Verta

Manasi Vartak  •  Mar 14, 2024

Can you apply AutoML to GenerativeAI Development?

Explore how AutoML techniques can revolutionize Generative AI development, tackling challenges in prompts, datasets, and evaluation. Discover the power of AutoML for accelerating GenAI projects.

Andy Butkovic  •  Mar 12, 2024

Adding GenAI to a Traditional ML Stack

Discover how to successfully integrate generative AI into your traditional ML stack. Learn key differences, challenges, and strategies for leveraging GenAI effectively. Subscribe for more insights.

Andy Butkovic  •  Mar 11, 2024

Intro and Tips For Companies new to AI

Learn how to successfully implement generative AI in your company with tips for newcomers and transitioning from traditional machine learning. Get ready to harness the power of AI!

Manasi Vartak  •  Feb 22, 2024

Model Distillation: A Method for High Performing yet Efficient LLMs

The usual process for distilling LLMs is quite complex. We’re changing that at Verta. We strongly believe distilling models can be good for companies, end users, and even the environment.

Baasit Sharief  •  Feb 7, 2024

January Model Roundup

The Verta GenAI Workbench is your one-stop shop to bring your Generative AI project from idea to launch, while ensuring that you have the highest-quality combination of model and prompts for your use case.

Conrado Silva Miranda  •  Feb 5, 2024

Rigorous LLM Evaluation With a Human Touch

As the ecosystem around evaluation for large language models (LLMs) matures, it’s important that we continue to keep humans in the loop. In this post, I’ll share how it’s possible to incorporate a human touch while still being rigorous. We’ll start with why this is important in the first place and then cover actionable strategies for building an LLM evaluation system.

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?

Andy Butkovic  •  Feb 1, 2024

LLM Prompting and the State of the GenAI Union (as told by Andy B.)

If your LLM-powered solution isn’t working, odds are you can’t fix it fast or cheap enough by training/retraining or fine-tuning the model. Messing with the prompts is the fastest, cheapest, and easiest way to see performance lift in your use case.

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.

Baasit Sharief  •  Dec 7, 2023

November Model Roundup

The Verta GenAI Workbench is your one-stop shop to bring your Generative AI project from idea to launch, while ensuring that you have the highest-quality combination of model and prompts for your use case.

Andy Butkovic  •  Nov 16, 2023

The GenAI Pivot for Product Managers

The Verta GenAI Workbench is your one-stop shop to bring your Generative AI project from idea to launch, while ensuring that you have the highest-quality combination of model and prompts for your use case.

Manasi Vartak  •  Nov 14, 2023

Announcing the Launch of Verta's Most Innovative Product Yet: the GenAI Workbench!

The Verta GenAI Workbench is your one-stop shop to bring your Generative AI project from idea to launch, while ensuring that you have the highest-quality combination of model and prompts for your use case.

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.

Manasi Vartak  •  Aug 29, 2023

5 Lessons From Building a #GenAI Product for Model Documentation

Five lessons we learned from building Generative AI product capabilities for automatic documentation creation -- our AI-Assisted Documentation (AIAD) feature.

Conrado Silva Miranda  •  Aug 16, 2023

Don't Overlook the Operational Challenges of Generative AI

AI challenges: learn why executives weighing generative AI initiatives need to be aware of the operational obstacles that make Gen AI difficult to deploy

Baasit Sharief  •  Aug 8, 2023

3 Documentation Horror Stories and How to Avoid Them

We have all heard model documentation horror stories. Here's my “hall of shame” stories also that highlight ways to avoid the pitfalls of poor documentation.

Andy Butkovic  •  Jul 24, 2023

Everyone Hates Model Documentation. Verta Is Changing That.

AI Assisted Model Documentation in Verta Model Catalog lets ML scientists easily create accurate and detailed model documentation in minutes, not hours.

Manasi Vartak  •  Jul 12, 2023

AI Regulations Are Here, and Companies Are Not Prepared

AI leaders (more than 300) answers in Verta's survey suggest that most companies are not prepared to meet the requirements in current & pending AI regulations.

Rory King  •  Jun 29, 2023

AI Regulations Are an Opportunity, So Don't Just Check the Compliance Box

Organizations can view upcoming regulations for AI as a cost to be endured, or as an opportunity to gain competitive advantage. We’ve seen this movie before.

Andy Reese  •  Jun 26, 2023

Enterprises Too Optimistic about Ability to Meet New Regulations

Companies prioritize AI compliance as they see AI regulations increasing, but few have automated processes that will be key to meeting new requirements

Andy Reese  •  Jun 20, 2023

As EU AI Act Advances, Data Science Teams Face Compliance Conundrum

Data science teams face the conundrum as the EU AI Act advances. The act’s provisions are at odds with current data science practices in key ways.

