What Is the Best Platform for MLOps? Your Guide to MLOps Software

With support from machine learning operations (MLOps) software, DevOps engineers, IT professionals, and data scientists can collaborate more effectively to develop, deploy, and manage machine learning (ML) models. These platforms can automate vital steps in the ML lifecycle, such as versioning, experiment tracking, and monitoring, helping you bring models to production faster and more reliably.

Discover how to determine the best MLOps platform to streamline your workflow and reliably bring models into production.

What is MLOps?

MLOps stands for machine learning operations, and by adopting MLOps practices, your team can streamline core stages of the ML pipeline. Techniques like automated data versioning and continuous integration/continuous deployment (CI/CD)—where code changes are automatically tested, built, and deployed—create a collaborative system that blends development with operations. This results in shorter development cycles and lower operational costs, making it easier to deliver production-ready ML models.

Rooted in DevOps, MLOps emphasizes many of the same foundational principles:

• Automation and continuity: Using CI/CD, automated pipelines retrain models as data evolves, test performance, and implement updates efficiently. This system aids data scientists in exploring and implementing new ideas in model architecture, feature engineering, and hyperparameters. It also provides swift feedback on each pipeline step's outcome.

• Collaboration: MLOps unifies data scientists, engineers, and operations teams. This approach drives clearer communication, allowing teams to work together on models, data, and code rather than handing off tasks between departments.

• Versioning: Beyond just code, MLOps extends version control to include data and model artifacts, helping teams document changes and restore previous versions when needed. With proper versioning, models stay reproducible and reliable over time.

• Monitoring: Your team can employ MLOps to track prediction accuracy, data drift, and system performance, maintaining operations and delivering consistent results as conditions change.

• Governance: MLOps provides tools for model explainability and compliance tracking. These capabilities improve AI transparency, enabling organizations to document model behavior and evaluate fairness, all while adhering to regulations and ethical standards.

What are MLOps tools?

MLOps tools are software that automate various aspects of managing the ML model lifecycle. To introduce MLOps into your ML workflow, consider adopting a platform like Amazon SageMaker AI, Metaflow, or MLflow—all three are popular choices within a diverse collection of specialized tools. Some platforms offer end-to-end solutions, while others address specific aspects of the model's lifecycle, both technical and organizational.

For example, you might rely on AWS SageMaker Ground Truth for data set labeling, while Comet ML and Aimstack can aid with experiment tracking and metadata management. Depending on your project's needs, your MLOps stack could include tools for:

• Data storage and versioning: Pachyderm, LakeFS

• Feature stores: Feast, Featureform

• Hyperparameter optimization: Optuna, SigOpt

• Model quality testing: Deepchecks, Kolena

• Deployment and serving: Hugging Face Inference Endpoints, BentoML

• Workflow orchestration: Flyte, Mage AI

• Model monitoring: Evidently AI, Fiddler AI

Platforms such as Databricks, Kubeflow, and Valohai feature comprehensive MLOps capabilities. 

You'll find both open-source and proprietary MLOps tools. Open-source solutions often provide greater flexibility and community-driven support, while closed-source options may prioritize stability, security features, and dedicated vendor assistance.

How to choose the best MLOps platform for your needs

As machine learning technologies become more complex and widely adopted, the demand for scalable, automated infrastructure is growing. The global ML market is projected to exceed $105 billion in 2025, with estimates suggesting it could reach $568 billion by 2031, and around $167.7 billion in the US alone [1,2]. This growth has not only created new career opportunities for ML engineers and researchers, but it has also intensified the need for tools that support long-term model management. By incorporating MLOps software into your team’s toolkit, you can ensure the development of more efficient ML models.

When selecting an MLOps platform, ensure to tailor the decision to your team's technology stack, expertise, and plans for scaling. A small team working on a single-model application might require a different infrastructure than a larger organization managing dozens of production pipelines. There's no universal solution—clearly defining your needs is beneficial before investing in tools that will best support your workflow.

Machine learning reaches beyond traditional software development with opportunities to manage trained models, adapt to evolving data patterns, and ensure reproducibility at scale. These variables make it important to choose platforms that align closely with your development process.

Some key factors to consider include:

• Tech stack compatibility: Select platforms that integrate with your existing languages, infrastructure, and preferred frameworks. Amazon SageMaker AI aligns naturally with AWS environments, while MLflow can integrate well with Python-centric workflows.

• Team expertise: Match your platform with your organization's technical strengths. Kubeflow offers powerful customization options but may require Kubernetes knowledge, whereas Metaflow is accessible to data science teams exploring MLOps for the first time.

• Cost and maintenance: Open-source solutions like MLflow eliminate licensing fees but may require more internal maintenance over time. Commercial options such as Azure ML include enterprise features and dedicated support at a higher initial cost. 

• Community: Popular open-source platforms like MLflow have active communities that offer user insights, best practices, and troubleshooting assistance. These resources provide support for implementing similar solutions. 

• Vendor support and reliability: For proprietary platforms, responsive support services and regular updates can simplify implementation. Clear service-level agreements (SLAs) help ensure your MLOps infrastructure remains effective as your needs grow.

