As companies are increasingly adopting machine learning (ML) for their mainstream enterprise applications, more of their business decisions are influenced by ML models. As a result of this, having simplified access control and enhanced transparency across all your ML models makes it easier to validate that your models are performing well and take action when they are not. In this post, we explore how companies can improve visibility into their models with centralized dashboards and detailed documentation of their models using two new features: SageMaker Model Cards and the SageMaker Model Dashboard. Both these features are available at no additional charge to SageMaker customers. Model governance is a framework that gives systematic visibility into model development, validation, and usage.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Artificial intelligence (AI) governance software provider Monitaur launched for general availability GovernML, the latest addition to its ML Assurance Platform, designed for enterprises committed to the responsible use of AI. GovernML, offered as a web-based, software-as-a-service (SaaS) application, enables enterprises to establish and maintain a system of record of model governance policies, ethical practices and model risk across their entire AI portfolio, CEO and founder Anthony Habayeb told VentureBeat. As AI deployment accelerates across industries, so have efforts to establish regulations and internal standards that ensure fair, safe, transparent and responsible use of this often-personal data, Habayeb said. "Good AI needs great governance," Habayeb said.

Monitaur, an AI governance software company, announced the general availability of GovernML, the latest addition to its ML Assurance platform, designed for enterprises committed to responsible AI. Offered as a web-based, SaaS application, GovernML enables enterprises to establish and maintain a system of record of model governance policies, ethical practices, and model risk across their entire AI portfolio. As deployments of AI accelerate across industries, so too have efforts to establish regulations and internal standards that ensure fair, safe, transparent and responsible use. "Good AI needs great governance," said Monitaur founding CEO Anthony Habayeb. "Many companies have no idea where to start with governing their AI. Others have a strong foundation of policies and enterprise risk management but no real enabled operations around them. They lack a central home for their policies, evidence of good practice, and collaboration across functions. We built GovernML to solve for both."

It's critical to keep a finger on the pulse of AI developments. Here are three trends that those involved in creating successful AI and ML models need to heed. Artificial intelligence (AI) and machine learning (ML) models hold the potential to identify customer trends and patterns, to quickly adjust at scale to improve business insights and processes, and to generate new revenue streams. However, the promise of AI and ML models to make things easier by computerizing human cognition has seen its challenges and will surely see more as the industry matures. It's critical to keep a finger on the pulse of AI developments because it helps to learn from others' mistakes as well as their victories.

With data becoming a core business asset for financial companies, artificial intelligence and machine learning (AI/ML) continues to be a focal point in maximising the competitive advantage of this'new oil'. This is according to a new study by LSEG Labs, The Defining Moment for Data Scientists, on the applications of AI/ML in financial services. The study found that the adoption of AI/ML within organisations has remained steady since 2018. Over the last three years, between 40-50% of respondents reported deploying AI/ML in multiple areas. The survey is based on responses from 482 data scientists, quants, model governance professionals and C-suite executives, from both sell-side and buy-side financial institutions.

With data becoming a core business asset for financial companies, artificial intelligence and machine learning (AI/ML) continues to be a focal point in maximising the competitive advantage of this'new oil'. This is according to a new study by LSEG Labs, The Defining Moment for Data Scientists, on the applications of AI/ML in financial services. The study found that the adoption of AI/ML within organisations has remained steady since 2018. Over the last three years, between 40-50% of respondents reported deploying AI/ML in multiple areas. The survey is based on responses from 482 data scientists, quants, model governance professionals and C-suite executives, from both sell-side and buy-side financial institutions.

ML software development is complex; building an ML model is one thing, improving and maintaining it, is another. If you want your machine learning models to be robust, compliant, and give reproducible results, you must invest time and money in quality model management. Model governance, model provenance, and model lineage tools help you in doing just that by tracking model activity, recording all changes in the data and the model, and outlining best practices for data management and disposal. In this post, let us discuss what these tools are and how to choose the best ones. While these three practices are meant for different things, they have a lot in common. So a tool that is good for, say model governance, is usually great for the other two as well. I will guide you through some of the most popular tools for model governance among developers and explain which one you should choose based on your particular use case.

I'd like to take a step back and give a high-level view of what we're observing with our clients before answering the question, "What is AI model governance?" Financial institutions have data scientists who create models meant to improve specific functions of the business, such as the fraud prevention department. A data scientist working there's focused on fraud detection with the goal of reducing fraud and its impact on the company's bottom line. This can only happen if the data scientist's model uncovers fraudulent transactions and the company can then take the appropriate action. Unfortunately, after models leave the lab environment, there's very little visibility on what's happening to them.

For some industries, the use of AI and machine learning models is novel, but several industries--consumer finance and insurance in particular--have been building, using and governing models for decades. These industries have well-developed governance practices built largely around algorithmic, rule-based and other model technologies and regulations that predate AI models. Many of the enterprises I talk to are revisiting their model operationalization and governance processes and strengthening them with new capabilities to accommodate the increased use of AI/ML technologies. You can't govern what you can't see, so every model risk management (MRM) program must start with a centralized model inventory that includes all the metadata associated with every model throughout its life cycle, from development to deployment, modification and retirement. This model metadata, which documents the model's complete history and lineage, captures a broad range of elements including the specific software and libraries used in its development, the data used to train the model, the people involved in the model's development and maintenance and what they created or changed, the model's intended business use and KPIs, an explanation of the key influencing factors behind the model's decision-making, etc.

Today, predictive models help many companies run their business-critical processes. These predictive models are probabilistic -- they involve chance variation -- and they depend on some underlying assumptions to make them work properly. Chief among these are 1.) that the data flowing into them is distributed in a certain way and 2.) that underlying business scenarios are unchanged from the time the models were created. As a result, it's important for companies to monitor changes to business scenarios and data quality, otherwise the model will no longer perform as intended. Additionally, it's important that these models comply with applicable laws and regulations once they are in production (active use).