Top challenges in the MLOps process and how to overcome them
- Edwin Kuss
- 7 min
MLOps, short for Machine Learning Operations, manages developing, deploying, monitoring, and preserving machine learning (ML) models in operational settings. It aims to bridge the gap between data science and IT operations by applying principles and practices inspired by DevOps to ML workflows. MLOps integrates tools and processes to streamline data preparation, model training, testing, validation, deployment, and monitoring while emphasizing continuous iteration and improvement to ensure that ML models are reliable, scalable, secure, and cost-efficient.
#F58635In this article, there will be a discussion on the significance of MLOps, delve into the primary challenges associated with its implementation—including issues related to data, models, infrastructure, and organizational processes—and discuss potential solutions to overcome these hurdles, enabling organizations to harness the power of MLOps effectively.
Table of contents
The importance of MLOps in scaling Machine Learning models
MLOps plays a crucial role in maximizing the potential of machine learning (ML) models by ensuring they are effectively deployed and managed across various stages of their lifecycle.
Reliability
MLOps ensures that ML models are reliable and deliver consistent, accurate results over time, which is essential for fraud detection and predictive maintenance applications.
Scalability
By enabling models to scale efficiently, MLOps allows ML systems to handle large data volumes and high user demand, making it essential for real-time decision-making and processing of high-velocity data streams.
Security
With a focus on safeguarding ML models, MLOps helps protect against security threats like data breaches, cyber-attacks, and unauthorized access, ensuring the safety of applications dealing with sensitive or confidential information.
Overall, MLOps empowers businesses to fully utilize their ML models through a well-structured approach to managing the entire machine learning lifecycle—from initial development to deployment and ongoing maintenance.
Kiroframe, as an end-to-end MLOps platform, brings these principles into practice by helping engineering teams scale and manage ML models with full transparency and control. It combines experiment tracking, dataset management, artifact versioning, profiling, and leaderboards into one workflow, ensuring that models can be deployed reliably, monitored for drift and performance, and reproduced across teams. With Kiroframe, businesses not only scale their ML operations but also reduce risks, improve collaboration, and accelerate the path from prototype to production.
How the MLOps process works: A complete guide
Understanding the MLOps process is key to successfully implementing machine learning models. There’s no single, universally agreed-upon approach to how many stages the MLOps process should consist of—some divide it into three phases, while others break it down into as many as nine. For clarity and focus, we will outline the MLOps process in five stages, with one ongoing phase of continuous improvement:
Data collection and preprocessing for Machine Learning
The first step in the MLOps process is to collect and preprocess the data to ensure it is high quality, adequate in quantity, and suitable for model training. This stage is essential for providing reliable input for ML algorithms to perform effectively.
Kiroframe supports this stage by tracking dataset versions, capturing metadata such as schema and data distribution, and flagging anomalies or drift before they affect training.
Training and evaluating ML models for optimal performance
In this phase, data scientists train machine learning models using the prepared data. The models are then evaluated to assess their accuracy, performance, and robustness and determine their readiness for deployment.
Experiment tracking and profiling in Kiroframe automatically log parameters, metrics, and artifacts for each run, making it easy to compare experiments side by side and ensure models are reproducible and optimized.
Seamless model deployment for scalable AI solutions
Once trained and evaluated, models are deployed into production environments where they can provide real-time predictions or analytics, contributing directly to business operations.
By integrating artifact management and shared environments, Kiroframe helps streamline deployment pipelines, reducing conflicts between teams and ensuring models move smoothly from development to production.
Real-time model monitoring and lifecycle management
Ongoing monitoring is crucial to ensure deployed models continue functioning as expected. This phase involves tracking model performance, detecting data drift, model decay, and performance degradation, and making necessary adjustments.
Kiroframe’s monitoring features tie metrics back to datasets and experiments, allowing teams to trace the source of issues, detect silent failures, and maintain compliance with reproducible records.
Continuous optimization and automation in MLOps
The final stage focuses on continuously improving the ML models by iterating on data, models, and infrastructure. This process ensures that models stay relevant and effective as business needs and data evolve.
Through leaderboards, metadata tracking, and automation features, Kiroframe enables teams to continuously refine their models, track progress over time, and align improvements with business goals.
To successfully implement these stages, MLOps relies on various tools and technologies, including version control systems, continuous integration and deployment (CI/CD), containerization, orchestration tools, and monitoring platforms. The MLOps process also fosters collaboration among specific data scientists, IT operations, and experienced business stakeholders to ensure that ML models align with overall business objectives and meet the needs of all parties involved.
Key challenges in the MLOps process
The MLOps process is highly complex and involves multiple challenges that can affect the overall success of deploying and maintaining machine learning models. These challenges span data, models, infrastructure, and organizational processes. Below, we explore organizations’ key obstacles in the MLOps process.
Challenges related to models
Various factors can impact the quality and performance of ML models. First, ensure that the selected model aligns well with the specific problem and can learn from the data. Transparency and interpretability of models are also essential, especially in mission-critical applications. Another common issue is model overfitting, which occurs when models fail to generalize to new data due to insufficient or noisy data. Additionally, model drift is another challenge—ML models can become ineffective or outdated over time as data and environments evolve.
Challenges related to infrastructure
Many overlook the importance of infrastructure when implementing MLOps, but stable and scalable infrastructure is critical for training, testing, and deploying ML models. As ML models grow in complexity, infrastructure must also be scalable to meet their increasing demands. Proper hardware and software resources are required to run models efficiently, so resource management is vital. Moreover, teams should closely monitor infrastructure to prevent system failures, security breaches, or resource shortages. Finally, proper deployment and integration with other systems ensure ML models deliver business value.
Challenges related to people and processes
Successful MLOps implementation relies on effective collaboration across multiple teams, including data scientists, IT operations, business analysts, and other stakeholders. The MLOps team serves as a bridge to facilitate communication and collaboration between these groups. Additionally, establishing consistent processes and workflows for model development, deployment, governance, and management is essential for streamlining the MLOps process and ensuring that models align with business objectives.
Challenges related to data
Data challenges are a fundamental aspect of the MLOps process, as the quality and availability of data directly influence model accuracy and performance. Poor-quality or biased data can lead to ineffective models, so MLOps teams must focus on keeping data clean, relevant, and sufficient quantity. Privacy and security concerns pose critical data-related challenges, but organizations can address them through robust security protocols, access controls, and encryption mechanisms that protect sensitive data.
By addressing these challenges and refining the MLOps process, organizations can ensure the successful development, deployment, and management of ML models that meet business goals and drive innovation.
Conclusion: Addressing MLOps challenges with effective solutions
MLOps teams face persistent challenges in managing data, models, infrastructure, and collaboration workflows. These hurdles often include ensuring reproducibility, maintaining model performance in production, detecting data drift, and coordinating across distributed engineering teams. To overcome them, organizations rely on a mix of tools for dataset management, model versioning, experiment tracking, monitoring, and automation. However, stitching together separate tools can increase complexity and slow down adoption.
Kiroframe offers a unified MLOps platform designed for ML/AI engineers and data teams. It brings together experiment tracking, dataset versioning, artifact management, profiling, leaderboards, and monitoring into one streamlined workflow. By consolidating these capabilities, Kiroframe helps teams reduce friction, scale machine learning operations with confidence, and ensure that models remain reproducible, transparent, and production-ready.