Here is the list of top10 reasons why large-scale machine learning projects fail
Nowadays we can read about artificial intelligence and
machine learning content almost everywhere. Undoubtedly
AI and
ML have the potential to solve a lot of problems. However, not all machine learning projects succeed. According to some reports, 85% of Machine Learning projects fail. There are many predictable ways that
ML projects fail, which can be avoided with proper expertise and caution. Here is the list of top10 reasons why large-scale machine learning projects fail.
Using Data That Isn’t ML-Ready: Most companies are engaged in some form of digital transformation, which means they’re generating data. Machine learning can do remarkable things with data, but it has to be ML-ready or “clean” data. And there are many ways that data can fail this test. The data needs to be multifaceted enough that ML can detect meaningful patterns in it. This is one of the top three use cases our customers are pursuing since energy represents almost 20% of their output costs.
Lack of Expertise: The bar for data scientists is getting lower and lower. Most
machine learning or artificial intelligence projects requires experienced data scientists to deal with tasks such as model selection, performance monitoring, and evaluation.
Lack of Collaboration: Lack of collaboration between different teams such as Data Scientists, Data engineers, BI specialists, and engineering, is another major challenge. This is especially important for the teams in the engineering scheme of things. It is the engineering team who is going to implement the machine learning model and take it to production.
Lack of Data Strategy: Only 50% of large enterprises with more than 100,000 employees are most likely to have a Data strategy. Developing a solid
data strategy before you start the Machine learning project is critical.
Technically Infeasible Projects: Since the cost of
ML projects tends to be extremely expensive, most enterprises tend to target a hyper-ambitious moon-shot project that will completely transform the company or the product and give an oversized return or investment.
Missing Good Quality Data: As the impact of the data set increases, there are also new challenges emerging. There are a lot of situations where you will have to merge data from a bunch of different data sources. Data with bad quality is not usable and could result in misleading results.
Lack of strong signals in the data: The right data has the signals you need to optimize for business results. Machine learning can’t work without the right data. Run small experiments and use common sense to find the right input data for your problem. This is one area where experienced data scientists can add a lot of value.
Technically Impossible Tasks: Because ML projects are very costly, most companies tend to focus on a Moon-Shot Project. Such a project may push the data science team to its limit and is not likely to complete. In the end, the management loses confidence and stops investing.
Lack of leaders’ support: Sometimes leaders lack the patience and technical confidence needed to fulfill a machine learning project. For a machine learning project to be successful, it is very important to keep everyone on board.
Optimization Without Exploration: In machine learning, it is important to build the ability to continually validate and improve the model. It is important to understand the value of not simply using the best model for your entire audience. For models that provide explanations, you need to retain enough variation in the data to continually validate those explanations and generate new insights.
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David Ferrucci, who led the team that built IBM’s famed Watson computer, was elated when it beat the best-ever human “Jeopardy!” players in 2011, in a televised triumph for artificial intelligence.