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Machine Learning Project: Navigating the Model and Product Development Phase

Product development techniques are changing faster than ever as manufacturers use machine learning to increase their quality. The process of developing flawless products using ML is very different than traditional methods. It comes with all kinds of considerations, risks, and constraints. 

The only way to create a high-quality product using machine learning is to overcome the initial challenges and find solutions. That can sometimes be easier said than done, but if your dev team puts their heads together, nothing will stop them from increasing product quality. Let's see how everything works in more detail.

Using Machine Learning To Develop Better Products

Developing a new product is exciting. There's just something special about arriving at work and combining your skills with the skills of experienced engineers and scientists to create a new, high-quality product that uses machine learning to provide details for various outcomes. However, ML can help optimize other areas, not just product development. It can help optimize the supply chain, detect product defects automatically, or even help your customers get a better feel for how your product will look and behave in certain situations. 

The development includes a lot of collaboration, technical design, and feature design. Your data scientists will lead the rest of the team, but they will also run into all kinds of problems months into the product development phase. All that hard work can lead to a mediocre app which can lead to frustration and all kinds of unforeseen issues. Developing AI in the industry is the hardest of all due to the many technical challenges found across production plants. 

After months of hard work, your team is ready to release a pilot product, but it turns out that users aren't too interested in its features. The management team becomes furious, the technicians feel like they've lost time, and the engineers feel like they've failed. Such things happen during product development all the time, and experienced technicians can lose their status practically overnight. 

Sadly, this scenario happens to many companies that try to use machine learning during digital product development. What's more, well over 50% of all AI projects rarely make it further than the proof of concept due to various design challenges and increased costs.

Put simply, the process of ML product development comes with different risks than regular product development, and it's up to the manufacturer to find a way to overcome those challenges and create a working product. Successful development of AI in the industry can take years to complete.

Why Machine Learning Product Development Fail

There are multiple reasons why ML product development projects seem to fail before they reach production. The main reason is the lack of internal communication between departments. The data scientists and product developers have to be on the same page to be able to overcome the challenges down the road. The miscommunication happens easily as the data scientists focus on technical feasibility while the development team focuses on increasing desirability and overall value. It's easy to forget about the data quality needed to develop a new product from scratch.

The real challenge is to consider all key factors across the departments to create a balanced product with little or no risks of value. Here are some of the actions that lead to failed products.

  1. Not Testing Model Feasibility Enough

The essential part of ML-driven product design is to create a strong foundation. The best approach is to focus on the biggest challenges first, and if they can't be solved, the dev team should look for another, more feasible approach or abandon the project completely. For that to work, the project needs to have access to high-quality data, employ technicians with the right skills, and have the same level of expertise across the entire development phase. 

  1.   Investing Too Much Time Into The ML Model Before Product Validation

Of course, your data scientists will focus most of their energy on improving ML models, but all of that effort could be wasted without validation. Put simply, if the product doesn't help the users as intended, the solution could fail. That's why the process of machine learning needs accurate data to work.

  1. Demotivated Data Scientists

Most data scientists involved in AI in industry development prefer developing advanced ML models that challenge their skills. While that's great, the problem is that most data scientists don't share the same skills with software engineers, so they often feel like they are doing too much work. 

If you fail to mitigate the first two risks on this list, your data scientists will be frustrated and might abandon the project. That's why you have to plan every step during development very carefully. 

The Right Development Approach for Machine Learning Product Development

ML product development goes through four stages. They are discovery, introduction, growth, and maturity. Every development of AI in the industry must go through all four stages, and it's essential that every stage is handled correctly. Here are the four steps in more detail:

  1. Can The Design Issues Be Solved With Technology?

Most product ideas come from product managers. Whenever they come up with a plan, it's up to the technicians to figure out if the plan can be executed using existing technologies. It's extremely important to trust your technician's skills and experience during the earliest stages, as they will find the best way to build the product that does what it's supposed to do. 

The goal here is to create a detailed roadmap of future actions and ensure that all available technological solutions can work together to create a high-quality product. You'll also need a steady stream of high-quality data for the ML model to learn and make accurate predictions. Most AI solutions that never make it to the market fail because of poor quality data used to train the ML model behind them. Therefore, it's essential that you select the right technologies and data streams for everything to work. 

  1. Provide Users With Value

Once you know what type of product you want to create, you have to listen to your users to ensure that the final result offers value and is appealing enough. The key here is to identify the ideas users would like to interact the most with and create a strong value proposition around them. The designers have to find the best way to present what the ML model learned to allow the users to make the right choice. The final product has to provide accurate answers very quickly, with little or no latency.

The development of AI in industry often leads to poor user interfaces that make it hard to navigate the solutions. The product must be easy to understand and provide accurate results in a readable format. This is the most important step, as it's the one where all of the product design solutions and features come together to form the final product. If done wrong, the product may never make it to the markets.

  1. Improving Product By Driving Value Through Machine Learning

Once you have a product that provides value and is desirable enough, it's up to your engineers to work on increasing its ML capabilities. They have to put the ideas to the test and increase the model's accuracy. The so-called technology maturing is an essential step in the product development process, and it's essential that the final version is easily scalable and applicable. 

This is the stage where the sketches become a reality. The ML models will keep learning, and engineers have to monitor the process closely to ensure accuracy. Otherwise, the ML models will stop being productive and become useless. Engineers must focus the learning on user value proposition and monitor all KPIs. 

  1. Product Evolution 

When developing solutions for AI in industry, it's essential that the ML models are trained using historical data, as well as real-time data. That's the only way the ML model can learn how to make accurate predictions for specific problems. While the model might work on internal data, it might run into problems when applied to other data pipelines. Your engineers have to find a way to help the ML apply to other data pipelines as well.

If they manage to do that and find the best way to scale the model, the product is ready to launch. But before it reaches the market, your team should add more features to increase the product's capabilities and reduce risks.

Conclusion

The development of advanced AI in industry solutions is a complicated process that relies on many different factors. However, success largely rests on hiring the right talent and using the right data pipelines when training the ML model. Even if that works out, there are plenty of other challenges you must face along the way.