Modeling Related Topics 
Analytics Vidhya
JANUARY 15, 2025
In this article, we dive into the concepts of machine learning and artificial intelligence model explainability and interpretability. We explore why understanding how models make predictions is crucial, especially as these technologies are used in critical fields like healthcare, finance, and legal systems.
Analytics Vidhya
NOVEMBER 26, 2024
However, with the vast number of ML models available, choosing the right one for your specific use case can be challenging. Whether you’re working on a classification task, predicting trends, or building a recommendation […] The post How to Choose Best ML Model for your Usecase? appeared first on Analytics Vidhya.
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Analytics Vidhya
NOVEMBER 21, 2024
Imagine generating high-quality 3D models in under 10 seconds—no more long waits or cumbersome processes. 3D Modeling with AI-Driven Speed and Precision appeared first on Analytics Vidhya. Introducing Hunyuan3D-1.0, a game-changer in the world of 3D asset creation.
Analytics Vidhya
OCTOBER 14, 2024
Introduction While FastAPI is good for implementing RESTful APIs, it wasn’t specifically designed to handle the complex requirements of serving machine learning models. FastAPI’s support for asynchronous calls is primarily at the web level and doesn’t extend deeply into the model prediction layer.
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Let’s explore how you can apply scenario modeling in supply chain network design. To build your supply chain’s agility and responsiveness, you need to look at scenarios more frequently instead of relying on a single plan.
Analytics Vidhya
DECEMBER 17, 2024
The landscape of AI is evolving rapidly, and language models, particularly those designed for reasoning and problem-solving tasks, are at the heart of this revolution. One such breakthrough in AI is Phi-4, a 14-billion parameter model developed by Microsoft Research.
Analytics Vidhya
OCTOBER 18, 2024
As models grow larger and more complex, efficiently managing memory during model loading becomes increasingly important, especially when working with limited GPU or CPU resources. I recently came across a post by Sebastian that caught my attention, and I wanted to dive deeper into its content.
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In this article, we share best practices about modeling carbon costs in network design. Do you want to know the environmental impact of your supply chain and make sustainable decisions?
Speaker: Gertjan de Lange
If the last few years have illustrated one thing, it’s that modeling techniques, forecasting strategies, and data optimization are imperative for solving complex business problems and weathering uncertainty. Experience how efficient you can be when you fit your model with actionable data. Don't let uncertainty drive your business.
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This report explores how the state of supply chain network design has changed – including how the tools, maturity models, and market demands are transforming the network design practice. Advanced analytics & Scenario Modeling. This report is useful if you are interested in: Exploring new network design insights and capabilities.
Advertiser: Data Robot
As machine learning models are put into production and used to make critical business decisions, the primary challenge becomes operation and management of multiple models.
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Modeling your base case. Modeling carbon costs. Scenario analysis and optimization defined. Creating a strategic digital twin (digital representation) of your supply chain network. Optimizing your supply chain based on costs and service levels. Dealing with multiple capacity constraints. Network design for risk and resilience.
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The guide includes a checklist, an assessment, industry-specific use cases, and a data & analytics maturity model and roadmap. Download this guide for practical advice on using a semantic layer to improve data literacy and scale self-service analytics.
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Answer 10 relevant questions and find out if your needs qualify for advanced network design & scenario modeling technology. Dedicated supply chain network design software is fuelled by intuitive scenario analysis capabilities on the front end and powerful mathematical optimization on the back end.
Advertiser: Data Robot
How to choose the appropriate fairness and bias metrics to prioritize for your machine learning models. How to successfully navigate the bias versus accuracy trade-off for final model selection and much more. Download this guide to find out: How to build an end-to-end process of identifying, investigating, and mitigating bias in AI.
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