Three Biggest Reasons Data Scientists Struggle to Get ML Models Into Production

The world has reached a point where businesses worldwide have to up their game to meet the rising demand of the markets. The customer experience quality has reached an entirely new level, and many companies are having a hard time catching up with the latest trends.

Creating a fully immersive customer experience is a key factor in business success, so many companies started developing their own machine learning models. Their role is to gather information to improve the products and the customer experience. However, most ML models never reach production, even though they are very expensive to build, train, and test.. Keep reading, and we’ll tell you why that is.

Common Problems With Machine Learning Models

The harsh truth is that many corporations simply aren’t ready to set up and run their own ML models. Significant investment is required to create a viable DevOps team, and they have to set up high-quality data pipelines to get the best results. However, with the lack of experienced data scientists and poor communication between departments, only around 10% ML models ever get released. Here are some of the biggest problems companies face when developing ML models.

1. Lack of Communication Between Departments

Developing a working ML model takes a lot of time and patience. It also involves multiple departments including teams from DevOps, engineering, and data science. One of the reasons why most ML models never get to see the light of day can be found in the lack of communication between departments.

Most companies keep their IT and data science departments as distinct entities. Both of these departments work in different ways. For example, IT focuses on finding working solutions to all kinds of issues and making sure that they are stable and scalable. On the other hand, data scientists focus on finding the breaking point of existing systems. They experiment with all kinds of features and possibilities, many of which can overload a system and lead to unforeseen problems.

As you can already expect, these two different approaches lead to serious problems in communication. Moreover, data scientists often don’t include the engineering department in their work. When that happens, engineers have a hard time understanding the details put in place by the data scientists. Engineers often approach the same problem from a different perspective, or they simply struggle to implement the details provided by the data scientists.

The miscommunication between departments often keeps everyone involved running in circles, making it much harder to create a working ML solution.

2. Explaining What The Model Should Look Like To The DevOps Team

Another prominent issue that keeps holding many ML models back is the inability to provide the right information to the DevOps teams. Even if the data quality the model is based on is on a high level, data scientists often don’t provide the right details when writing scripts and instructions for the DevOps team.

The result is that the engineering team doesn’t have a clear picture of what they are supposed to do. If the documentation they get isn’t accurate and sufficiently detailed, they might try to provide a solution using different logic. If they can’t make sense of the documents, they often rewrite the code in a different language, which can cause unintended consequences.

For example, even if the documentation is right and the code is implemented correctly on the first try, there’s still going to be a lot of back and forth between the engineers and the data scientists. They will try to figure out how the code works in more detail, making changes on the go. Every change is often followed by bugs that take time to manage.

Furthermore, data scientists can only gather the data and hope that the engineering team does a good job. They can only wait for the end product without the possibility to help the production itself. Many ML models are too complex and require constant maintenance and updates to start working properly. That alone is the biggest reason why most ML models never get deployed.

Lastly, some models are based on newer technologies, so they can’t be written in certain production languages. If that’s the case, engineers have to simplify things to be able to create a working solution, losing performance quality along the way.

3. The Model is Not Accurate or Predictive

If the initial ML model uses the same logic without including different factors, it will make bad predictions. On the other hand, if it’s too accurate, you will be able to predict the results with ease once you understand the logic. That’s why ML maintenance needs constant supervision from both your data scientists and your engineers.

One of the biggest challenges to any ML model is to make sure that it stays accurate in the long run. That means that your teams have to work together to improve the data quality by conducting online measurements, retraining pipelines, and constant A/B testing.

Value of Adaptive Machine Learning

Unlike traditional machine learning, adaptive ML is run online, and it has the option to quickly implement new information into an existing system and quickly find out if it’s useful or not. The models run on a single-channeled structure that can use multiple data collection methods and analytics. It doesn’t simply collect and analyze data; it also learns from it.

The best thing about it is that it keeps learning as long as you feed it with new information. That way, every future prediction it makes will be more accurate. Adaptive ML runs in real-time to adapt to new information as soon as it is received. As a result, the model will be able to provide high performance and incredible precision. It’s agile, powerful, and very efficient, and you don’t need a dedicated DevOps team to interpret it as production ready. The same code that is developed and trained locally can be deployed into production as a microservice – without any new production scripts or code changes. For more on this, check out PandioML – an open source offering that enables adaptive machine learning at scale.


There’s no doubt that we’ve come a long way when it comes to machine learning. However, new technologies, but finding high-quality information as well as experienced data scientists often slow the process down to a complete stop.

With the use of adaptive ML, companies can simplify the process of building and training models, minimize model drift and ensure real-time precision, and eliminate costly back-and-forth between the data scientists and the DevOps that are primarily responsible for operationalizing these models in a batch environment.

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