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MLOps Meetup #16 // Venture Capital and Machine Learning Startups with John Spindler
Jun 6, 2020
57:29
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John Spindler, CEO of Capital Enterprise, shares insights on evaluating machine learning startups and trends in MLOps. He discusses missed opportunities, challenges in quantum computing, and the role of humans in ML. The impact of Amazon and investing in computer vision in agriculture are also explored.
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Quick takeaways
- Investing in machine learning startups requires careful evaluation and diversification to mitigate the high rate of failure.
- Timing is crucial for machine learning startups to bring their product to market, refine their models, and capture market share.
Deep dives
Investing in Machine Learning Startups
Investing in machine learning startups presents unique challenges, as there is a higher rate of failure compared to other investments. The success rate is around 6%, making it crucial to diversify investments in multiple startups to increase the chances of one being successful. Key factors to consider when evaluating machine learning startups include the team's expertise and background, the value the product creates for customers, the process used to build the models, the quality and source of the data, and the positioning of the startup in the market. It is essential to identify startups that tackle difficult technical problems and offer unique solutions that Amazon or other tech giants are less likely to enter. Additionally, some machine learning startups face the challenge of high costs, particularly when it comes to compute power. Overall, investing in machine learning startups requires careful evaluation and a recognition of the risks involved in the rapidly evolving field.
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