Home > Blog > Machine Learning > 9 Significant MLOps Challenges and Lessons Learned

9 Significant MLOps Challenges and Lessons Learned


Trinh Nguyen

Oct 23, 2023

Machine Learning Operations, or MLOps, have proved to be a vital practice to automate and streamline complex machine learning workflows and deployments. It’s a collaborative endeavor, uniting data scientists, operations experts, business analysts, and IT professionals in the pursuit of deploying deep learning and machine learning models seamlessly within a large-scale production environment.

However, machine learning operations are still in their early stages, which means difficulties will inevitably arise. This article delves into nine core MLOps challenges, accompanied by potential solutions that empower your MLOps journey.

Let’s get started!

What Is MLOps?

Machine learning operations (MLOps) is a set of practices that automates and streamlines machine learning workflows and deployments. It’s a team-based effort that involves data science, operations teams, and IT.

Adopting these practices allows you to automatically deploy deep learning and machine learning models in sizable production settings, leading to improved quality and simplified management procedures.

MLOps is gradually growing into an independent solution to machine learning life cycle management, covering the complete lifecycle, from data collection and model building to deployment, diagnostics, and governance.

MLOps’s major phases

MLOps’s major phases are as follows:

  • Data gathering
  • Data analysis
  • Data preparation/ transformation
  • Model training & development
  • Model evaluation
  • Model validation
  • Model deployment
  • Model monitoring
  • Model re-training

Why Is MLOps Important?

MLOps capabilities lie in its efficiency, scalability, and risk mitigation.

Firstly, MLOps enables data teams to design, deploy, and produce higher-quality machine learning models in a shorter time period.

On top of that, machine learning operations also allow extensive scaling and management, enabling the continuous integration, continuous delivery, and continuous deployment of thousands of models.

Particularly, MLOps makes ML pipelines reproducible, allowing for closer collaboration between data teams, mitigating conflict with dev and IT teams, and speeding up release velocity.

What’s more, machine learning models usually require regulatory inspection and drift-checking, and MLOps makes it possible for faster and more transparent responses to such requests while also ensuring greater compliance with a company or industry’s regulations.

9 Main MLOps Challenges and Solutions

While MLOps brings multiple benefits regarding managing machine learning models, applying it presents its own challenges. Let’s take a closer look at each of them, along with their potential solutions.

1. Lack of Data Science Talent

Glassdoor lists 11k+ jobs linked to data science and 15k+ positions related to ML engineers, each of which requires a distinct level of expertise, education, and abilities. This means organizations are increasingly investing in their personnel to realize machine learning benefits.

However, this raises problems in finding competent data scientists and minimizing attrition in machine learning and data science teams. Employee attrition and a lack of expertise have a huge impact on the delivery of the machine learning lifecycle, as well as the timescales required to begin every new project.

Mitigating this obstacle is tough owing to its competitive nature. Hiring remotely is one option since it enhances the odds of having more talented data scientists. Another good option is to explore young people who wish to build skills within the organization through internships or graduate programs. Also, in the short term, hiring consulting firms for AI projects may help your company get started more quickly.

2. Unrealistic Expectations

In the first phase of the machine learning pipeline, business stakeholders, including the customer, solution architect, and the technical team, develop the solution. At this point, the first task is to set expectations.

The challenge is that some companies see artificial intelligence as a miraculous solution for every issue and set unfeasible goals. This belief is mostly held by non-technical stakeholders who adopt the hottest buzzwords without taking the business context into account.

This is where the expertise of technical leaders comes into play. To address the challenge, they must make sure all stakeholders are informed of the viability of the solution as well as their limits through accurate and transparent data.

3. Data Management

As much as data quality greatly influences machine learning models, it is also a major challenge when building and implementing MLOps.

Data discrepancies

Since data needs to be obtained from numerous sources, there is often a mismatch in data formats and values. For example, while current data may be easily accessed from a pre-existing product, historical data may be gathered from the customer. Such differences in mappings, if not appropriately resolved, could negatively affect the entire machine learning model performance.

Limiting data discrepancies might be laborious and time-consuming, but it is nonetheless necessary. The ideal solution is to centralize data storage and have shared mappings across multiple teams.

Lack of data versioning

Even when the data in use is undisrupted and in the correct formats, there’s always the problem of time. Because data constantly changes and renews, the outcomes of the same machine learning model might vary greatly.

Data versioning comes in many forms, from different processing procedures to new, modified, or deleted data. The model performance won’t be good unless you version it efficiently.

So, the solution is to develop new data versions or edit current data dumps for space optimization. Nevertheless, with space optimization, you may want to save the metadata of a certain data version so that it can be recovered from the updated data unless the values are also changed.

Data trends

Since machine learning models are trained on local and past data, assessing how well they function on new, unseen data is vital. However, there are situations when data drift, referring to the addition and rapid changes of new data over time, makes the initial training data lose its statistical properties and causes the ML model performance to decrease due to less accurate predictions or failure to perform as intended.

For instance, a related news article or new tax regulations may have a significant influence on import statistics or stock data. The list goes on, and it’s difficult to deal with such abrupt interruptions.

