Minh - From theory to reality: how the #AI lifecycle evolves with #GenAI? Many companies still view AI as a linear transformation: #Data → #AI → #Value But industry specialists know it’s far more complex. In a traditional #Machine #Learning, AI is an engineered pipeline: Structured data flows (cleaning, feature engineering, scaling) Iterative modeling and tuning Deployment, monitoring, and continuous retraining With Generative AI, the operating model shifts: It’s no longer just statistical models — we now rely on experimental workflows and composable stacks (#LLMs, #APIs, code, traditional ML), with a strong focus on: Human-in-the-loop Governance & guardrails (safety, compliance, alignment) Adaptive monitoring Continuous value loops, where feedback = improved outcomes This requires cross-disciplinary skills (AI Ops, prompt engineering, governance) and a deep understanding of the organizational decision-making process. To succeed in today's AI landscape, we must go beyond building models and deliver scalable value across complex ecosystems. | Facebook
(2) Facebook
What is Visual Data Storytelling and How to Tell a Story With Data
Visual data storytelling is the ability to communicate insights from a dataset using narratives & visualizations. It can make complicated data easier to understand & consume. Companies can use visual data storytelling to put data insights into context for their audience & initiate appropriate action. Steps to visual data storytelling includes knowing your audience, finding the correct analysis to solve problems, filtering insights & many more. #datastorytelling #datascience
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