ML System Design: Most Common Pitfalls and How To Overcome Them

Dmytro Ivanov
Alina Ampilogova

Machine learning system design is a complicated process. Several companies have already learned that fact the hard way. According to PwC, 24% of enterprises consider data complexity one of the major factors hindering ML technology adoption. Furthermore, around 68% of enterprise executives reported not tracking model performance and data integrity. 

While these numbers don’t look optimistic, they don't imply that company leaders should give up on their machine learning initiatives. Most machine learning systems design pilots fail not because there are too many steps in ML system design but because each step can be sabotaged by pitfalls that are dangerously easy to overlook.

To help executives ensure their project reaches the finish line, we decided to provide a comprehensive breakdown of all the pitfalls teams can encounter in ML system design and offer tips on how to avoid them. 

What is ML system design: a complex concept in simple words

ML system design is a set of steps, practices and strategies covering regulatory compliance, data management, model training and everything else a functional ML model needs. It’s an integral part of any ML product, making all components click together, securing long-term value, and enabling high-level security

There is a system behind every solution. Whether it’s hardware or software, it’s based on a certain model that was developed in accordance with specific requirements and needs. Machine learning models aren’t different in that regard. Leveraging ML takes a number of design steps that define how the final product will be used, what kind of data it will need, how it will be trained, how its efficiency should be measured. 

Therefore, ML system design is essentially a process of creating an ML model that is scalable, resource-efficient, and capable of adapting to enterprise growth. What is worth keeping in mind, is that ML design also consists of multiple complicated steps that require attention to details and awareness of potential development issues. Such issues include human factors, data-related challenges, and even the impact of global events. Therefore, it’s imperative to dissect every obstacle in advance.

Key Challenges in ML System Design

Despite easily fitting in a large number of sectors, machine learning remains a tool that needs to be explored thoroughly before utilization to avoid organizational resistance and many other issues affecting results.

Large volumes of complex data

The company has too many data sources and too much unstructured data while lacking a dynamic approach to aggregating information.

Project scaling and integration complexity

The company is unable to embed the ML system into its structure without interrupting performance and has issues synchronizing the model with its growth.

Lack of necessary skills and experts

The company lacks knowledge and experience with ML system design, prompting blindspots across the roadmap, miscommunication, and numerous development errors.

Lack of required tools for model creation

The company doesn't have the platforms necessary for training a functional ML model.

From my point of view, stakeholders sometimes underestimate the commitment AI/ML takes. It's a constant work in progress. Even once you build and train a model, you need to ensure that it matches your enterprise's pace. Therefore, you need to constantly monitor it and avoid potential issues during and after development.

What are the most common pitfalls of a machine learning project lifecycle?

While machine learning is becoming more sophisticated and its adoption rate is surging, at its core, it remains a complex effort with multiple stages. Each stage requires a particular skill set and understanding of ML design patterns to be successful. Therefore, it's important to break the entire machine learning system design journey into fragments and examine the specific challenges of each step.


Project scoping

Project scoping is the starting point of ML system design, within which teams and stakeholders identify the priorities and goals they intend to achieve with the model. During project scoping, participants also establish performance KPIs, potential constraints, and limitations, as well as the depth of model personalization. Mapping all these details at the beginning of the journey defines the success of the project.

It's a common belief that ML design is 80% development. However, it's 60% communication, 30% data preparation, and 10% model development. Without nailing the 60%, there is no point in advancing further.

In the course of planning and outlining goals, the following challenges can emerge:

  • High cost-low ROI ratio
    Any ML design project is costly and time-consuming, so stakeholders expect a proper investment return. To make this expectation a reality, executives should be realistic about the needs, resources, and processes of the enterprise.

  • Conflicting requirements
    It's crucial to dissect a potential ML system design adoption scenario and envision the needs and concerns of each group to provide a clear image of potential issues between different stakeholders.
"Let's say a company needs an intelligent system for selling traffic that would allow better decision-making by scoring users based on their conversion progress. The company's marketing team would be the main stakeholder, wanting to profit from low-score users. Meanwhile, a machine learning team wants to get as much data as possible by using many complex techniques. But the company's product and behavior intelligence teams don't need so much data and aren't ready for high computing costs.

When there are too many contradicting priorities, a consensus is in order. For example, the ML system design team should focus on the data with the most significant influence on the model or avoid complex algorithms.

Marketing team

ARPU lower than resale price

ML team


Product team


BI team

Lean DBMS management

  • Inaccurate results
    Faulty algorithm calculations can affect work efficiency or customer satisfaction. For example, intelligent algorithms on platforms such as Netflix are responsible for selecting and representing film recommendations based on users' preferences and behavior patterns. Users won't bother renewing their subscriptions if such algorithms keep recommending the wrong movies. So, before working on a machine learning systems design project, teams need to acknowledge the potential risks of inaccurate predictions and see whether (and to what degree) these risks are acceptable. 
To address these issues, we always go through a machine learning design interview, which allows investors and stakeholders to evaluate their priorities, align objectives, and define the outcomes they want to see.”
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What is the ML system design interview structure?

