Machine learning model development generates an array of experiments as ML engineers tweak architectures, parameters, and datasets. Manually tracking these iterations becomes intractable as projects increase in complexity.
MLflow is a popular open source solution to manage the machine learning lifecycle and facilitate reproducibility. It consists of three main components:
It also provides a way to package data science code into projects and to define MLOps workflows. Recently, the MLflow developers have added capabilities around Large Language Models (LLMs): a dedicated LLM experiment tracker, experimental support for prompt engineering, and an experimental interface for connecting to LLMs provided by third parties like OpenAI.
MLflow excels at streamlining machine learning lifecycle management and simplifying experiment tracking. However, it lacks many features that data science teams seek, such as dataset versioning or user access management. Further, you need to deploy MLflow on your own infrastructure and rely on its community for support.
That’s where alternatives come in. These are typically available as SaaS – meaning you don’t have to worry about hosting and updates – and come with security and compliance capabilities required by many organizations. Further, their UI is often more advanced and centered around teams and collaboration.
In this article, we will first examine MLflow’s limitations in depth. We’ll provide a comprehensive overview based on insights from extensive user interviews, first-hand experience in various projects across industries, and in-depth research during vendor selection processes.
Main limitations of MLflow and reasons to explore alternatives
Every project and team has distinctive requirements for managing the machine learning lifecycle. While MLflow is a great tool and often the first option that comes to mind, it’s by no means the best tool for every scenario.
To break down why MLflow may not be a good fit for you, we’ll look at the most common challenges users encounter.
The concerns with MLflow often raised by users can be divided into the following categories:
- 1 Security and compliance
- 2 User, group, and access management
- 3 Lack of collaborative features
- 4 UI limitations
- 5 Lack of code and dataset versioning
Scalability and performance
Further, open source MLflow comes with all the drawbacks of a self-hosted tool not backed by a company:
- 1 Configuration and maintenance overhead
- 2 Integration and compatibility challenges
- 3 Lack of dedicated support
In the following, we’ll look at each category in detail.
Security and compliance
Many organizations – and, in turn, their ML teams – have strict security and compliance requirements. While open source MLflow offers resource-level permissions management and password-based authentication, by design, it is up to the user to configure more advanced access controls and ensure compliance adherence.
If you choose MLflow, it’s your responsibility to:
- Implement measures to ensure that specific resources, such as experiments and models, can only be accessed by authorized individuals.
- Ensure that sensitive data remains encrypted, safeguarding it from potential threats.
- Regularly conduct vulnerability assessments and mitigate potential risks.
Sure, this gives you the flexibility to design a security framework tailored to your specific requirements. However, this flexibility is a double-edged sword. It requires significant expertise, development effort, and vigilant oversight.
While some organizations might find the self-managed security approach challenging, others might appreciate its flexibility. When considering MLflow, it’s essential to weigh its adaptability against the comfort of the built-in, ready-to-use security features of other platforms.
Lack of user and group management
Hand in hand with the relative lack of security features comes a lack of user management. While it’s commonplace in many enterprise applications to be able to restrict access to files or information to a select group of users, open source MLflow does not support even coarse-grained permissions.
This lack is a serious limitation in the eyes of organizations that are used to systems like LDAP or the IAM capabilities of cloud platforms:
I would say that the main caveat we have with open-source MLflow is the lack of ubiquitous access control. Projects become accessible to all, forcing us to either write extensive infrastructure code or deploy separate MLflow instances for each team to ensure data isolation.— Senior MLOps Engineer, Digital Identity Verification Platform, UK
Lack of collaborative features
The ease of collaboration – or lack thereof – can make or break a machine learning project. This becomes even more evident when working across diverse teams. MLflow, while celebrated for its wide range of MLOps capabilities and availability as a high-quality open source software, leaves much to be desired.
For instance, collaboration tools allowing team members to seamlessly review projects, share data, or create detailed reports are noticeably absent in MLflow. Instead, a more manual process is required. To share your projects with collaborators, you’d need to create URL aliases for each experiment.
Kha Nguyen, a Senior Data Scientist at a leading retail and hospitality analytics service company, recalls his experience with MLflow:
There’s also the issue of creating a URL alias for [MLflow experiments]. Why do I have to do all this manually?— Kha Nguyen, Senior Data Scientist
This became a significant hurdle because Kha worked primarily as a solo data scientist, reporting to a non-technical manager who could not navigate MLflow himself.
User interface limitations
A poorly designed user interface can seriously hamper productivity and adoption, especially for less technical users or those new to a tool. Without a doubt, MLflow provides a clean and straightforward interface. But while MLflow’s UI is clean and functional, it is far less configurable or feature-rich than the UI some other platforms discussed in this article offer.
