2016 and Data Quality - Data Science Current

The 2016 Crystal Ball – What’s Next in Data?

Alation

FEBRUARY 20, 2020

Considering what we’ve seen this year in industry trends and patterns, we have compiled some predictions for 2016 from our co-founders at Alation. Venky Ganti, CTO & Co-Founder: Data sprawl will finally hit its threshold. Data sprawl has been prevalent for several years. 2016 will be the year of the “logical data warehouse.”

Data Warehouse

Data Warehouse Hadoop Data Science ETL

AI hallucinations: Are AI models like Chat GPT doomed to always hallucinate?

Data Science Dojo

SEPTEMBER 15, 2023

Inaccuracies span a spectrum, from odd and inconsequential instances—such as suggesting the Golden Gate Bridge’s relocation to Egypt in 2016—to more consequential and problematic scenarios. Generation method: Training and generation methods, even with consistent and reliable data, can contribute to hallucinations.

AI

AI AI Data Quality Algorithm

Data Fabric and Address Verification Interface

IBM Data Science in Practice

NOVEMBER 28, 2022

Data fabric is defined by IBM as “an architecture that facilitates the end-to-end integration of various data pipelines and cloud environments through the use of intelligent and automated systems.” The concept was first introduced back in 2016 but has gained more attention in the past few years as the amount of data has grown.

Data Pipeline

Data Pipeline Data Quality Data Preparation Data Governance

Webinars

Agent Tooling: Connecting AI to Your Tools, Systems & Data

Automation, Evolved: Your New Playbook for Smarter Knowledge Work

How to Modernize Manufacturing Without Losing Control

Mastering Apache Airflow® 3.0: What’s New (and What’s Next) for Data Orchestration

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The Hidden Cost of Poor Training Data in Machine Learning: Why Quality Matters

How to Learn Machine Learning

OCTOBER 10, 2024

This article explores real-world cases where poor-quality data led to model failures, and what we can learn from these experiences. By the end, you’ll see why investing in quality data is not just a good idea, but a necessity. Why Does Data Quality Matter? The outcome?

Machine Learning

Machine Learning Machine Learning Data Quality Algorithm

Efficient continual pre-training LLMs for financial domains

AWS Machine Learning Blog

MARCH 28, 2024

Preprocessing – You might consider a series of preprocessing steps to improve data quality and training efficiency. For example, certain data sources can contain a fair number of noisy tokens; deduplication is considered a useful step to improve data quality and reduce training cost. the SEC assigned identifier).

AWS

AWS Machine Learning Machine Learning Data Quality

10 Years Later: Who’s the GOAT of Data Catalogs?

Alation

DECEMBER 15, 2022

March 2015: Alation emerges from stealth mode to launch the first official data catalog to empower people in enterprises to easily find, understand, govern and use data for informed decision making that supports the business. April 2016: Tesco Group becomes first customer outside North America.

Data Governance

Data Governance Data Quality Data Warehouse Data Scientist

The Dual Utilization of Big Data In SEO And UX

Smart Data Collective

JULY 11, 2019

Big data is playing a vital role in both of these areas. In 2016, AJ Agrawal, the CEO of Alumnify, published an article detailing the ways that big data is affecting SEO. These definitely are the SEO and UX. Truth being told, in today’s digital marketing world SEO and UX cannot be treated separately. More nuanced analytics.

Big Data

Big Data Big Data Machine Learning Machine Learning

UK Defence Sector Data Rules: 6 Ways to Ensure Compliance

Alation

MARCH 28, 2023

Central to this is a uniform technology architecture, where individuals can access and interpret data for organisational benefit. Standardisation will also ensure easy reuse of data, by storing it consistently, with a single, authoritative source. Enduring data Data is an enduring asset and capability, not just a resource.

Data Silos

Data Silos Cloud Data Data Quality Data Governance

Mikiko Bazeley: What I Learned Building the ML Platform at Mailchimp

The MLOps Blog

JANUARY 26, 2024

I started my ML journey as an analyst back in 2016. Since then, I’ve worked as a data scientist for a multinational company and an MLOps engineer for an early-stage startup before moving to Mailchimp in May 2021. Technical projects must be aligned with business objectives. I joined just before its $12 billion acquisition by Intuit.

