Many enterprises are hindered in making full use of AI. Usually, it’s because stakeholders don’t have visibility into the processes and methods used by the AI solution. With industry-leading expertise, our team offers the tools, assets and partnership you need to expedite implementation. Working across all stages of the AI lifecycle, we help deliver trusted AI solutions at scale and speed.
Does the model treat disparate groups of people equally?
How well is the model performing in the real world compared to training time?
Can the model’s outcomes be interpreted by end-users and key stakeholders?
Under what conditions is a model likely to yield more uncertain outcomes?
Are key model development decisions documented and approved using a well-defined process?
Does the model shield sensitive data?
Can the model be protected from adversarial attacks?
See what clients are doing to improve AI confidence, expedite deployment and meet regulation compliance and requirements.
The banking giant deploys AI quality control to reduce risk and improve insights, operationalizing AI in a way that’s repeatable, sustainable and trusted.
A healthcare startup uses predictive AI to protect the most vulnerable newborns, reducing the time required to identify at-risk infants by up to several hours while protecting patient data privacy.
An AI-powered recommendation engine uses data to help financial coaches share inclusive and equitable fintech products, building financial security for low-income communities.
In a world where trust, transparency and explainable AI matters, every organization needs the comfort and compliance of understanding how analytic insights and decisions are being made.
Document, govern and monitor machine learning models on a multi-cloud Data and AI platform supported by RedHat OpenShift, and apply lifecycle governance, risk management, and regulatory compliance to your business.
Well-governed AI requires proactive planning to align people, tasks and technologies. Automated tools and processes help to produce more consistent, compliant and effective AI solutions at scale.
Solutions
Provide training and enablement
Benefit from IBM’s expertise on trust in AI, including best practices and industry-driven recommendations. Provide training and enablement on all aspects of the AI lifecycle. “Learn through doing” with side-by-side work in planning, building, deploying, and operating trusted AI solutions.
Create an AI action plan
In planning AI solutions, it is critical to translate business needs into specific, actionable requirements to ensure trust in the solution itself, as well as its monitoring and maintenance. Solution planning for AI uses a structured method to establish AI business needs and translate them into precise technical specifications.
Create trusted AI solutions with an agile approach
At the core of any business’s use of AI is a specific AI solution that must be trusted, usually a machine learning model. An experienced team of data scientists and AI practitioners produces an initial solution with the characteristics needed for trust in just six weeks using agile methodologies.
Efficient and reliable AI deployment
Even the best AI model brings no value to the business until it can be confidently deployed and used. The key to promoting models from development to test in production is validation — not only of the accuracy, but also of trustworthy characteristics and configuration management, which must be maintained in order to trust what is promoted. MLOps Validate and Deploy establishes pipelines for the full process, regardless of what tools were used to build models.
Operating with trust and transparency
Even with the best processes for planning and building a trusted solution, we need special monitoring and processes for machine learning models to be able to use them confidently. MLOps monitor and manage uses IBM Cloud Pak® for Data and OpenScale™ to establish operational monitoring for key elements of trusted AI.
Responsible, transparent and explainable AI workflows
IBM AI governance provides automated tools and processes enabling an organization to direct, manage and monitor across the AI lifecycle. Operationalizing AI helps to drive transparent AI workflows and explainable results designed to mitigate risk and ethical concerns, all while complying with AI regulations and preserving the reputation of the organization.