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Understanding business challenges for tailored algorithms.
Translating problems into mathematical models.
Developing robust, scalable algorithms.
Testing algorithms for effectiveness and efficiency.
Adapting algorithms for technologies and future applications.
Updating algorithms for ongoing relevance.
Incorporating algorithms into AI models.
Refining algorithms based on testing feedback.
Analyzing and preparing data for model training.
Choosing appropriate AI model architectures.
Implementing the first training cycle with prepared data.
Enhancing models through repeated training cycles.
Ensuring models can scale and evolve with changing business.
Integrating into business processes, ensuring optimization.
Rigorously testing models under different scenarios.
Adjusting model parameters for optimal performance.