ML

ML ML Data Scientist Machine Learning

Extract non-PHI data from Amazon HealthLake, reduce complexity, and increase cost efficiency with Amazon Athena and Amazon SageMaker Canvas

AWS Machine Learning Blog

FEBRUARY 28, 2023

If you want to add rules to monitor your data pipeline’s quality over time, you can add a step for AWS Glue Data Quality. And if you want to add more bespoke integrations, Step Functions lets you scale out to handle as much data or as little data as you need in parallel and only pay for what you use.

ML

ML ML AWS Machine Learning

Find Your AI Solutions at the ODSC West AI Expo

ODSC - Open Data Science

OCTOBER 15, 2023

Outerbounds Founded in 2016, Outerbounds is a company that provides a platform for building and managing anomaly detection models. Their platform is designed to help businesses identify and respond to potential problems in their data.

Machine Learning

Machine Learning Machine Learning Data Pipeline AI

NLP in Legal Discovery: Unleashing Language Processing for Faster Case Analysis

Heartbeat

AUGUST 23, 2023

Data Quality and Bias NLP systems rely significantly on massive training data to understand patterns and generate accurate predictions. However, if training data is biased or of low quality, it might result in skewed results and exacerbate existing inequities. Artificial intelligence in law: The state of play 2016.

Natural Language Processing

Natural Language Processing Algorithm Artificial Intelligence Artificial Intelligence

Smart Factories: Artificial Intelligence and Automation for Reduced OPEX in Manufacturing

DataRobot Blog

MARCH 10, 2022

The “Fourth Industrial Revolution” was coined by Klaus Schwab of the World Economic Forum in 2016. DataRobot will automatically perform a data quality assessment, determine the problem domain to solve for whether that be binary classification, regression, etc., and recommend the best optimization metric to use.

Artificial Intelligence

Artificial Intelligence Artificial Intelligence Machine Learning Machine Learning

Evaluating Classification Models: Metrics, Techniques, and Best Practices

DagsHub

DECEMBER 16, 2024

Improve your data quality for better AI DagsHub helps you easily curate and annotate your vision, audio, and document data with a single platform. Image generated with Midjourney Improve your data quality for better AI DagsHub helps you easily curate and annotate your vision, audio, and document data with a single platform.

Machine Learning

Machine Learning Machine Learning Python Deep Learning

The Pros and Cons of Using the Top 5 Open-Source Named Entity Recognition Datasets

Defined.ai blog

APRIL 10, 2023

WNUT 2016 : A collection of social media posts annotated for named entities with a focus on difficult to recognize entities in informal text, such as named entities that are misspelled or that use non-standard forms. The dataset includes over 300,000 tokens of text and covers a wide range of named entity types.

Natural Language Processing

Natural Language Processing Algorithm Data Quality Machine Learning

The Pros and Cons of Using the Top 5 Open-Source Named Entity Recognition Datasets

Defined.ai blog

APRIL 10, 2023

WNUT 2016 : A collection of social media posts annotated for named entities with a focus on difficult to recognize entities in informal text, such as named entities that are misspelled or that use non-standard forms. The dataset includes over 300,000 tokens of text and covers a wide range of named entity types.

Natural Language Processing

Natural Language Processing Algorithm Data Quality Machine Learning

Data Science Current

The 2016 Crystal Ball – What’s Next in Data?

AI hallucinations: Are AI models like Chat GPT doomed to always hallucinate?

Webinars

Trending Sources

Data Fabric and Address Verification Interface

Webinars

The Hidden Cost of Poor Training Data in Machine Learning: Why Quality Matters

Efficient continual pre-training LLMs for financial domains

10 Years Later: Who’s the GOAT of Data Catalogs?

The Dual Utilization of Big Data In SEO And UX

UK Defence Sector Data Rules: 6 Ways to Ensure Compliance

Mikiko Bazeley: What I Learned Building the ML Platform at Mailchimp

Extract non-PHI data from Amazon HealthLake, reduce complexity, and increase cost efficiency with Amazon Athena and Amazon SageMaker Canvas

Find Your AI Solutions at the ODSC West AI Expo

NLP in Legal Discovery: Unleashing Language Processing for Faster Case Analysis

Smart Factories: Artificial Intelligence and Automation for Reduced OPEX in Manufacturing

Evaluating Classification Models: Metrics, Techniques, and Best Practices

The Pros and Cons of Using the Top 5 Open-Source Named Entity Recognition Datasets

The Pros and Cons of Using the Top 5 Open-Source Named Entity Recognition Datasets

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