Trying out new tools/frameworks is part of R&D and should be part of the deadline estimation from the very beginning You can’t simply switch your frameworks and tools in the middle of development. And if you do, you’ve to do it in a phase manner

To ensure project success while managing time constraints and new machine learning tools: Prioritize Impactful Tasks ?? – Focus on high-value tasks that fit the timeline. Use Proven Models First ?? – Start with reliable models, and experiment later. Leverage Automation ? – Automate repetitive tasks to save time. Stay Aligned with Stakeholders ?? – Regular check-ins to avoid misalignment. Start Simple, Optimize Later ??? – Simplicity first, refine as needed. Ensure Early Feedback ?? – Regular quality checks prevent major issues. This ensures efficiency while maintaining quality.

Staying updated with new tools and tech stacks is essential, but transitioning existing infrastructure mid-project can be challenging. The key is to first understand the requirement thoroughly, break the project into phases followed by a regular feedback and review at each stage. Lastly and very important - R&D is crucial but should come with a hard stop to avoid delays, ensuring the project stays on track and leads to project success.

To succeed in machine learning projects under time pressure, it's essential to prioritize ruthlessly. Start by creating a minimal viable model that solves 80% of your problem, validate it quickly, and only then consider exploring new tools that can demonstrably improve performance. The crucial thing is to focus on the business impact rather than chasing after the latest shiny tools.

To ensure project success while balancing time constraints and new machine learning tools, prioritize understanding the problem first. Break down the task into manageable chunks, and start with proven models to build a foundation. Experimentation is key, but avoid over-complicating—focus on simplicity first, optimizing later. Automation tools, like data preprocessing pipelines, can save time, and frequent check-ins with stakeholders ensure you stay aligned with project goals. Lastly, remember that even with limited time, quality checks are non-negotiable—early feedback prevents larger issues down the road.