What do you do if your industry competitors are gaining an edge with machine learning?

?? Assessing your position is paramount. ?? Explore competitors' ML ventures, deciphering value creation and efficiency boosts. Illuminate market gaps ripe for ML's touch. Gauge your data infrastructure, analytics prowess, and ML finesse. ?? Conduct a SWOT odyssey to chart ML's course amidst strengths, weaknesses, opportunities, and threats.

If your competitors are using machine learning to get ahead, it's time to roll up your sleeves. Learn about what they're doing, invest in your team's skills, and figure out how machine learning can give you an edge too. Look for unique ways to apply it to your business, and keep evolving to stay ahead of the game.

1?? Assess your current situation by analyzing competitors and customers' use of machine learning. 2?? Define your ML roadmap with clear objectives, KPIs, and implementation plans. 3?? Build or buy your ML team considering budget, resources, and risks. 4?? Learn and improve ML capabilities by fostering a culture of innovation and staying updated. 5?? Implement and evaluate ML solutions in business processes, monitoring performance and collecting feedback. 6?? Considerations emphasize the importance of continuous learning, experimentation, and tracking industry trends for success in ML implementation.

It's never too late to learn something new. Irrespective of your competitors you are supposed to keep upskilling. - Examine yourself - If you are getting started brush up your basics -Make note of all the ML skills you need to stand out. - Keep a track of your progress and keep implementing new techniques. - Ask your peers for help - Fill the gaps -

Training your existing own team and creating/adding new talent to a team are the only two ways to boost your ML team's performance. Training can be outsourced to a 3rd party, like well-reputed boot-camps, courses, development trainings can be used. While for hiring new talent, you don't need to deal with the overhead of training your team to a desired level, but you will have to provide KT to the new hires and make them get acclimated to their new environment. This may end up taking just as much time.

?? The pivotal choice: build or buy your ML dream team. ??? Crafting your own entails the arduous journey of recruiting, nurturing, and retaining ML virtuosos. ?? Alternatively, opt to acquire, partner, or outsource expertise, offering swifter, cost-effective solutions. Yet, tread with care: the path demands astute selection and seamless collaboration for triumph.

Evaluate whether to build in-house ML expertise or partner with external vendors, consultants, or specialized firms to augment your team. Invest in hiring data scientists, machine learning engineers, and domain experts with the necessary skills and experience to execute your ML roadmap effectively.

?? Cultivate a culture of perpetual growth to elevate your ML prowess. ?? Embrace experimentation and innovation, urging your team to pioneer new data vistas and ML methodologies. ?? Stay vigilant, tracking ML trends and industry benchmarks to fine-tune your strategies. ?? Benchmark against competitors, soliciting insights from luminaries and peers alike. This relentless pursuit of excellence fuels mastery in the dynamic realm of ML.

FOMO is the WORST reason to get into ML. Just because your competitors are using ML does NOT mean that they're extracting value from it, and often the ROI is low. Blindly following what your competition is doing is a recipe for disaster when getting into ML/AI.