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What We Do
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Feasibility AssessmentMinimize risk, maximize results, and set your organization up for success.
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AI LabLeverage shared experience and collaboration to drive adoption and results.
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Catalog of OpportunitiesEmbark on your AI adoption journey with confidence.
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Generative AI WorkshopUncover innovative ways to engage with your data to shape your future.
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Featured
Navigating Bias in AI with Open-Source Toolkits
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Some Industries We Support
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Energy and ResourcesDrive innovation and promote sustainability while gaining a competitive edge.
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Financial ServicesBoost efficiency, reduce costs, and streamline processes.
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Forestry and AgriculturePave the way for a productive, efficient, and greener future.
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HealthRevolutionize care delivery, improve outcomes, and save lives.
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ManufacturingStreamline production, eliminate costly downtime, and enhance quality control.
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Featured
AltaML Secures Spot on AIFinTech100 for Consecutive Year
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What We Do
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Contact
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Featured
Jurisage and CiteRight Finalize Merger, Become Jurisage Group Inc.
News
AltaML Named 2020 Responsible AI Award Winner by AI Global
AltaML is honored to be recognized by AI Global, now known as the Responsible Artificial Intelligence (AI) Institute, a community-driven non-profit dedicated to promoting responsible and ethical adoption of AI, as a recipient of one of its 2020 Responsible AI Awards. The Responsible AI Awards program acknowledges global technology innovators, leaders, and visionaries who are pioneering and adopting the responsible and ethical application of AI and supporting the mission and vision established by AI Global.
AltaML received the Start-up Leader Award for responsible AI design work in financial, health, and social services that balanced innovation with building responsible AI systems and is proud to showcase Alberta’s responsible AI leadership along with AI4Society at the University of Alberta which received an Academic Leader Award. The other 2020 Responsible AI Award winners are Anthem (Corporate Leader Award), Mark Caine at the World Economic Forum (Community Leader Award), and Martha Czernuszenko (Youth Leader Award).
“We extend our congratulations to this year’s winners and thank them for their leadership in putting digital trust and ethics at the center of their approach” said Ashley Casovan, Executive Director of AI Global. “We are pleased to recognize AltaML for their work on advancing our Responsible AI Design Assistant. With ambition matching their dedication to making AI safer and more trustworthy, AltaML is forging a path in the AI industry, demonstrating how it’s possible to balance building responsible AI systems with being innovative. Their insights and feedback were critical to advancing AI Gobal’s efforts this year.”
“At AI Global, we are providing a vision, framework, and tools to help businesses and governments ensure that AI is applied with a human and ethical focus and are proud of our pioneering work on the development of an independent Responsible AI certification in collaboration with World Economic Forum and Schwartz Reisman Institute” she added.
“AltaML is a purpose-driven organization and we believe that strong Responsible AI governance is essential to building solutions that elevate human potential,” said Nicole Janssen, Co-Founder and Co-CEO of AltaML. “Through our engagement with AI Global we had the opportunity to explore their Responsible AI Design Assistant as part of our investigation into best practices and tools for governance, and we are honoured to be recognized for our work toward a framework for ethical development of AI solutions. We are looking forward to great collaboration in 2021!”
About AltaML
AltaML is a leading developer of artificial intelligence (AI)-powered solutions. Working with organizations that want to use AI to leverage their data to develop solutions that drive tangible business results, AltaML empowers partners to create operational efficiency, reduce risks and generate new sources of revenue. Through a deep understanding of organizational pain points and challenges, AltaML’s solutions encompass the entire machine learning (ML) life cycle, from evaluating potential use cases and determining feasibility to piloting solutions, putting code into production, and ensuring model evolution.