If you are a data scientist, business analyst or a machine learning engineer, you need model management – a system that manages and orchestrates the entire lifecycle of your learning model. Analytical models must be trained, compared and monitored before deploying into production, requiring many steps to take place in order to operationalize a model's lifecycle. In this blog, I will describe how SQL Server can enable you to automate, simplify and accelerate machine learning model management at scale – from build, train, test and deploy all the way to monitor, retrain and redeploy or retire. SQL Server treats models just like data – storing them as serialized varbinary objects. As a result, it is pretty agnostic to the analytics engines that were used to build models, thus making it a pretty good model management tool for not only R models (because R is now built-in into SQL Server 2016) but for other runtimes as well.
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.
There is a revolution in AI coming and it's going to render legacy data and model governance practices obsolete. What this all adds up to is an explosion in the volume of predictive models and of the data in motion in your organization. Where there were no models, there will suddenly be many. Where there was one model, you may find there are now hundreds. And the pipes providing data into and delivering results out of these models are going to proliferate.
Artificial intelligence (AI) is poised to redefine how businesses work. Already it is unleashing the power of data across a range of crucial functions, such as customer service, marketing, training, pricing, security, and operations. To remain competitive, firms in nearly every industry will need to adopt AI and the agile development approaches that enable building it efficiently to keep pace with existing peers and digitally native market entrants. But they must do so while managing the new and varied risks posed by AI and its rapid development. The reports of AI models gone awry due to the COVID-19 crisis have only served as a reminder that using AI can create significant risks.
Vulcan Cyber, the vulnerability remediation company, announced the release of a new eBook titled, "The Vulnerability Remediation Maturity Model." The eBook provides security and IT operations teams with a blueprint for transforming inefficient vulnerability management programs into agile, effective vulnerability remediation programs that scale to the needs of the business. The eBook is available for download here. "Most vulnerability management programs are paper tigers -- they generate a mountain of data and work but have negligible benefit to enterprise security," said Yaniv Bar-Dayan, co-founder and CEO of Vulcan Cyber. "We created the vulnerability remediation maturity model after consulting with hundreds of CISOs, security and IT professionals to understand shortcomings in vulnerability management programs. The model helps companies design outcome-driven vulnerability remediation programs through a unique approach to people, process and tool alignment. We've seen it help companies like Snowflake, Comcast and Informatica get fix done."