Remove Metadata Remove Modeling Remove Publishing
article thumbnail

Underlying Engineering Behind Alexa’s Contextual ASR

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Conventionally, an automatic speech recognition (ASR) system leverages a single statistical language model to rectify ambiguities, regardless of context. However, we can improve the system’s accuracy by leveraging contextual information.

Metadata 400
article thumbnail

Knowledge Graphs are Critical to Data Intelligence and AI

David Menninger's Analyst Perspectives

These catalogs combine technical and business metadata and data governance capabilities with knowledge graph functionality to deliver a holistic, business-level view of data production and consumption. Key industries include media, publishing, life sciences and pharmaceuticals.

Metadata 130
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Neptune.ai?—?A Metadata Store for MLOps

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. A centralized location for research and production teams to govern models and experiments by storing metadata throughout the ML model lifecycle. A Metadata Store for MLOps appeared first on Analytics Vidhya. Keeping track of […].

Metadata 143
article thumbnail

What is SCOR? A model to improve supply chain management

CIO Business Intelligence

Thats where the SCOR model comes in. What is the SCOR model? The SCOR model is designed to evaluate your supply chain for effectiveness and efficiency of sales and operational planning (S&OP). What is the main focus of the SCOR model? model to further address the growing need for digitization of supply chains.

Modeling 104
article thumbnail

Specialized tools for machine learning development and model governance are becoming essential

O'Reilly on Data

A few years ago, we started publishing articles (see “Related resources” at the end of this post) on the challenges facing data teams as they start taking on more machine learning (ML) projects. We are still in the early days for tools supporting teams developing machine learning models. Model governance.

article thumbnail

Proposals for model vulnerability and security

O'Reilly on Data

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks. This is like a denial-of-service (DOS) attack on your model itself.

Modeling 278
article thumbnail

The state of data quality in 2020

O'Reilly on Data

Just 20% of organizations publish data provenance and data lineage. These include the basics, such as metadata creation and management, data provenance, data lineage, and other essentials. They’re still struggling with the basics: tagging and labeling data, creating (and managing) metadata, managing unstructured data, etc.