A successful ML system design interview stems from the values and needs of stakeholders. It’s a deep look into the current capabilities of the enterprise and exploration of how a machine learning system can amplify them. Accordingly, the key to accurately identifying objectives and mapping a seamless journey is transparency, clarity, and a detailed understanding of core operations. 

For instance, investors can start by answering the following questions:

How urgent is our problem? Who or what defines its urgency?

Assess the urgency of the ongoing problem and look over the factors that dictate this urgency.

Why do we need an ML system to solve this problem?

Outline the tasks and goals you want to cover and decide whether these tasks are worth investing in ML development.

What is our definition of success?

What is the endgame of an ML design project? Be clear about the expected results and how you would measure these results (KPI, revenue, time optimization, etc.)

What are thepotential risks? How can we avoid or minimize them?

Visualize the worst case scenario and potential losses. Think through the ways to prevent them and extra steps you can take.

The interview structure may vary depending on the organization’s industry, pain points, operations, and needs. However, the ability to communicate, exchange perspectives, and awareness of limitations allows to dodge many pitfalls that may emerge during the ML system design process.

Data engineering

Data is the key driver of ML system design. After goal planning and constraints visualization, understanding of the data volumes needed and approaches to data gathering and storage become other factors impacting the outcome.

Any machine learning system is extremely data hungry and data-sensitive. Give it too little data, and it comes out under-fitted. Give it too much data — and it ends up overfitted. Give it the wrong data — and it doesn't perform as expected. Yet, collecting data is only half of the battle. Before it can be used, raw data must be cleaned and transformed into an understandable format.

Due to such complexities, data engineering can take around 45% of the data scientists' time. Otherwise, the project can get compromised by numerous issues.

  • Lack of proper data
    It may seem that all the necessary data is available–in documents, papers, or transferred to a digital format. However, in the middle of the testing process, the model ended up missing a large fraction of data — because it wasn't found in traditional data sources.

  • Data storage and collection
    Proper data gathering means experts must be familiar with several methods. Not all information can be obtained with just one method or from just one source. It takes a thorough scan across departments and all available data storage platforms to ensure no essential bits are missing. Therefore, teams in charge of ML system design need to rethink their approach to data storage and structuring. 

  • Missing values
    When compiling data in a dataset, it's not uncommon to see empty cells, NULL values, or question marks. Whenever it happens, missing values should be removed or substituted after a manual search through all data sources.

  • Data noise
    Data is rarely neat and organized. It's messy, noisy, and biased in ways unknown to all stakeholders and decision-makers. Using data from sources lacking data validation features also leads to multiple outliers and anomalies.

  • Inconsistent values
    Such a problem often occurs when combining data from various sources, providing different variants in variables (for example, one system uses the NEW YORK value, while another uses NY). That's where data may get noisy, so to avoid this pitfall, participants must find all variations and standardize them correctly.

  • Data compliance issues
    Not all data can be used freely — some bits of information gleaned from unstructured documents or other data goldmines may be valuable for the model or vital for the company clients’ NDA. So, before data scientists can proceed with modeling, a CIO must explore the data they will be using and ensure it doesn’t violate safety guidelines, customers’ privacy and follows data compliance regulations.
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After dealing with data, modeling is a relatively easier and less time-consuming step. After visualizing and processing data, data scientists can finally use it for building a model and generating predictions. However, modeling doesn't come without its pitfalls.

  • Interpretability
    A successful model must have a transparent and predictable logic that human users can track and comprehend. When users struggle with understanding the ML model's approach to making decisions or predictions, the end product will be of little value to them. Due to this, modeling requires a perspective from all the departments using the potential ML system.

  • Scalability
    Deciding that scalability can be saved for later is one of the biggest mistakes one can make at this stage. Such an approach may lead to the need to develop a new ML system — cue, more expenses, and fewer chances of adequate ROI. To be scalable, a model needs robust infrastructure, capacity for integration, and customized tech stack.

  • Maintenance
    ML models remain sensitive to the data they receive and/or are based on. A seemingly minor change, like a software update or a shift in customer behavior, may lead to a drift of concept (change in the relationship between input and output data), reducing the model's accuracy. Data scientists must maintain their ML models, running tests and updating them following software updates and validating an ML model before deployment.

Monitoring and continuous learning

So, an ML system is designed, modeled, and finally deployed. However, the work is far from over. Deploying an ML design project is costly and resource-heavy, so it’s important to ensure that not a single cent or minute goes to waste.

