summary: This article provides a comprehensive overview of data migration, including its definition, importance, processes, common challenges, and popular tools. By understanding these aspects, organisations can effectively manage data transfers and enhance their data management strategies for improved operational efficiency.

Data Quality 52

Data Quality Data Integration Automation Machine Learning 52

Pickl AI

JULY 19, 2023

Supports data governance and data lineage tracking. Provides scheduling and automation features. Informatica Data Quality Pros: Robust data profiling and standardization capabilities. Comprehensive data cleansing and enrichment options. Scalable for handling enterprise-level data.

Explainability 40

Explainability Data Quality Data Integration Automation 40

Unite.AI

AUGUST 15, 2024

Tamr makes it easy to load new sources of data because its AI automatically maps new fields into a defined entity schema. This means that regardless of what a new data source calls a particular field (example: cust_name) it gets mapped to the right central definition of that entity (example: “customer name”).

Machine Learning 144

Machine Learning Computer Scientist LLM Large Language Models 144

Pickl AI

JULY 8, 2024

These pipelines automate collecting, transforming, and delivering data, crucial for informed decision-making and operational efficiency across industries. API Integration: Accessing data through Application Programming Interfaces (APIs) provided by external services. The Difference Between Data Observability And Data Quality.

Data Quality 52

Data Quality ETL Data Integration Automation 52

O'Reilly Media

NOVEMBER 28, 2023

Automating the process of building complex prompts has become common, with patterns like retrieval-augmented generation (RAG) and tools like LangChain. Few nonusers (2%) report that lack of data or data quality is an issue, and only 1.3% Developers are learning how to find quality data and build models that work.

Generative AI 137

Generative AI AI AI Data Analysis 137

AWS Machine Learning Blog

SEPTEMBER 14, 2023

The complexity of developing a bespoke classification machine learning model varies depending on a variety of aspects such as data quality, algorithm, scalability, and domain knowledge, to mention a few. We have made this process simple by automating the whole training pipeline.

Categorization 112

Categorization Machine Learning Data Scientist Natural Language Processing 112

Pickl AI

AUGUST 29, 2024

These approaches differ fundamentally in how they handle data acquisition, model training, and human interaction. In this blog, we will delve into the world of passive and active learning, exploring their definitions, key differences, advantages, and practical applications in Machine Learning.

Machine Learning 52

Machine Learning Natural Language Processing Computer Vision Data Quality 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. Relying on a credible Data Governance platform is paramount to seamlessly implementing Data Governance policies. The same applies to data.

Data Platform 52

Data Platform Data Integration Data Ingestion Automation 52

Pickl AI

MAY 30, 2024

Data Collection Methods There are several methods for collecting data. Surveys and questionnaires can capture primary data directly from users. Automated systems can extract data from websites or applications. APIs provide structured data from other systems. Why is Data Quality Crucial in Both Cycles?

Data Analysis 52

Data Analysis Data Science Data Scientist Data Quality 52

Pickl AI

JUNE 7, 2024

Top contenders like Apache Airflow and AWS Glue offer unique features, empowering businesses with efficient workflows, high data quality, and informed decision-making capabilities. Introduction In today’s business landscape, data integration is vital.

ETL 40

ETL Data Integration Data Quality Metadata 40

Pickl AI

AUGUST 27, 2024

Summary: This blog provides a comprehensive overview of data collection, covering its definition, importance, methods, and types of data. It also discusses tools and techniques for effective data collection, emphasising quality assurance and control.

Data Analysis 52

Data Analysis Data Integration Categorization Data Quality 52

Pickl AI

MAY 11, 2023

In this blog, we have covered Data Management and its examples along with its benefits. What is Data Management? Before delving deeper into the process of Data Management and its significance, let’s scratch the surface of the Data Management definition.

Data Integration 52

Data Integration Big Data Automation Data Quality 52

Topbots

SEPTEMBER 11, 2023

The model serves as a tool for the discussion, planning, and definition of AI products by cross-disciplinary AI and product teams, as well as for alignment with the business department. It aims to bring together the perspectives of product managers, UX designers, data scientists, engineers, and other team members.

UX Design 59

UX Design AI AI Automation 59

Pickl AI

OCTOBER 18, 2024

Complexity in Goal Definition: Requires domain knowledge for accurate goal setting. Data-Driven Insights: Utilises historical data for informed predictions, improving accuracy over time. Disadvantages Data Quality Dependency : Predictions are only as good as the data quality; poor data can lead to inaccurate forecasts.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Chatbots Robotics 52

Bugra Akyildiz

AUGUST 3, 2024

Data Quality and Processing: Meta significantly enhanced their data pipeline for Llama 3.1: Data Quality and Processing: Meta significantly enhanced their data pipeline for Llama 3.1: ceLLama is a streamlined automation pipeline for cell type annotations using large-language models (LLMs).

