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As Machine Learning is becoming in demand these days, it is unwise to ignore it altogether. Statista projects that the AI market is going to grow in a flash by 2025 and obtain 126 billion US dollars. McKinsey, a global management consulting company, did some calculations too, and 70% of companies are on their way to adopt at least one AI sub technology by 2030. This power move will increase the world’s economy by an estimated $13 trillion.

It wouldn’t be fair if you’re a startup and you wouldn’t make an attempt. There are thousands of existing ML startups today, but what would it cost to become the thousands first? If you want to change the world, my guess is you should start small. C.S. Lewis put it this way —

“You never know what you can do until you try.”

Machine Learning Explained: Startup’s Utility Box

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What Does It Take To Start?

It is always hard to start your business as it requires an abundance of moves and constant investments of time and money. Stanford’s university Sam Altman and Dustin Moskovitz, who have been teaching people how to start startups for more than 14 years, see this process as a journey from having an idea, creating a team of software developers, and assembling a product, and up to market involvement. 14 years later, their lectures still make sense!

Machine Learning Explained: Startup’s Utility Box

Remember these steps as they will help you optimize your time and save money usually spent on chaotic moves. Start with an idea and everything else will pull up.

The Idea

Machine Learning, which is a subset of AI, originates from algorithms that teach computers to act on data, understand data patterns, self-learn, and predict the future based on the acquired knowledge. No exhaustive coding is required, but you still need to know Python as approximately 60% of developers apply it in data science. Machine Learning became popular about ten years ago, but there are cool ways you can use it for your startup today:

E-commerce: online shopping has been the most popular online activity around the world from the moment it became possible and, nowadays, when pandemics hit, it is the only means of shopping left that guarantees your safety. Statista predicts a 6.5 trillion US dollars growth of e-commerce by 2022. So, it is a brilliant way to offer something huge here to the public. For example,

  • an e-commerce human-oriented search engine that will significantly improve the experience of product search results
  • image and video recognition search for enhanced user experience
  • chatbots and personalization of user experience
  • tools for smart homes

Healthcare: as long as people will be interested in health improvement there will be a place for medical technology advancement. Statista expects 7 billion US dollars of investments made into the big data field by 2021. So, you still have time to jump on that train. For instance, think about

  • automated analysis of medical images to set right diagnoses
  • automated patient-screening methods and navigation
  • chatbots and online consultations with remote vital signs check

Cybersecurity: with the digitalization of every industry humanity also obtained new ways of fraud in cyber hack attacks, data breaches, and data leakage by specific malware that harms businesses and puts confidential information of every user in danger. Statista states that the cybersecurity market should grow up to 248 billion dollars and beyond this number by 2023. So, why not make your contribution to this field? For example, create

  • enhanced hack and data leakage prevention
  • predictive detection of threats via smart analysis
  • ML integration into enterprise security monitoring

Fintech: everything concerning financial services and digital payments has become so convenient due to becoming online instead of offline and in queues. This industry has vast potential and will, without a doubt, win a jackpot within the following couple of years. So, why not try your fortune and develop something for

  • predicting future market trends
  • automating customer services
  • detecting customer risk profiles

Education: Covid-19 has changed so much of what we call “normal” education. When everyone had to shift to online studying, most of the educational institutions met the problem of being simply not ready. So, online education tools are the technology of tomorrow! There’s a need to reflect on proposing

  • an educative platform for kids with special needs
  • a program that makes the smart choice of school to apply to (based on children’s skills)
  • creating study content automatically based on the topic of study

Preservation of Land Resources: global warming, destruction of rainforests, pollution, contamination, drought, famine, hurricanes, tsunamis, and other environmental changes are not only the words present in the dictionary now. We experience them and the consequences of human disrespect of nature are shocking! That’s why there is a great need for ecosystem preservation. This can be done by useful ML tech support such as

  • contamination prediction and detection
  • provision of data on environmental changes and climate control
  • reduction of energy and water usage

There are as many ideas as your head is creative to brainstorm further. Sometimes, even a one-horse idea might skyrocket. So, get an idea and make sure it can’t be replicated that easily. Within time, your idea will expand and become more ambitious.

Machine Learning Explained: Startup’s Utility Box

Good ideas need specialists to make them a reality. So, let’s proceed with understanding how to choose teammates for your ML Startup!

The ML Team

The closest team member of your Startup is a co-founder. Co-founders should be on the same wave as you are. What’s more, if you are not a tech-savvy person, your co-founder should be a techie specializing in Machine Learning development trends and nuances.

