1 - Rely on Experience 2 - Use Structured Frameworks 3 - Focus on Key Priorities 4 - Be Willing to Iterate 5 - Adjust as Needed

1. Identify critical aspects of the model that impact performance and focus on those to avoid over engineering. 2. Rely on established best practices and algorithms to minimize the need for trial and error. 3. Automate repetitive tasks like hyperparameter tuning or feature selection to speed up decision-making. 4. Trust your expertise and intuition, knowing when to trust a model’s output and when to refine further. 5. Embrace fast iterations by releasing early, testing rapidly, and continuously improving the model.

Robust model creation, rigorous testing, and a thorough comprehension of the underlying facts are all essential for making decisions without sacrificing quality. Firstly,place a high value on creating solid and trustworthy models. This calls for thorough data cleansing, feature engineering, and algorithm selection,make sure models are accurate and generalisable by thoroughly testing them on a variety of datasets and apply cross-validation. Secondly,make use of pipelines for automation and ML. These tools facilitate rapid iterations and decision-making by streamlining the model building and deployment process.Automating these duties allows to concentrate on more complex analysis and strategic choices.

use simplified models for real time decisions, parallel and distributed processing, stablish recommendation and monitoring feed back mechanism

1. Develop Proficiency: Acquire extensive knowledge in your domain to bolster confidence in your intuition. 2. Establish Priorities: Create a hierarchy of urgency and significance to evaluate jobs efficiently. 3. Utilize Decision Frameworks: Employ instruments such as the Eisenhower Matrix for immediate organizing. 4. Engage in High-Pressure Simulations: Replicate intense scenarios to enhance rapid decision making skills. 5. Acquire Knowledge from Experience: Contemplate previous decisions to enhance forthcoming choices. 6. Trust Your Intuition: Depend on your instincts while maintaining composure and concentration. Pursue Pragmatism: Endeavor to make educated, practical decisions, recognizing that perfection is not always attainable.

The other colleagues have shared excellent insights on balancing speed and quality. If I could add something, I’d say : - Focus on core features and optimize for them first. - Use pre-trained models or frameworks to save time while keeping things reliable. - Early stopping is a lifesaver. I mean why spend extra time training when the performance isn’t improving? - Lightweight models are underrated; they’re faster and often good enough for quick decisions. And finally, never underestimate the power of explainability. It helps make decisions faster when you (and others) can clearly understand the model’s outputs.

Balancing speed and quality requires structured methods: Rely on data-driven insights: Use historical data or metrics to guide decisions swiftly. Simplify choices: Narrow options using predefined criteria to streamline decisions. Delegate effectively: Assign tasks to skilled team members to share the load.

The key lies in establishing decision thresholds, utilizing lightweight models, incorporating explainability tools and iterative Improvement. Ultimately, it's about striking the right balance between speed and accuracy by aligning the model's purpose with business needs.

1) 𝑻𝒓𝒖𝒔𝒕 𝑬𝒙𝒑𝒆𝒓𝒊𝒆𝒏𝒄𝒆: Rely on past knowledge and instincts to guide quick and informed decisions. 2) 𝑺𝒆𝒕 𝑷𝒓𝒊𝒐𝒓𝒊𝒕𝒊𝒆𝒔: Focus on the most critical tasks and defer less urgent ones. 3) 𝑳𝒆𝒗𝒆𝒓𝒂𝒈𝒆 𝑭𝒓𝒂𝒎𝒆𝒘𝒐𝒓𝒌𝒔: Use tools like the Eisenhower Matrix to categorize tasks by urgency and importance. 4) 𝑴𝒂𝒊𝒏𝒕𝒂𝒊𝒏 𝑭𝒐𝒄𝒖𝒔: Concentrate on impactful areas to ensure quality under pressure.