Precisely

MARCH 7, 2024

Insurance industry leaders are just beginning to understand the value that generative AI can bring to the claims management process. By harnessing the power of machine learning and natural language processing, sophisticated systems can analyze and prioritize claims with unprecedented efficiency and timeliness.

AI 72

AI AI Data Governance Data Quality 72

The MLOps Blog

JUNE 27, 2023

Learn more The Best Tools, Libraries, Frameworks and Methodologies that ML Teams Actually Use – Things We Learned from 41 ML Startups [ROUNDUP] Key use cases and/or user journeys Identify the main business problems and the data scientist’s needs that you want to solve with ML, and choose a tool that can handle them effectively.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

IBM Journey to AI blog

DECEMBER 16, 2022

An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.

Data Quality 52

Data Quality Data Governance Data Scientist Data Wrangling 52

Pickl AI

DECEMBER 2, 2024

Processing frameworks like Hadoop enable efficient data analysis across clusters. Analytics tools help convert raw data into actionable insights for businesses. Strong data governance ensures accuracy, security, and compliance in data management. What is Big Data?

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

Pickl AI

NOVEMBER 25, 2024

Processing frameworks like Hadoop enable efficient data analysis across clusters. Analytics tools help convert raw data into actionable insights for businesses. Strong data governance ensures accuracy, security, and compliance in data management. What is Big Data?

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

Alation

AUGUST 26, 2021

It asks much larger questions, which flesh out an organization’s relationship with data: Why do we have data? Why keep data at all? Answering these questions can improve operational efficiencies and inform a number of data intelligence use cases, which include data governance, self-service analytics, and more.

Data Governance 64

Data Governance ML ML Augmented Analytics 64

Precisely

JANUARY 22, 2024

In CX, generative AI applications can include language translation and localization, consumer research and behavioral analysis to deepen understanding, and helping customer service reps research answers to complex queries. Gartner research has found that 85% of consumers will engage with a brand without ever interacting with a human.

Natural Language Processing 59

Natural Language Processing Machine Learning Machine Learning ML 59

Pickl AI

DECEMBER 4, 2023

Exploring technologies like Data visualization tools and predictive modeling becomes our compass in this intricate landscape. Data governance and security Like a fortress protecting its treasures, data governance, and security form the stronghold of practical Data Intelligence.

Data Analysis 52

Data Analysis Data Analysis Artificial Intelligence Artificial Intelligence 52

IBM Journey to AI blog

DECEMBER 20, 2023

Steps for building a successful AI strategy The following steps are commonly used to help craft an effective artificial intelligence strategy: Explore the technology Gain an understanding of various AI technologies, including generative AI , machine learning (ML), natural language processing, computer vision, etc.

AI 89

AI AI Artificial Intelligence Artificial Intelligence 89

Heartbeat

OCTOBER 23, 2023

Natural Language Processing (NLP) and Text Mining: Healthcare data includes vast amounts of unstructured information in clinical notes, research articles, and patient narratives. Data scientists and machine learning engineers employ NLP techniques and text-mining algorithms to process and analyze this textual data.

Machine Learning 52

Machine Learning Machine Learning Data Scientist Big Data Analytics 52

DagsHub

FEBRUARY 29, 2024

Data preparation involves multiple processes, such as setting up the overall data ecosystem, including a data lake and feature store, data acquisition and procurement as required, data annotation, data cleaning, data feature processing and data governance.

Data Preparation 52

Data Preparation Machine Learning Machine Learning Data Governance 52

Heartbeat

MAY 29, 2023

LLMs are one of the most exciting advancements in natural language processing (NLP). We will explore how to better understand the data that these models are trained on, and how to evaluate and optimize them for real-world use.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78

The Data Administration Newsletter

AUGUST 7, 2024

My column today is a follow-up to my article “The Challenge of Data Consistency,” published in the May 2023 issue of this newsletter. In that article, I discussed how semantic encoding (also called concept encoding) is the go-to solution for consistently representing master data entities such as customers and products.

Natural Language Processing 52

Natural Language Processing Data Quality Data Governance Data Modeling 52

phData

SEPTEMBER 4, 2024

ThoughtSpot is a cloud-based AI-powered analytics platform that uses natural language processing (NLP) or natural language query (NLQ) to quickly query results and generate visualizations without the user needing to know any SQL or table relations.

Analytics 52

Analytics Analytics SQL ETL 52

DagsHub

OCTOBER 23, 2024

Large Language Models We engineer LLMs like Gemini and GPT-4 to process and understand unstructured text data. They can generate human-like text, summarize documents, and answer questions, making them essential for natural language processing and text analytics tasks.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52