The CEO of the company is a team member, who motivates employees to excel every day. If you are going to recruit a startup team in Machine Learning, be ready to be on the same wave as your employees are. ML specialists are rare talents with deep knowledge across many fields. You’ll need a strong and self-motivated team, so prepare yourself to be a strong and confident leader, who can drive the company forward. Intensity is the word that should be on the tip of your tongue because intensity gives power.

Sam Altman says that at the beginning you have to spend at least 25% of your time hiring. This number is just an average one as everything depends on your business idea and its requirements. Altman also suggests staying small as long as possible and expanding only after a certain breakthrough — initially costs per hire will be enough to try the patience of a saint. What about the developers’ knowledge? I think, hire the best ones, don’t compromise. For again, it is better to have 1 or 2 professionals than 5 amateurs. And, last but not least, consider aptitude over experience. You haven’t got the time and money to be extra choosy!

“There are three things that I look for when I hire people: Are they smart? Do they get things done? Do I want to spend a lot of time around them? And when I end up with a ‘yes’ to all the three, I almost never regret this hire!” — Sam Altman

Now you have an idea and a team, what’s next? The answer is in the product to develop. if you still don’t have a team, consider successfully outsourcing software development! Here you will get your idea implemented and a dedicated team as a bonus!

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The Product

Machine Learning is the key to prediction-making and autonomous decision-making. After the machine is being educated, i. e. programmed, it will carry out tasks on its own, without specific human help because ML is powered by AI. To make sure ML works according to your boldest expectations, the first step is to define the requirements, outline the scope of work, set standards (metrics) and let the techies on your team explore all the possibilities. Make this process a flexible one, and think outside the box!

ML includes four types of learning possibilities. These are:

  • supervised: algorithms are programmed to predict outcomes by processing the labeled data (various amounts of training data that has tags with outcomes)
  • unsupervised: algorithms detect patterns without processing the labeled data (e.g. associations, clustering, or detection of anomalies)
  • semi-supervised: it’s a merge of the supervised and unsupervised learning algorithms
  • reinforced: algorithms learn based on obtained feedback over some time (mostly robots work like this)

The product itself also has the background to adhere to. The major thing is to understand your intentions — is it an ML product you’re trying to build or integrate ML into a product? This straightforward question matters. Here’s why. ML product stands on ML models — the pillars of any developed product, which have ML at its core. Don’t reinvent the wheel and get engaged with understanding the input and output of the models and that’ll be enough.

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The End-Users

The key contributor to assembling a product is the end-user. Find a couple of end-users and give them to test your product. It is best to make a product for a small number of people to love dearly, than for a larger number of people, who will only like it. To add, what is the most important thing for the end-user? Right, the user experience or, as we know it, the UX. Yes, it is critical in software development, and yes, we use ML to enhance UX. It is less important only in the case of performance accuracy as the top priority.

Tech Stack and Challenges

Building an ML solution requires not only the knowledge of Python and algorithms. Machine learning engineers are in high demand as their skill set includes Java, R programming languages, probability and statistics, data modeling, computer science, and system design. Also, they have to be flexible team members ready to work side by side with project managers, UI/UX designers, business analysts, and other team members of a cross-functional team.

Machine Learning engineering teams face a lot of challenges. ML algorithms tend to act like a “black box”, which takes the input and produces the output being completely covert. So problem-solving and critical thinking are the ones of the most important skills too.

Machine Learning Explained: Startup’s Utility Box

The last instance of this journey is execution — not so fun, but worth the candles. Let’s dig deeper!

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Accumulating ideas is quite easy but executing them is the opposite. Implementation takes an abundance of effort and sleepless nights. There are two focus questions the answers to which will help you a lot. First, “What to do?” And, second — “How it’s done?” If you figure out these focus questions, you will get stuff done quickly and top-notch. To add, make a plan, set company goals, and communicate your product to the masses. Continuously.

It would be also good to find a business partner that can provide expertise in the execution of your ML product. Overview of the machine learning companies can be found on which enlists the best software development companies based on transparent reviews. Among the top-rated companies specializing in data science and engineering are IBM, Intel, Google which also provide machine learning data catalogs to transform, model, and visualize data.

Read also: the benefits of  CRM in the healthcare industry

Final Words

If you want to test your idea, we can always help you out. Follow the link and let’s make a discovery! Choose our web development services for startups to succeed!