To keep the ML pilot operational and functional while delivering tangible outcomes, teams need to monitor it closely. In particular, they should be scanning it for the following disruptive processes:

  • Data drifts
    Data is never static. It's dynamic and constantly shifting, so data scientists must monitor all input features, so they can locate input data changes and respond to them promptly.

  • Data inconsistencies
    Even if the data obtaining and preparation step went smoothly, it doesn't protect the data gathering pipelines from becoming infested with integrity issues (e.g., outdated or missing data points). Regularly scanning input data for missing values allows keeping data structure complete and functional.

  • Concept drift
    Concept drifts may be sudden or gradual, triggered by external factors (social crisis, wars, political instability) or internal ones (fluctuation in the evaluation metrics). It's vital to perform regular correlation studies to maintain productive performance of a machine learning solution.

To keep the deployed ML system safe from drifts and inconsistencies, data scientists and stakeholders must regularly invest their time and resources in continuous learning. Continuous learning is the fuel that keeps a ML model going. It involves constantly feeding large volumes of new data to the model, re-educating it, so it could keep up with the shifts in its environment (new customer behavior patterns, workflow adjustments, new target audience segments or internal company processes).

Since ML projects operate similarly to machines (requiring new parts, testing and checkups), continuous learning is vital for securing the project’s resilience.

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Reporting (BI)

Reporting is not exactly the last stage of ML system development — it's the stage where a company can start using their deployed product, gleaning business intelligence insights, visualizing the received data, and gain a deeper look into customer behavior and other factors impacting business efficiency.

The only pitfalls that can compromise reporting are the ones not avoided during previous steps (poor data quality, lack of continuous learning, conflicting business objectives, etc.).

However, with all these steps performed correctly, developers and stakeholders get more than an efficient ML project — they gain a goldmine of data to enrich their understanding of their target audience, driving comprehensive analytical insights and predictions.

How to build machine learning systems that work?

Despite its complexities and hidden pitfalls, ML/AI has considerably grown in popularity, reaching 52% spending momentum across various sectors. Evaluated to reach $45 billion in 2032, the current M/AI solutions market is still seeing high demand among enterprises seeking competitive advantage and service improvement. 

These numbers signify that machine learning technology has evolved beyond a futuristic concept—it's already a part of everyday routine and workflow. Therefore, to enable new successful business models and approaches to operations, enterprises will need to invest in ML system design.

What are the steps in designing a machine learning system?

Given the abundance of data, machine learning system design is unlikely to become easier. How can enterprises prepare for a much-needed upgrade and avoid the most common development issues?

  • Precise goal identification
    Ultimately, machine learning models are tools—and every tool has limitations. To maximize the value of an ML system design project, stakeholders should be well aware of the pain points they want to address and the value they plan to glean from using the technology.
What stands between machine learning systems design and great results is wrong expectations. AI/ML is expected to do almost everything, which isn't accurate. A machine learning model can be trained to perform a wide range of tasks. Accordingly, it is as efficient as it was trained to be. So, to be efficient in your ML design, you need to know what you'll need it for. Only then will you be able to benefit from the technology.
  • Enabling synergy throughout the ML system design process
    Understanding machine learning system design steps and ML design patterns is a major part of a successful project. By knowing how they get started and what should be done at the finish line, stakeholders can be more confident about navigating their roadmap and evaluating results. On the teams' side, it's important to keep stakeholders updated on their journey, communicating potential outcomes, expenses, and returns. 

Key components to ML design synergy


Communicate your priorities to the rest of the group, be open to their suggestions, and prepare to reach a compromise.


Stay in touch with your development team, get them involved at the problem statement level, and welcome them to share their insights and experience with your industry. Be ready to cooperate on all ML system design changes.


Keep your data organized, structured, consistent and accessible. Make sure that all your data sources come with strong data validation controls so that no bad data ends up in your data-gathering pipelines.

  • Engaging vetted experts from the first step
    Since the lack of necessary skills and professionals is among the most common factors hindering AI/ML adoption, involving experienced data scientists and ML engineers is imperative. For that reason, enterprises are encouraged to cooperate with technology partners that can train an ML model and then seamlessly integrate it into their ecosystem.
Bringing an external perspective and domain knowledge to ML system design can make a powerful difference. By combining important external enterprise insights with the experience of teams with  multiple successful projects, you end up with a model tailored to your business needs, which complies with all the best practices.

If you want to reinforce your operations with machine precision, make the most out of your data, and be aware of every potential challenge, let’s chat

Having successfully upgraded numerous Fortune 500 clients with robust ML cores, we’ll help you make your ML design project a reality. Working in synergy with your teams, from project scoping to reporting, we'll enable a flawless transformation, avoiding pitfalls and focusing on impact.

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