The only way to deal with this challenge is to keep the data as updated as possible, especially if the machine learning solution is time-sensitive. This can be accomplished by using automated crawlers that can periodically verify data.


4. Security

Machine learning solutions often handle very sensitive data and work on tricky tasks. As a result, safeguarding the environment is crucial for the long-term viability of the ML project. According to a new IBM survey on AI adoption, 1 in 5 companies report having trouble guaranteeing data security.

The most prevalent issue regarding security is running outdated libraries. Users usually aren’t aware that this could cause a number of security holes that could let malicious attackers in.

Another security flaw is model endpoints; data pipelines are not adequately protected. These are sometimes made public, potentially exposing sensitive metadata to third parties.

Security can be a tricky issue in any MLOps setting; therefore, having software that provides security patching and support is vital for your project’s existence and deployment to production. It is also recommended to employ multi-tenancy technology that protects the internal environment and data privacy.

5. Inefficient Tools and Infrastructure

Because ML models are highly research-based, much testing is required to find the best path. However, running many tests might be chaotic and taxing on company resources.

Different data versions and processes must operate on hardware capable of doing complicated computations in a short period of time. In addition, inexperienced teams usually execute their tests on notebooks, which is inefficient and laborious.

If hardware is a challenge, development teams could ask for budgets for virtual hardware subscriptions such as those on AWS or IBM Bluemix. Regarding notebooks, developers should make it a habit to do tests on scripts because they are easier and far more effective.

6. Lack of Communication

Culture shock

MLOps necessitates a culture of collaboration and cooperation across many teams, such as data scientists, data engineers, and operations personnel. This can be tough, particularly in businesses not accustomed to operating in this manner.

In a 2023 NewVantage Partners study, 79.8% of respondents claimed that cultural obstacles are the most significant barrier to enterprises trying to become data-driven.

Lack of user engagement

Failing to help end users understand how an ML model works or what algorithm is providing insight is a common pitfall. After all, this is a complex subject, requiring time and expertise to understand. If users don’t understand a model, they are less likely to trust it and engage with the insights it provides.

Organizations may prevent this issue by involving clients early in the process and asking them what problem the model needs to solve. Show and explain model results to users on a frequent basis and allow them to give feedback throughout the model iteration.

7. Lack of Iterative Deployment

While the MLOps solution is being deployed, the deployment and development teams have to work together to execute the launch. However, in most cases, the dev and production teams are in disagreement and only begin to collaborate at the end of the solution design. Although the machine learning solution uses a research-based methodology, this one-time model deployment technique is flawed, insufficient, and not effective.

To solve this, your business should consider deploying machine learning models iteratively, which can significantly reduce rework and friction. Ideally, the solution’s many modules should be set up iteratively and updated in sprints.

8. Suboptimal Company Framework

A company’s current software deployment infrastructure may be insufficient or of little use for implementing a machine learning model. For instance, a Python-based ML solution might have to be implemented through a Java-based framework to be compatible with the business’s current system. This might cause the work of the development and deployment teams to double since they have to recreate most of the codebase, which takes a significant amount of time and resources.

A long-term solution would be to invest in developing a distinct machine learning stack that can integrate into the company’s framework. Meanwhile, a straightforward workaround is to use virtual environments to install machine learning solutions for the end user. Docker and Kubernetes are incredibly helpful in these situations.

9. High costs

MLOps might need a significant financial and time investment. To make MLOps successful, organizations must be ready to spend money on the proper equipment and resources. It can take from a few machine learning developers a couple of months to a dozen engineers up to two years to construct a machine learning platform.

However, it’s common to see that development teams are unable to access corporate resources due to financial constraints or because a resource is shared by several teams. Despite being essential for scaling ML solutions, resources with powerful computation or massive storage capacity don’t typically fit inside an organization’s budget. In this case, ML teams have to find a workaround (usually suboptimal) to enable the solution to operate with the same power.

To alleviate budget limitations, the data science teams need to look at the business side and do a detailed cost-benefit analysis of restricting provisions vs. the return on investment from workable solutions that can run on such provisions. For the teams to receive correct feedback on the cost data, other departments may need to work with them. Key decision-makers in businesses tend to have either a short-term or long-term profit-oriented perspective, and a cost-benefit analysis that forecasts growth may be the drive that breaks down such obstacles.

Overcome MLOps Challenges

The journey through MLOps is marked by both immense promise and profound challenges. We’ve unraveled these challenges, from the shortage of skilled data scientists to security concerns and the need for efficient tools and communication. Addressing these hurdles will definitely help you bring ML models into production swiftly and efficiently.

Instead of suffering from these challenges, getting help from an MLOps service would be a smart choice. At Neurond, we offer MLOps consulting and MLOps development for businesses across diverse industries. With our strong expertise and many years of experience in creating state-of-the-art AI and ML solutions, not only can we solve your business needs but also work alongside you to turn your business ideas into reality.

Whether you’re an enterprise seeking process automation or a startup in pursuit of scalable AI projects, our MLOps AI Service is here to transform your aspirations into reality. Contact us now!