Conrado Silva Miranda  •  Jun 14, 2023

AWS US-East-1 Goes Down Again, and People Scream “Multi-Region!” - A Rant

AWS US-East-1 had issues Tuesday, bringing down a lot of the internet. The usual response: People scream, 'Multi-regions!' But that's hard to implement.

Andy Reese  •  Jun 8, 2023

Successful Organizations Prioritizing AI Regulatory Compliance

AI regulations priority becomes the key: executives at successful organizations are placing it higher than their counterparts at less successful companies.

Andy Butkovic  •  Jun 6, 2023

Enter the Tracking: Better Manage External Model Deployments

Track external deployments for all your ML models to better manage and govern your ML assets, improve collaboration and mitigate security risks. Here's how.

Rory King  •  Jun 2, 2023

3 Lessons to Avoid Becoming AI’s Regulatory Cautionary Tale

AI case: with AI regulations on the horizon, CEOs and AI leaders can apply past lessons from complying with environmental regulation to today's decision-making.

Andy Butkovic  •  May 31, 2023

Use Custom Attributes to Capture Model Metadata to Manage & Track Your Models

Tagging ML models with custom attributes in Model Catalog helps harness the potential of models while promoting efficiency, governance and collaboration.

Rory King  •  May 25, 2023

Blood Minerals’ Legacy and Apple’s Lessons for Executives Embracing Responsible AI Regulations

AI transpanency is the key: Apple’s example in addressing the blood minerals issue offers valuable lessons for organizations facing impending AI regulations.

Rory King  •  May 23, 2023

5 Easy Steps for Prompt Engineering with Large Language Models (LLMs)

Prompt engineering with large language models is an iterative process, so experiment and iterate to fine-tune the prompt. Here are 5 steps to simplicity.

Manasi Vartak  •  May 18, 2023

5 Signs You Need a Model Catalog

Enterprises are adopting model management techniques and model catalogs as part of their journey toward Operational Excellence in AI and machine learning.

Andy Butkovic  •  May 15, 2023

The Value of Maintaining Model Activity Logs

Activity logs an indispensable component of model lifecycle management due to Responsible AI and regulatory compliance. Learn why you should maintain them.

Andy Butkovic  •  May 11, 2023

Top 3 Benefits of Implementing a Machine Learning Model Catalog

Unlock the power of a machine learning model catalog: learn the top 3 benefits which implementing it brings to your organization’s data-driven journey.

Andy Butkovic  •  May 5, 2023

Model Release Management Made Easy with a Model Catalog

Model release becomes easy with a model catalog and release checklists letting organizations streamline the process and ensure models are put into production

Rory King  •  May 1, 2023

With EU AI Act, History Repeats, and Leaders Gain Advantage from MLM

EU AI Act gives the first-mover competitive advantage. Early adopters of Model Lifecycle Management today stand to see it, as when PLM systems were adopted.

Rory King  •  Apr 27, 2023

US Agencies Reaffirm: No AI Exception to Consumer Protection Laws

AI Tools need to be compliant with joint statement on enforcing laws against discrimination and bias in automated decision-making systems using ML & AI.

Baasit Sharief  •  Apr 21, 2023

The Five Principles of Responsible AI – and How to Apply Them

AI Accountability, AI Fairness, AI Transparency, AI Privacy, and AI Safety are the five principles of Responsible AI. Learn more about each in Verta's blog.

Rory King  •  Apr 13, 2023

Amidst Ongoing Tech Layoffs, AI/ML Talent Continues to Be in Demand

AI/ML Technology Investment Priorities research conducted by Verta finds that AI/ML talent continues to be in demand as companies expand use of machine learning

Baasit Sharief  •  Apr 6, 2023

Five Steps to Responsible AI

Responsible AI concepts have been gaining traction in technology for several years — here's five steps that companies can take to adopt Responsible AI.

Rory King  •  Apr 4, 2023

Key Drivers of AI/ML Investments Differ for Leading and Lagging Performers

Top factos driving ML & AI investment & spending decisions include changes in business strategy, cloud migration and modernization, and cost pressures.

Rory King  •  Mar 28, 2023

Majority of Organizations Increasing Infra Investments to Support AI/ML Initiatives

Learn how increasing ML infra investments make hybrid, multi-cloud approach the default technology strategy for many organizations in their AI/ML Initiatives.

Rory King  •  Mar 21, 2023

Companies Continue to Push AI/ML Investments

Investment in AI and ML technology and talent continues to grow and is proving resilient to economic headwinds. Read the blog to learn more about the trend

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.

Manasi Vartak  •  Nov 8, 2022

Key Enterprise Technology Trends Impacting ML Infrastructure Today

ML infrastructure has five themes in enterprise technology and machine learning - resiliency, risk, real time and other key trends. Read more for details.

Andy Butkovic  •  Oct 17, 2022

Model Catalog part 2: A Deeper Dive

Read for a deeper dive into the model version information contained in Model Catalog — deploy, version, integrate, and reproduce models quickly and safely.