Enterprise AI is the widespread use of AI in business to increase productivity, growth, and competitiveness
In contrast to small-scale or experimental AI systems, enterprise AI requires solid infrastructure, strategy alignment, and cross-functional interaction
Calculate goals and ROI when adopting solutions in a firm, not an academic or hobbyist project
AI adoption requires data scientists, engineers, domain specialists, and business executives
PyTorch and TensorFlow for deep learning. For structured data, XGBoost and LightGBM are best, whereas Scikit-learn is preferred for conventional machine learning
Explainable AI (XAI) frameworks, SHAP, LIME, and others to make your AI systems visible and intelligible to corporate leaders and engineers
Monitoring tools like Azure's Application Insights and Evidently AI can help developers spot these issues early
Enterprise AI implementation is difficult yet worthwhile. Companies can gain and innovate by using relevant technologies and strategies