Verta | Blog | Meeta Dash

Meeta Dash • Mar 26, 2024

RAG Evaluation: An Introduction

Explore the key steps and metrics for effective evaluation of Retrieval-Augmented Generation (RAG) systems in AI applications. Enhance accuracy and relevance with expert review and automated methods. Subscribe for more insights.

Meeta Dash • Feb 5, 2024

HELM: A Better Benchmark for Large Language Models

From answering questions to writing code, large language models (LLMs) can perform seemingly any task, making them hard to benchmark. It’s also not obvious what “better” even means. Most AI research has emphasized accuracy, but what about fairness, efficiency, and other qualities?

Meeta Dash • Jan 24, 2024

Why a Top 5 Global Insurer Chose Verta for MLOps

A top 5 global insurer—a household name with over 40B in annual revenue—chose Verta as their comprehensive solution for MLOps.

Meeta Dash • Nov 14, 2023

Introducing the Verta GenAI Workbench: Go from GenAI Idea to Product on One Platform

The Verta GenAI Workbench is an all-in-one platform. It’s the only solution that includes model and prompt experimentation, robust automated and human-in-the-loop evaluation, and continuous iteration based on feedback.

Meeta Dash • Oct 5, 2023

Custom Attributes Now Include Categories, New Types, and Audit Trails

Custom attributes is a unique feature of Verta’s model catalog to define metadata for capture about models & versions, including Audit Trails, Categories & more.

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Meeta Dash • Nov 15, 2022

How Verta Supports AI Trust, Risk and Security Management

AI TRiSM (AI trust, risk and security management) practices are consistently supported by he Verta Operational AI platform & Enterprise Model Management system.

Meeta Dash • Apr 22, 2022

What’s New in Verta Experiment Manager

Verta Experiment Manager enables data scientists organize modeling experiments, visualize experimental data, metrics, hyperparameters, model quality, and data samples.

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Meeta Dash • Apr 14, 2022

Extending ML Observability with Verta & Datadog Integration

Verta & Datalog integration allows you to send endpoint metrics from Verta to Datadog where you can monitor ML endpoint states, utilization, and resources.

Meeta Dash • Apr 7, 2022

Verta and PyPI Integration

PyPI repository integration is now available on the Verta platform. Safely scale the use of your favorite trusted Python libraries to your data science team.

Meeta Dash • Feb 10, 2022

What Is Model Monitoring and Why Is It Important?

Machine Learning Model usually lacks visibility, and monitoring ML models across the AI/ML lifecycle is quite tricky. Find out why, along with best practices.

Meeta Dash • Dec 9, 2021

Agile for Intelligent Products

Learn why intelligent products need adopting agile practices for AI initiatives, how to adapt and work with them on your machine learning and AI projects.

Meeta Dash • Nov 24, 2021

Interactive Charts to Debug Model Performance

Model performance improves with Verta's interactive charts: build custom charts and dashboards to analyze, compare, and share the results of your experiment.

Meeta Dash • Nov 5, 2021

Introducing Verta Model Registry

Verta Model Registry is a central repository to manage and deploy production-ready models to ensure a reliable and automated release process. Learn more here!

Meeta Dash • Oct 19, 2021

Increase Security & Collaboration with Verta User Management

We’re excited to announce the release of enhanced user management capabilities in Verta for improved security and collaboration. Read the blog for the details.

Meeta Dash • May 27, 2021

Verta Model Monitoring is now in Community Preview!

ML models are failing silently without a good way to detect issues in production. We identified three key Model Monitoring challenges and how to address them.

Meeta Dash • Feb 18, 2021

Top 3 reasons you need a Model Registry

The ML model registry allows data scientists to publish their production-ready models and share them for collaboration with other teams and stakeholders.

Meeta Dash

April Model Roundup

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