For some teams and use cases, the simplicity and restrictiveness of MLflow’s UI is a strength. If you only care about some standard metrics like accuracy or precision, the fairly basic plots in MLflow’s Tracking UI are more than sufficient.
Others might do most of their analysis outside of MLflow, retrieving the necessary data through MLflow’s API and importing it into other tools to create visualizations or dashboards.
Concerns about scalability and performance
For organizations looking to scale their ML projects and integrate machine learning models into their products, performance is paramount. When evaluating machine learning platforms, it is thus important to understand how they fare under increased loads.
When it comes to performance, the main areas of concern are model training, experiment tracking, and model serving.
MLflow, while renowned for its ease of use for individual users or smaller teams, reportedly faces challenges when tracking a large number of experiments or machine learning models. A common observation is that MLflow seems to not be as resource-efficient as some of its competitors.
Sometimes, MLflow is unreliable because I think it’s not optimized, as it consumes quite a lot of RAM and runs slow, too. The real challenge arose when we ran 100 experiments and 100 forecasts simultaneously, streaming data into MLflow. That’s when we experienced issues with MLflow’s responsiveness.— MLOps Engineer at a large retailer
MLflow supports distributed computing platforms like Apache Spark and Databricks for model training and provides integrations with distributed storage systems such as AWS S3 and DBFS. However, it’s up to the users to configure, tune, and maintain these systems.
When it comes to model serving, open source MLflow offers plenty of options. Aside from its built-in model server, through Seldon’s MLServer, MLflow integrates with Seldon Core and KServe. Further, MLflow ships with integrations for third-party model serving solutions, namely Microsoft’s AzureML, Amazon SageMaker, and Apache Spark. It also provides a Python interface for deploying to custom targets, enabling users to write their own deployment integration. This gives teams the flexibility to choose the optimal model serving solution for their use case, but it often comes with engineering and maintenance overhead or additional costs.
Configuration and maintenance overhead
As an open source tool, MLflow is free to download, and anyone can operate as many instances as they like without incurring license fees. There are also virtually no limits to its adaptability, allowing organizations to tailor the platform to their needs.
However, hosting an MLflow instance comes with costs for the infrastructure and maintenance. You need to configure and manage the servers and the underlying storage, watch out for and apply security patches, upgrade as new MLflow versions are released, and troubleshoot any issues.
The difficult part for us was making it work very quickly. We had to spend 50 engineering hours to set it up and make it work for us.— Principal ML Engineer, Software Development, USA
Setting up and deploying MLflow can be complex. It typically requires a virtual machine or Kubernetes cluster. You must also manage a backend store (like MySQL, SQLite, or PostgreSQL) and an artifact store (like S3, Azure Blob Storage, or GCS). Further, you’ll have to take care of backups.
What is quite important for us as a company is data security and knowing which data is saved on which servers. MLflow would, therefore, be perfect but require a lot of administration, which is why I would prefer a SaaS solution. It’s a hassle for me to experiment and maintain MLflow simultaneously.— VP of Engineering at a large enterprise
Additionally, MLflow only provides password-based authentication by default. Integrating it with authentication protocols like OAuth or LDAP or setting up role-based access control (RBAC) will inevitably add complexity.
While there are challenges to consider, the open source nature of MLflow provides unparalleled options for customization and adaptability. Whether this flexibility is a drawback or an advantage depends on how much configuration and maintenance your team and organization can handle.
Integration and compatibility challenges
MLflow integrates with many machine learning frameworks, cloud platforms, and third-party tools. However, in practice, the extent of these integrations might not always meet every organization’s unique requirements – especially for those utilizing less conventional tools or proprietary frameworks.
We developed a proprietary tool for preprocessing our data, and integrating it with MLflow wasn’t straightforward. We had to invest additional engineering hours to make it work.— Lead ML Engineer at a FinTech firm in the UK
Data storage integration might pose another set of challenges. While MLflow can work with several storage solutions, it does not support all of them.
Due to industry-specific compliance, we use a niche cloud storage solution. Sadly, MLflow didn’t offer an immediate integration for it.— Data Scientist at a health technology firm
Overall, while MLflow is versatile, teams with unique workflows or tools should be prepared for some hands-on tweaking to achieve seamless integration.
Lack of dedicated support
Open source MLflow benefits from solid documentation and community support, which many users find sufficient. This support typically comes from forums and discussion groups, but it’s important to note that there’s no assurance of a prompt response.
Further, since all discussions take place in public and are archived, you cannot share sensitive information. Your organization’s policies might even prohibit you from sharing any details about your infrastructure or tech stack.
