The computer vision and machine learning market, currently valued at $12 billion and expected to grow to over $20 billion by 2030, presents ample opportunities for startups. However, launching a successful venture in this high-growth sector requires more than just a promising idea; it demands a working proof of concept (POC) machine learning model that can transform annotated datasets into commercially viable solutions. Startups are often founded by individuals with backgrounds in software engineering, data science, and related fields, who must navigate challenges like sourcing unique, high-quality training data and choosing whether to build or leverage open-source computer vision models. Essential steps include validating the commercial potential of the startup idea by engaging with potential customers, annotating datasets, training models for accuracy, and testing them against industry benchmarks. Once the model demonstrates effectiveness and commercial relevance, it becomes a compelling proposition for investors and clients, highlighting the importance of integrating business objectives into the development process.