Analyze business requirements, data infrastructure, and goals to identify suitable applications and formulate a roadmap for implementation. Identify patterns, correlations, and trends that may not be apparent to others

Develop and implemet machine learning and deep learning models tailored to the specific needs of the organization, ensuring optimal performance and accuracy

Choosing the most appropriate algorithms and techniques for specific use cases, considering factors such as data type, complexity, scalability, and interpretability

Validate, how the model will perform on unseen data and assess the model's reliability and ensures it has not been overfitted to the training data

Apply Techniques like grid search, random search, or Bayesian optimization can be used to find the optimal set of hyperparameters

assist in deploying models into production environments, integrating them with existing systems or building a dedicated application or API, and ensuring scalability, reliability, and real-time performance

Establish monitoring processes, track model performance, and implement necessary updates and improvements