The lack of dedicated support when it comes to setting up, troubleshooting, or maintaining the platform is a pain point for many organizations, especially as they scale their machine-learning initiatives.
As an open source project, MLflow does not guarantee:
- Timely response to questions
- Access to expert guidance on complex topics that go beyond the documentation
- Onboarding and continued training
- Acting on feature requests
- Support for custom integrations and extensions
MLflow has a vibrant community. Many of its users share their experiences through online discussions, talks, and blog posts. However, the lack of dedicated support might lead you to consider a managed platform backed by a company.
This begs the question: What other options exist if MLflow is not for your team?
Let’s explore some of the alternatives to MLflow available.
Alternatives to open source MLflow
There are many alternatives to open source MLflow available.
First of all, there is Managed MLflow by Databricks. It’s exactly what it sounds like: MLflow instances hosted and managed for you by Databricks, the original creators of MLflow.
Azure Machine Learning, the end-to-end ML solution on Microsoft’s Azure cloud platform, is unique among the alternatives to MLflow. While not based on MLflow, many of its components, such as the model registry or experiment tracker, are compatible with MLflow.
Then, there are managed ML products offered by dedicated companies. neptune.ai, Weights & Biases, Comet ML, and Valohai all provide platforms with different feature sets worth considering.
Metaflow, an open source framework initially developed by Netflix, is focused on orchestrating data workflows and ML pipelines. While it lacks many features MLflow offers regarding experiment tracking and model management, it excels at managing large-scale deployments.
Finally, there are Amazon SageMaker and Google’s Vertex AI, the end-to-end MLOps solutions integrated into these tech giants’ cloud platforms.
With that overview in mind, let’s dive deep into each of these alternatives to MLflow.
Managed MLflow (Databricks)
MLflow is available in two main flavors: open source MLflow and Managed MLflow, a service offered by Databricks, MLflow’s original creators. While both versions retain the core functionalities that MLflow is widely renowned for, they cater to different audiences and use cases.
One of the benefits of managed MLflow is the tight integration with other Databricks services, such as Databricks Notebooks, the Databricks Jobs Scheduler, and managed Spark clusters.
Cases where Databrick’s Managed MLflow excels over open source MLflow
Managed MLflow alleviates many drawbacks that large organizations face with the open source variant. It’s a good choice for teams for which MLflow fits their machine learning workflow but for whom the lack of security and user management features is a dealbreaker.
neptune.ai is an experiment tracking platform and metadata store that offers model versioning, model registry, and real-time model performance monitoring. It’s focused on ML team collaboration, comes with fine-grained user management, and has a highly customizable user interface with many visualization features. It is available as a managed and self-hosted offering.
Thanks to its built-in MLflow integration, data scientists can use MLflow’s client for experiment tracking. Thus, Neptune is an interesting option if you’re migrating from an existing MLflow setup.
Neptune’s features include:
- Metadata management: Users can log a diverse range of metadata, from metrics and hyperparameters to interactive visualizations, jupyter notebooks, source code, and data versions.
Neptune can be the one source of truth no matter where your team runs the training (whether it’s in the cloud, locally, in notebooks, or somewhere else). For any model, you’ll know who created it and how, what data it was trained on, and how it compares to other model versions.
- Intuitive user interface and custom dashboards: Neptune’s user interface facilitates the viewing, analyzing, and comparing different experiments. This is particularly useful when trying to discern patterns or identify the best-performing models among a batch of runs.
Users can design and set up custom dashboards, aggregating pertinent metadata. This feature is tailored for collaborative scenarios where sharing insights with team members or stakeholders becomes essential.
- Collaborative features available in the free tier: Even without paying for the service, up to five users can collaborate on a project. This makes Neptune an interesting option for researchers and students without a tool budget. It also allows teams to test whether Neptune fits their workflows and collaboration needs.
Neptune offers integrations with a wide range of ML frameworks and tools.
Cases where neptune.ai excels over MLflow
Neptune shines for teams looking for an easy-to-use platform with extensive experiment visualization and metadata tracking features. However, it might pose integration challenges for organizations heavily invested in custom or legacy infrastructure.
Azure Machine Learning
Azure Machine Learning is Microsoft’s cloud-based MLOps platform. It lets you manage and automate the whole ML lifecycle, including model management, deployment, and monitoring. It is tightly integrated with the Azure platform, offering seamless integration with other Azure services that many organizations already use.
Weights & Biases (WandB)
Weights & Biases (WandB) is a platform for experiment tracking, dataset versioning, and model management. WandB’s components, including the Model Registry and Artifacts, allow you to store and manage the model ML lifecycle, version datasets, and models. That helps with the lineage tracking of machine learning models and fosters their reproducibility.
