Organizations must streamline traditional software development and machine learning (ML) workflows as the demand for faster innovation grows. This has led to the emergence of two specialized disciplines: Platform Engineering and MLOps.
Both aim to simplify complex processes, but they focus on different aspects. Platform Engineering provides developers with tools to abstract infrastructure complexities, while MLOps focuses on the unique challenges of managing ML lifecycles. Though distinct, their overlapping principles often make them complementary in practice.
This article compares Platform Engineering and MLOps by examining:
- their similarities and differences
- how Platform Engineering and MLOps evolved
- shared challenges and the modern solution for improved AI/ML workflows.
How do Platform Engineering and MLOps compare?
The simplest way to compare Platform Engineering and MLOps is to look at their definitions side-by-side:
What is Platform Engineering?
Platform Engineering builds and maintains internal platforms that abstract infrastructure complexities like Kubernetes, cloud environments, and CI/CD pipelines. These platforms empower developers to deploy, manage, and monitor applications with minimal operational overhead.
Rather than handling infrastructure directly, developers use self-service tools provided by the platform. This approach promotes consistency and reduces the burden of managing distributed architectures, enabling teams to focus on building software.
As AI becomes more prevalent, organizations increasingly leverage Platform Engineering to support traditional software development and ML operations, bridging gaps between infrastructure and AI workflows.
What is MLOps?
MLOps is a practice that streamlines the development and deployment of machine learning models. Borrowing from DevOps, it automates key processes like data preprocessing, model training, versioning, and deployment.
Unlike traditional software, ML models depend on dynamic data and require constant monitoring to address issues like model drift. MLOps integrates tools and workflows to manage these unique challenges, enabling reproducibility, scalability, and collaboration between data scientists, engineers, and operations teams.
In essence, MLOps transforms experimental ML models into reliable, production-grade solutions, ensuring they deliver value efficiently and consistently.
Differences between Platform Engineering and MLOps
Although Platform Engineering and MLOps share some principles, they focus on distinct aspects of software and machine learning workflows, as highlighted in this table:
Platform Engineering focuses on building self-service platforms that abstract infrastructure complexities such as cloud environments, Kubernetes, and CI/CD pipelines. It is geared toward enabling developers to efficiently deploy, manage, and monitor applications without handling infrastructure directly.
MLOps, on the other hand, addresses the unique challenges of the machine learning lifecycle. It emphasizes automating tasks such as data preprocessing, model training, deployment, monitoring, and retraining to combat issues like model drift and ensure reproducibility.
While Platform Engineering is infrastructure-centric, providing the foundational tools and environments for software development, MLOps is model-centric, targeting the iterative nature of ML workflows. The two disciplines require different skill sets—Platform Engineering involves expertise in infrastructure design, while MLOps combines knowledge of machine learning, data engineering, and operational workflows.
These roles often remain distinct in larger organizations, reflecting their specialized focus. However, smaller or agile teams may overlap, requiring professionals to bridge infrastructure and ML operational expertise. This separation of focus underscores each discipline’s complementary but different roles in advancing modern technological capabilities.
Key similarities between Platform Engineering and MLOps
While Platform Engineering and MLOps address different areas, they share foundational principles and often work together to streamline modern development workflows. Key similarities include:
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Focus on automation: Both disciplines emphasize automating repetitive tasks to improve efficiency and reduce errors. Platform Engineering automates infrastructure provisioning, while MLOps automates model training, deployment, and monitoring.
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Self-service enablement: Both create self-service platforms to empower teams. Platform Engineering provides tools for developers to manage infrastructure, while MLOps enables data scientists to handle ML workflows independently.
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Collaboration across teams: Both aim to bridge silos and encourage collaboration. Platform Engineering connects developers with operations, and MLOps integrates data scientists, engineers, and operations teams.
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Shared use of DevOps principles: Both adapt DevOps concepts like CI/CD pipelines, versioning, and monitoring to their respective domains, ensuring streamlined processes and consistent results.
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Support for scalability: Both disciplines focus on scalability to enable systems and workflows to handle increasing demands, whether for applications or ML models.
These shared goals complement Platform Engineering and MLOps, driving innovation and efficiency while reducing operational burdens across teams.
Challenges with Platform Engineering and MLOps
Since Platform Engineering and MLOps share similarities in their principles and goals, they also face overlapping challenges that can hinder collaboration and efficiency. These challenges include:
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Infrastructure complexity: Managing Kubernetes, cloud-native tools, and multi-cloud environments requires significant effort and expertise.
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Inconsistent environments: Variability in development, staging, and production environments leads to unreliable deployment pipelines.
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Model versioning: Tracking and packaging models, datasets, and metadata for reproducibility across environments is challenging.
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Model drift and monitoring: Detecting and addressing performance degradation over time is resource-intensive.
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Silos between teams: Lack of integration between platform engineers and MLOps teams results in fragmented workflows.
A modern solution should integrate platform engineering and MLOps workflows into a unified framework. This approach should include:
- Tools for consistent packaging and versioning of models and datasets.
- Automated pipelines for deployment, monitoring, and retraining.
- Centralized platforms to bridge gaps between platform engineering and MLOps teams.
- Security features to manage sensitive data and maintain compliance.
How Jozu solves these problems
Jozu provides the tools and frameworks to address these challenges, empowering organizations to streamline their workflows. Its innovative architecture includes two key components:
KitOps:
- A tool for organizing and managing models, datasets, code, and configurations.
- Standardized versioning and packaging capabilities.
- Enables reproducible deployments across diverse environments.
Jozu Hub:
- An OCI 1.1-compliant registry designed for enterprise AI.
- Offers deployment as SaaS, on-premises, or private installations.
- Prioritizes security, privacy, and control for regulated industries.
- Integrates with enterprise registries for a seamless workflow.
Jozu eliminates silos between platform engineering and MLOps, promotes collaboration, and accelerates innovation by creating a shared platform that simplifies complex processes.
Conclusion
While both Platform Engineering and MLOps support efficient software deployment, MLOps is tailored to the specific needs of ML, addressing unique challenges like data drift and model decay. Platform engineering, however, is broader and optimizes the development lifecycle at a high level across various applications, often serving as a backbone that supports MLOps within an organization.
Jozu offers tools like KitOps for model packaging and Jozu Hub for secure AI registries, providing a unified approach to streamline processes and drive innovation. Get started with KitOps or join the conversation on Discord.
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