What do you do if you're an entrepreneur seeking funding for your machine learning startup?

Having a SOTA proprietary Machine Learning IP is like having the first corner piece to a large business puzzle. Having lots of early users with a good retention rate is a great way to assemble the rest of the corner pieces together. Find and place down those pieces first as part of your roadmap.

Four considerations come to mind for creating your USP (especially in deep tech): 1. Do you have a team with genuine capabilities in the domain? 2. What sets your technology apart from others? It doesn't necessarily have to be patented; it could be a trade secret. You need a secret sauce, whether it's proprietary algorithms or data. 3. Does your technology work? Provide case studies to demonstrate its effectiveness. Better if it is in a commercial setup. 4. Once you've established both of these, assess your value. This of course varies depending on the industry. For instance, in drug discovery (my domain), possessing both 1 and 2 makes you highly valuable.

Articulate clearly what sets your machine learning startup apart. This includes your unique value proposition, the problem you're solving, and how your solution is innovative or superior to existing solutions. Understanding and communicating your startup's value is fundamental to attracting investors.

Attend networking events, conferences, and industry meetups to connect with potential investors, mentors, and advisors. Cultivate relationships with individuals who can provide valuable insights, introductions, and support for your fundraising efforts

Start networking early. Attend industry events, join relevant online communities, and reach out to potential investors and mentors through platforms like LinkedIn. Building relationships over time can lead to more opportunities for funding and partnerships.

As an entrepreneur seeking funding for your machine learning startup, perfecting your pitch is essential. Craft a compelling narrative that highlights the problem your startup addresses, the innovative solution it offers, and the market opportunity it represents. Clearly articulate your value proposition, demonstrating how your machine learning technology sets you apart from competitors. Tailor your pitch to resonate with investors, emphasizing the scalability, potential ROI, and impact of your startup. Practice delivering your pitch with confidence and clarity, incorporating compelling visuals and data to support your claims.

Your pitch should be concise, compelling, and tailored to your audience. It should cover what your company does, the market opportunity, your business model, and your team. Practice your pitch to ensure it's polished and persuasive.

Remember that investors don't really care about how cool your machine learning model or algorithms are. They care about your ability to create value by solving a problem that matters to customers willing to pay for it. Or that will attract free users whom you can monetize by ads. So your top priority is establishing and communicating product-market fit, or at least the potential for it. Then you have to make the case others cannot -- or will not -- easily or quickly replicate what you have built. Basically, investors -- or at least sensible investors -- want to know why anyone will care about you have built, and why others won't quickly out-compete or commodify your solution.

Research potential investors who have a history of investing in machine learning startups or show interest in your industry. Knowing their investment thesis, past investments, and what they look for in startups can help you tailor your approach.

Venture capital isn't the only route. Consider angel investors, crowdfunding, grants (especially for tech and innovation), and accelerators that specialize in tech startups. Each funding source has its pros and cons, and some may be more suitable for your startup's stage and goals.

Venture capital isn't the only route. Consider angel investors, crowdfunding, grants (especially for tech and innovation), and accelerators that specialize in tech startups. Each funding source has its pros and cons, and some may be more suitable for your startup's stage and goals.

As an entrepreneur with a machine learning startup, securing funding is a critical step on your journey. Start by crafting a compelling business plan that clearly articulates your unique value proposition, the problem you're solving, and your strategy for growth. Research potential investors who have an interest in machine learning and prepare a tailored pitch for them. Networking is key, so attend relevant industry events and engage with the community online. Be prepared for due diligence, as investors will want to understand your business model, team, and technology in depth. Remember, persistence is key, and every 'no' is one step closer to a 'yes'. Good luck on your funding journey!

Proving that your technology works as you say and can deliver as per the market needs is super important, especially in a time when everybody is claiming everything. So, does your ML technology really work? The best way to show this is by actually using it. You can build case studies in-house or try them out in real situations through pilot projects with your potential customers/partners. Testimonials might not be as convincing, especially in deep tech. May work, if you're in the consumer domain.

More effective and long-lasting company practices can be achieved by shifting the focus from self-interest to customer solutions. Get to know your customers' wants, problems, and aspirations inside and out. Don't waste time trying to achieve internal objectives if external parties are interested in these solutions as well. Get your consumers to tell you what they think and listen to their suggestions and complaints. Rather than getting caught up in passing technological fads (like the GenAI hype of today), focus on addressing immediate, practical issues that your customers are dealing with. This will show how useful your solutions are in real-world situations, which will lead to more people using them.