You've been there. You've done that. You listened to consultants. You listened to your partners. It still doesn't work. Talk to Parallaxis. We use our proprietary evaluation framework to identify your problems and get you back on track. Our Data Maturity Framework? provides new views and insights into your data labyrinth that unlock the true power of AI/ML. Working hand in hand, we fix your broken processes, systems, and structures to deliver sustainable solutions that provide you real business value.
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This is the fourth and final post in our Data Science Process. The Model Release and Assessment Phase releases and monitors our models for effectiveness. Monitoring includes tracking data drift, model performance metrics, and system health. Impact assessment ensures fairness through bias audits, transparency through explainability, and considers the social and economic effects of the model. How have your models performed vs. expectations?
Ethics in AI/ML is an ongoing effort aimed at ensuring fairness, privacy, and accountability throughout the process. Models must align with regulations like GDPR to prevent biased or harmful outcomes that could harm customers and your business. Key ethical practices include data minimization, differential privacy, fairness metrics, and robustness testing. Ethical AI prioritizes responsible, transparent, and equitable technology use.
This is the fourth and final post in our Data Science Process. The Model Release and Assessment Phase releases and monitors our models for effectiveness. Monitoring includes tracking data drift, model performance metrics, and system health. Impact assessment ensures fairness through bias audits, transparency through explainability, and considers the social and economic effects of the model. How have your models performed vs. expectations?
Parallaxis转发了
Award winning international leader of technology delivery organizations focused on the cost and pace of change of business value.
Here are our thoughts, what are your experiences in Model Development?
In this 3rd blog of our Data Science Process series, we explore the creation, training, and evaluation of Models. An iterative process of building, testing, and learning using the data & metrics we have previously identified. What are your experiences with Model development?
Dirk Wood and Aaron Welch talk about the Parallaixs Data Maturity Framework. Have a listen, let us know what you think, and then download our whitepaper. https://lnkd.in/g6fQ2RXn
The second post in our Data Science Process focuses on asking the right questions. Have a look. Tell me what you think.
Question Formation and Data Analysis is the first step of our data science process. Well-defined questions and thorough analysis are essential for building models that provide real business value. We explore to ensure that questions are specific and actionable and drive value, such as increasing revenue or reducing costs. Insights from data exploration are used to refine both the question and the data collection.
Question Formation and Data Analysis is the first step of our data science process. Well-defined questions and thorough analysis are essential for building models that provide real business value. We explore to ensure that questions are specific and actionable and drive value, such as increasing revenue or reducing costs. Insights from data exploration are used to refine both the question and the data collection.
A series of blogs that explore the process of creating and managing data models that deliver business value. It addresses the challenges senior leaders face in navigating AI/ML initiatives, emphasizing the need for clear success metrics and a flexible, repeatable process. The process is broken into three phases: - Question Formation & Data Analysis - Model Development - Model Release & Assessment It covers key concepts, including labeled data, feature engineering, and types of models. It also describes the importance of Explainable AI (XAI) for model transparency and compliance.