Cost Factors and Challenges in Machine Learning Projects: A Comprehensive Guide

Read Time 6 mins | Written by: Yuan Zhao | Ryo Hang

Embarking on a machine learning (ML) project brings forth numerous cost factors and challenges. From data availability to problem complexity, each aspect influences the project's budget and difficulty. In this section, we delve into the key challenges, including data gathering issues, accuracy demands, and problem complexity. Additionally, we explore the role of MLOps frameworks in optimizing deployment processes and reducing costs.


  • Cost Factors and Challenges in Machine Learning Projects: Major cost factors in ML projects include data availability, accuracy requirements, and problem complexity. Issues in data gathering and labeling, high accuracy demands, and the nature of the problem (e.g., output complexity, reliability, and generalization) have an impact on the project’s budget and difficulty. In addressing these challenges, introducing MLOps as a solution to streamline model deployment and testing processes is crucial. MLOps frameworks can automate and optimize these steps, leading to more efficient and error-free deployments. Furthermore, MLOps significantly reduces human labor and error-related costs. Automation in machine learning operations, such as automated deployments, can greatly reduce the resources and time required, resulting in cost savings.

  • ML Feasibility Assessment: Before commencing an ML project, evaluate the necessity of ML. Focus on defining success criteria with stakeholders, considering ethical implications, reviewing related literature, gathering a labeled benchmark dataset, and developing an MVP with manual rule sets. Ensure regular re-evaluation of the requirement for ML.

  • Understanding and Employing Baselines in Machine Learning Product Development: Baselines are pivotal in defining performance expectations for ML product development. They aid in determining subsequent steps based on the model's performance relative to the baseline. Baselines can be sourced externally or self-created, spanning from basic averages to comprehensive human performance data. The selection of an appropriate baseline relies on the specific task and desired quality.


Four Stages of A machine learning project 

1. Planning and Project Setup: This stage involves deciding the problem to be solved, setting goals, arranging resources, and considering privacy issues.

2. Data Collection and Labeling: Here, you gather the necessary data to train your machine learning model. Depending on the data sources, you might augment your data to provide 'ground truth'. If acquiring and labeling data becomes too challenging, you might need to revisit your plans.

3. Model Training and Debugging: Here, you'll create your basic models, test them, implement the top methods used in your field, and refine your models for improvement. At this stage, you might recognize the need for more data or find that your labeled data isn't reliable, requiring a return to phase 2. Should the task seem overly challenging, it might be necessary to reevaluate your initial plans. 

4. Model Deployment and Testing:  Lastly, in this phase you'll evaluate the model in a controlled setting, establish test processes to identify any regressions, and deploy the model. During model deployment and testing, there can be friction when machine learning models, built with different languages and platforms, are implemented in regular software environments. Deployment issues might create a barrier to scaling and potential revenue and cost savings. MLOps can resolve this by streamlining the integration of these models into production. Regardless of the model's original environment or language, MLOps facilitates consistent API access across teams. It eases the evaluation and adjustment phase of models, making the entire process more efficient and effective. If the model underperforms at this stage, it prompts an improvement phase (phase 3). An issue between training data and real-world data might necessitate gathering and labeling more data, returning you to phase 2. If the selected metrics or real-world performance doesn't meet expectations, it's necessary to review your project's metrics and requirements (back to phase 1).

Apart from these project-specific tasks, any machine learning team must focus on hiring the right talents and setting up the necessary tools and infrastructure to efficiently build ML systems on a large scale. Understanding the best results in your field of application is also essential to comprehend the possibilities and determine the subsequent steps.


Figure-1: Lifecycle of a ML Project (source:


Three Cost Factors and Challenges in ML Projects

The three primary cost factors in Machine Learning (ML) projects, in order of importance, are data availability, accuracy requirements, and problem difficulty.

  1. Data Availability

Concerns about data availability include the difficulty in obtaining data, expenses involved in labeling data, the required amount of data, data stability, and data security requirements.

  1. Accuracy Requirement

Questions regarding accuracy requirements include the cost implications of incorrect predictions, the required frequency of system accuracy to be useful, and ethical implications. It's essential to note that the costs of ML projects tend to increase more than proportionally to the accuracy requirement.

  1. Problem Difficulty

Considerations about problem difficulty include how well the problem is defined, published work on similar problems, computing requirements, and whether a human can perform the task.

In general, both unsupervised learning and reinforcement learning remain challenging areas in ML, despite their potential in certain domains with ample data and computational power available.


Within supervised learning, 3 categories of challenging projects include:

  • Complex Output: These involve high-dimensional or ambiguous outputs, such as 3D reconstruction, video prediction, dialogue systems, and open-ended recommendation systems.
  • Reliability: These situations require high precision and resilience, for instance, systems that can safely fail in out-of-distribution scenarios or are robust to adversarial attacks.
  • Generalization: These involve out-of-distribution data or reasoning, planning, and causality in domains like self-driving vehicles or systems dealing with small data.

ML Feasibility Assessment

  • Do you really need ML?
  • Dedicate initial effort to establish success criteria with all stakeholders.
  • Reflect on the ethical implications of employing ML.
  • Conduct a comprehensive literature survey.
  • Aim to swiftly accumulate a labeled benchmark dataset.
  • Develop an MVP (minimum viable product) using manual rule sets.
  • Reassess: Does your project really need ML?

Understanding and Utilizing Baselines in Machine Learning Product Development

Utilizing baselines is essential to establish performance expectations. These baselines define the minimum performance expected from a model. Examples of baselines can include human task performance, the best-known models, or simple rules.

Baselines guide decisions for the next steps. For instance, if a model's performance closely matches or exceeds the baseline, it might be necessary to avoid overfitting. Conversely, if the model performs significantly worse than the baseline, improvements are needed to address its underperformance.

Baselines can be established from external sources or be internally created. A common, simple internal baseline is the average performance across a dataset.

Human performance is a robust baseline, particularly when a model is designed to automate or support human tasks. However, collecting good human performance data can be challenging. High-quality data, especially from experienced workers, can provide better baselines but is more difficult to obtain. The choice of baseline depends on the task at hand, and collecting data specifically where the model struggles can enhance the quality of the baseline.

As we've explored the intricate dynamics of machine learning project costs and challenges, a pivotal factor emerges: the efficiency of operations. This is where Machine Learning Operations (MLOps) play a crucial role. MLOps not only streamline model deployment and testing but significantly reduce operational costs through automation.


For a comprehensive understanding of how these challenges are addressed in real-world scenarios, particularly through the use of Machine Learning Operations (MLOps), see our blog 2 in this series: 'Navigating the Complex Terrain of Machine Learning Operations: A Dive into MLOps'. To delve even deeper into the specifics of MLOps, particularly focusing on the automation of CI/CD pipelines, don’t miss the subsequent article, 'Evolving Machine Learning Operations: Mastering CI/CD Pipeline Automation'. We will dive deeper into how MLOps can transform your project’s efficiency and cost-effectiveness, illustrating the transformative power of these advanced practices in real-world scenarios.



Yuan Zhao

Data Solution Architect

Ryo Hang

Solution Architect @ASCENDING