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Gilbert Mutai
Gilbert Mutai

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How to transform machine learning models into data products that generate revenue and address actual business issues.

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For two years, I developed web applications and worked with others to find answers for two years. I developed both front-end and back-end skills as a full stack developer. In my free time, I made contributions to open source. I was still not entirely satisfied with my effort.

I support routines and goals. For years, I leveled up as a developer daily. I woke up feeling inspired to develop coding. My evening classes increased my knowledge. Open source and language study took place on the weekends.

With this program, my talents improved. I have intricate, scratch-built full-stack applications on my GitHub. My input was combined. However, I hadn’t reached my full potential.

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What was absent? Why was I unable to feel happy? There has to be more out there to explore.

I liked to read a variety of books, both fiction and nonfiction. Podcasts about technology and books on influencers, psychology and self-improvement appealed to me.

When going over “So Good They Can’t Ignore You” again this week. It encourages one to pursue happy, purposeful employment. Building “career capital” by acquiring rare, desirable abilities struck me as a brilliant notion. You can now have control and alternatives for your job. According to the author, “deliberate practice” is making investments to develop talents that employers will pay for.

He presents a “rule of financial viability”: only create abilities that consumers are willing to pay for. I gave this some thought. I had been unable to secure web development jobs for months. There was too much competition in the field. I didn’t stick out.

I have a background in statistics and math. Applying models for value is what I seek for as an entrepreneur.
It dawned on me abruptly: Could it be that my attention was being diverted? The newest thing in town is a machine learning.

How can I create things that work and make use of my unfair advantage? At this point, I considered doing more than just creating machine learning and applying the current models. ML models with integration into products and services could be quite valuable. I imagined transforming models into business-problem-solving data products.

I did a lot of study on ML techniques. I considered the large-scale integration and deployment of the model and iterating continually in response to new real-world data.

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This seemed to be my eureka moment. My knowledge of math, statistics, and programming all came together in ML.
It also fitted perfectly in the frame of mind of being business singular. Machine learning has an infinite number of practical applications from banking to healthcare and even agriculture.

When I had fantasized about for a singular future that evening, I was charged with gung-ho and motivation.

I felt fully satisfied when I was able to blend my interests and skills in just the right amount. I took a stride to develop models that are of help in solving real-time problems and rather looked forward to what future held for me in terms of lessons.

My love for machine learning, software, and commercial effect was reawakened by this. Even though I’m only getting started, I’m prepared to work hard and go far. Find the intrinsic abilities, passions, and skills that the world is willing to pay for if you’re an unfulfilled developer. Your way is there.

In conclusion, my search for answers has brought me to a realization: the combination of my machine learning, software development, and deep grasp of business problems presents a once-in-a-lifetime opportunity.

I’m committed to using this potential to develop useful data solutions that advance company expansion and address pressing issues in the real world.

I am motivated to make a significant impact and have a sense of purpose as I set out on my journey. I cordially invite you to accompany me on this innovative and exploratory adventure. When we work together, we can use technology to create a future where positive change is fueled by data-driven solutions.

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