Creating A Customized Study Plan

In this segment, we’re guided through how to create an effective, targeted prep plan for ML interviews. Preparation should be intentional, role-specific, and company-informed.

• Create a tailored study plan based on your target role. Spend 1–2 weeks on behavioral prep, overlap topics wisely, and prioritize deliberate, focused practice.
• Do company-specific preparation by researching their tech stack, ML frameworks, and use cases. Read blogs, papers, and talk to current employees to understand their challenges and how they approach ML.
• For product-focused ML roles, focus on business impact, case studies, model selection trade-offs, and deep feature engineering—interviewers value end-to-end thinkers.
• For infrastructure-focused ML roles, focus on scalable systems, distributed training, MLOps best practices, feature stores, and real-world reliability scenarios like traffic spikes.
• For research-oriented ML roles, go deep into recent papers, experimental design, evaluation strategies, and be ready to explain your own contributions with clarity—even to non-experts.

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