MLOps platforms

Selecting an MLOps platform depends on how well it fits your team’s workflow, infrastructure, and other unique preferences. While many tools offer overlapping features, such as pipeline automation or model tracking, they vary in how they're configured and who they're built for. Clarifying your needs can help you choose a platform that supports your long-term goals. Explore some popular MLOps platforms below.

Amazon SageMaker

Amazon SageMaker is an end-to-end machine learning platform that integrates analytics and AI workflows in a unified environment. It enables development and data science teams to build, train, and deploy models without having pre-existing infrastructure management, providing comprehensive tools for the entire ML lifecycle. SageMaker delivers:

• Access to data across S3 storage, Redshift warehouses, and external sources

• Tools for model creation, generative AI development, and Structured Query Language (SQL)-based analytics

• Enterprise-grade governance controls throughout the data and AI lifecycle

• Resources for toxicity detection, data classification, and responsible AI

• Apache Iceberg-compatible architecture for efficient analytics data management

Kubeflow

Kubeflow provides a comprehensive Kubernetes-based platform for deploying ML workflows with portability and scalability. By leveraging Kubernetes, Kubeflow creates a consistent environment for ML development that works across diverse computing infrastructures while supporting popular open-source tools. Kubeflow’s key capabilities include:

• Flexible implementation across on-premises, cloud, and hybrid environments

• Tracking of experiments, code versions, and model parameters

• Extensible architecture integrating with other ML services and cloud-based platforms

• Components for notebooks, hyperparameter tuning, and model serving

• Support for distributed model training with TensorFlow, PyTorch, and JAX

Metaflow

Metaflow is a user-friendly Python library that simplifies the development of data-intensive ML, AI, and data science applications from prototype to production. Originally developed at Netflix, it caters to computationally intensive projects with complex components. Metaflow offers its users:

• Native integration with AWS Batch, Kubernetes, Apache Airflow, and other systems

• Production-tested stability supporting thousands of workflows in large organizations

• Automated versioning of flows, artifacts, and experiments

• Scalable computation infrastructure leveraging cloud and Kubernetes resources

• Patterns for accessing data from lakes and data warehouses.

MLflow

MLflow is an open-source platform that addresses common ML development challenges through four modular components: Tracking, Projects, Models, and Registry. It's suitable for organizations that need standardized workflows across teams and allows you to adopt each component independently. MLflow’s core features include:

• Systematic logging of parameters, metrics, code versions, and output artifacts

• Vendor-neutrality for deploying models across various cloud environments and platforms

• Standardized APIs for accessing and deploying large language models (LLMs)

• Library-agnostic design to transform models into reproducible black boxes

• Centralized tracking to log all experiments and promote traceability

Azure ML

Azure Machine Learning is Microsoft’s cloud-based platform for expediting and managing ML workflows. It integrates seamlessly with other Azure services, permitting data scientists, ML engineers, and development teams to collaborate while leveraging common enterprise security features. Azure ML provides:

• Collaborative infrastructure for shared notebooks, compute resources, and data sets

• Multiple interface options, including Python SDK, CLI, and REST APIs

• Comprehensive tools for model discovery, prompt flow, and generative AI creation

• Enterprise security via Azure Virtual Networks, Key Vault, and Container Registry

• Direct integration with Synapse Analytics, SQL Database, and other Azure services

• Interpretability features and compliance documentation to support responsible AI

Databricks

Databricks offers a data lakehouse platform that uses AI to understand your data while managing infrastructure based on your business needs. It combines data warehouses and lakes to simplify enterprise data solutions, enabling teams to work with consistent data across the organization. Databricks features:

• Natural language tools for data search and code assistance

• Managed open-source integration with Delta Lake, MLflow, and Apache Spark

• ETL capabilities with Auto Loader for efficient data ingestion

• Support for integrating and fine-tuning LLMs

• Unified data governance through Unity Catalog

Careers that use MLOps

As MLOps adoption grows across the ML field, new professional opportunities are emerging. These roles use MLOps to support collaboration and build efficient ML systems:

Data scientist 

Average annual salary in the US (Glassdoor): $113,815 [3]

A data scientist builds and trains models using statistical analysis, ML algorithms, and exploratory data techniques.

ML engineer

Average annual salary in the US (Glassdoor): $119,514 [4]

An ML engineer combines data science and software engineering to design production-ready ML models, optimizes ML models for deployment, and builds MLOps infrastructure for testing.

ML architect 

Average annual salary in the US (Glassdoor): $135,592 [5]

ML architects guide the creation of ML architecture, crafting blueprints and strategies to be supported by MLOps in production. 

QA developer 

Average annual salary in the US (Glassdoor): $79,824 [6]

Quality assurance (QA) developers test ML systems across the lifecycle, identifying defects early and collaborating with developers to improve performance.

Learn more about MLOps on Coursera

Using MLOps platforms such as Amazon SageMaker and MLflow, you can streamline key stages of the machine learning lifecycle. These tools help automate repetitive tasks, improve collaboration across your team, and make it easier to deploy reliable models at scale.

If you want to further explore the field of machine learning, consider taking an online course on Coursera, which can give you the opportunity to better understand machine learning concepts and processes. For example, the IBM Machine Learning Professional Certificate offers practical skills and industry-relevant training to help you launch your career in the field of machine learning. For a broader perspective on artificial intelligence technologies, explore the IBM AI Engineering Professional Certificate, which covers topics such as machine learning, neural networks, and deep learning.