Neural Network 52

Neural Network Prompt Engineer Prompt Engineering Large Language Models 52

Snorkel AI

JULY 5, 2023

Organizations struggle in multiple aspects, especially in modern-day data engineering practices and getting ready for successful AI outcomes. One of them is that it is really hard to maintain high data quality with rigorous validation. The second is that it can be really hard to classify and catalog data assets for discovery.

Large Language Models 52

Large Language Models Metadata Machine Learning AI 52

Snorkel AI

JULY 5, 2023

Organizations struggle in multiple aspects, especially in modern-day data engineering practices and getting ready for successful AI outcomes. One of them is that it is really hard to maintain high data quality with rigorous validation. The second is that it can be really hard to classify and catalog data assets for discovery.

Large Language Models 52

Large Language Models Metadata Machine Learning AI 52

Snorkel AI

JULY 5, 2023

Organizations struggle in multiple aspects, especially in modern-day data engineering practices and getting ready for successful AI outcomes. One of them is that it is really hard to maintain high data quality with rigorous validation. The second is that it can be really hard to classify and catalog data assets for discovery.

Large Language Models 52

Large Language Models Metadata Machine Learning AI 52

Pickl AI

NOVEMBER 18, 2024

The article also addresses challenges like data quality and model complexity, highlighting the importance of ethical considerations in Machine Learning applications. Key steps involve problem definition, data preparation, and algorithm selection. Data quality significantly impacts model performance.

Machine Learning 52

Machine Learning Algorithm Data Quality Neural Network 52

The MLOps Blog

MARCH 15, 2023

Automation : Automating as many tasks to reduce human error and increase efficiency. Collaboration : Ensuring that all teams involved in the project, including data scientists, engineers, and operations teams, are working together effectively. This includes data quality, privacy, and compliance.

ETL 52

ETL Data Drift Machine Learning ML 52

Viso.ai

OCTOBER 10, 2024

When we integrate computer vision algorithms with geospatial intelligence, it helps automate large volumes of spatial data analysis. Computer Vision for Urban Analysis Computer vision (CV) techniques can be very helpful in analyzing visual data, such as satellite imagery, drone footage, or street-level photographs.

Computer Vision 52

Computer Vision Machine Learning Algorithm Convolutional Neural Networks 52

Pickl AI

SEPTEMBER 5, 2024

Using Power Query, you can automate cleaning and organising large datasets, making it easier to maintain data integrity. VBA allows you to write macros that automate tasks, including handling duplicates. VBA allows you to write macros that automate tasks, including handling duplicates. MIS Report in Excel?

Data Integration 52

Data Integration Data Analysis Automation Data Quality 52

The MLOps Blog

JULY 18, 2023

It’s an automated chief of staff that automates conversational tasks. We are aiming to automate that functionality so that every worker in an organization can have access to that help, just like a CEO or someone else in the company would. Jason: Hi Sabine, how’s it going? Jason, you are the co-founder and CTO of Xembly.

Conversational AI 52

Conversational AI Natural Language Processing Machine Learning AI 52

Snorkel AI

OCTOBER 12, 2023

Applying Weak Supervision and Foundation Models for Computer Vision In this session, Snorkel’s own ML Research Scientist Ravi Teja Mullapudi explores the latest advancements in computer vision that enable data-centric image classification model development. Wayfair does this by automating image tagging using a data-centric approach.

Data Scientist 64

Data Scientist ML Computer Vision AI 64

Snorkel AI

FEBRUARY 27, 2023

It’s a really historically exciting time—definitely in AI, but I venture across many different technology areas. But those elements used to be the blocker, and are often really not the blocker anymore because of all the amazing work that’s been done by the community—often now out in the open source.

Machine Learning 52

Machine Learning Large Language Models AI AI 52

Snorkel AI

FEBRUARY 27, 2023

It’s a really historically exciting time—definitely in AI, but I venture across many different technology areas. But those elements used to be the blocker, and are often really not the blocker anymore because of all the amazing work that’s been done by the community—often now out in the open source.

Machine Learning 52

Machine Learning Large Language Models AI AI 52

Snorkel AI

OCTOBER 12, 2023

Applying Weak Supervision and Foundation Models for Computer Vision In this session, Snorkel’s own ML Research Scientist Ravi Teja Mullapudi explores the latest advancements in computer vision that enable data-centric image classification model development. Wayfair does this by automating image tagging using a data-centric approach.

Data Scientist 59

Data Scientist ML Computer Vision AI 59