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From Bench to Bedside: Revolutionizing Healthcare with Modern Research Informatics
We've covered significant ground in accelerating our data innovation journey, leveraging analytics and AI at scale to transform the care ecosystem. This evolution can be symbolized by an infinity loop, where research on one side continuously fuels care delivery on the other, with each cycle enhancing care delivery and improving
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Machine Learning Lifecycle, Part 2: Selecting and Training Models
Machine Learning Lifecycle In this series on the machine learning lifecycle, we’re approaching the topic from end to start. In the last article, we explored the intricacies of model deployment—the final stage of the ML lifecycle. In this article, we’ll explore model selection and training.
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September: Best of the Web
Read below for a roundup of interesting sites, resources, and articles from around the web, curated and contextualized by unbiased analytics experts at IIA. Highlights include an article from HBR arguing for consolidation of tech-related C-suite roles, an article on resolving common AI pain points, and a webinar on fueling
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Tackling Business Intelligence Maturity: The Key to Advanced Analytics
As data and analytics leaders are making the strategic turn toward data-driven decision-making and advanced analytics for the enterprise, progress is slow for many because of BI maturity challenges—from data quality and accuracy to analytical integration. Join Nathan Hombroek, IIA expert and VP of Innovation at Axis Group, as he
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Machine Learning Lifecycle, Part 3: Data Collection
Machine Learning Lifecycle In our third installment of the “Machine Learning Lifecycle” series, we’ll dive into data collection. See the sidebar for Part 1 (deployment) and Part 2 (selecting and training models) to catch up to the discussion.
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Machine Learning Lifecycle, Part 4: Scoping
Machine Learning Lifecycle We have reached the fourth and final article covering the machine learning project lifecycle. In this series, I cover all four steps presented by Andrew Ng on DeepLearning.AI’s course called Machine Learning Engineering for Production (MLOps). Please see the sidebar for Parts 1-3, exploring deployment, modeling, and
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From Problem to Production: Good Data Science Products in Six Steps
A few years ago, I built a machine learning application for a company. It had predictions, explanations of the predictions, a dashboard that combined many data sources, and much more. Then the tool went live. And…it was hardly used. What went wrong? I had no idea. I had weekly contact
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Beyond the Hype: When Generative AI Isn’t Always the Answer
I work at Google in the Solution & Thought Leadership team (aka S&TL). Our role is to help large companies adopt AI technologies to boost their performance and innovate. In the past three years, I have been collaborating with different AI teams and decision makers across North America to build
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The Role of Data Governance as Tactical Decision Layer
I have been reading a lot of different literature on this topic, and something has made me wonder: The terms data strategy, data governance, and data management are not used consistently, even sometimes used interchangeably. Understanding the distinct roles and relationships between these is I think crucial for organizations aiming
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Analytics Maturity Assessment Insights
Scoring over 100 competencies and qualitative dimensions, IIA’s analytics maturity assessment portfolio is the broadest and deepest on the market. Join this session as Jack Phillips, CEO at IIA, reveals five key findings based on the synthesis of nearly two years of our enterprise-wide maturity assessment data, including why the