One of WandB’s standout features is its hyperparameter sweep capability. While you can undoubtedly set and adjust hyperparameters in any Python script, WandB automates and optimizes this process. By defining a hyperparameter search space, WandB can automatically train multiple models with different hyperparameter combinations to help you identify the best configuration without manual iteration. It visualizes the performance of those hyperparameter combinations in an intuitive dashboard, enabling data scientists to make informed decisions quickly.
Cases where WandB excels over MLflow
WandB excels in visualization, real-time monitoring, collaboration, and a user-friendly interface.
Comet ML offers a comprehensive platform for MLOps, providing cloud-based services that cover a broad spectrum of the MLOps lifecycle, from model training to deployment. Comet’s capabilities include ML experiment tracking, model management, collaboration tools, and proactive notifications.
Comet’s standout feature is its ability to automatically capture and categorize essential run metadata such as parameters, metrics, code versions, and outputs. The platform boasts a unique experiment comparison engine that greatly exceeds MLflow’s capabilities. Instead of merely displaying tables or lists of results, Comet’s engine visualizes the data in ways that facilitate quick comparisons and insights, allowing researchers and data scientists to discern differences between model variations efficiently.
Cases where Comet excels over MLflow
While both Comet and MLflow assist in tracking and managing ML experiments, their target audiences differ. Comet ML is more geared toward teams seeking an out-of-the-box, cloud-based solution with rich visualizations and collaboration features, while MLflow emphasizes customizability and integrations.
Valohai is an end-to-end MLOps platform for traditional machine learning and deep learning tasks. It supports hyperparameter tuning, feature engineering, and artifact tracking. Valohai manages the end-to-end orchestration, including scheduling, notifications, and handling failures.
However, Valohai might not be the best fit for more straightforward data analytics or statistical tasks that don’t require full-fledged machine-learning pipelines.
Additionally, integration requires extra effort for organizations that predominantly use platforms or ecosystems outside of what Valohai supports.
Cases where Valohai excels over MLflow
Valohai excels over MLflow when it comes to workflow orchestration, user management, integration with third-party tools, and Kubernetes features.
Metaflow is an open source framework for developing, deploying, and operating machine-learning applications. In contrast to MLflow, its primary focus is helping data scientists and ML engineers manage the infrastructure that powers these applications.
While MLflow excels at experiment tracking, model versioning, and deployment, Metaflow addresses the often complex pipelines that feed into and arise from these models. Its emphasis on orchestrating scalable data applications provides a robust foundation, ensuring that the data feeding into models is consistent, reproducible, and scalable.
Cases where Metaflow excels over MLflow
Originally created by Netflix, Metaflow excels over MLflow when it comes to scaling, pipeline orchestration, workflow design, and integration with third-party features.
Amazon SageMaker is a fully managed service on AWS that enables users to build, train, and deploy machine learning models in the cloud. Included in Amazon’s suite of cloud services, SageMaker offers capabilities such as logging machine learning experiments, tracking model performance, and storing relevant metadata and artifacts.
Amazon SageMaker is one of the oldest offerings on the market and is particularly interesting for those already invested in the AWS ecosystem.
Cases where Amazon SageMaker excels over MLflow
Amazon SageMaker shines for teams looking to manage the entire machine learning lifecycle on one platform and native integration with the AWS ecosystem.
Vertex AI is the fully managed machine learning solution of Google’s Cloud Platform. Vertex helps you build, train, deploy, and monitor machine learning models at scale. It provides a unified platform for the entire ML lifecycle, from data preparation and model training to model deployment, model experiment tracking, and monitoring.
Google has firmly established itself as a leading machine learning and AI player, with many advances coming from the tech giant’s research branch and infrastructure teams. Much of this experience has found its way into Vertex. The platform is especially interesting for teams working on Google Cloud or looking to leverage leading data management solutions like BigQuery.
Cases where Vertex AI excels over MLflow
Vertex AI shines when it comes to managing scalable training and deployment infrastructure and seamless integration with the Google Cloud platform.
MLflow has become a cornerstone of many machine learning platforms due to its flexibility and availability as an open source tool. However, as teams scale up, limitations around collaboration, deployment, and advanced functionality like user management, fine-grained access control, and collaboration often emerge.
Whether MLflow is the right choice depends heavily on your team’s needs and existing MLOps stack. But for many, the advanced visualization, hosting, permissions, and ease of use of alternative platforms like neptune.ai provide compelling reasons to move away from MLflow.
In this article, you learned the key considerations when evaluating alternatives to MLflow, including deployment requirements, functionality needs, and ease of transition. You and your team should also weigh the benefits of community-driven development versus commercial solutions. There is no universal best choice – the optimal machine learning platform depends on the team’s and organization’s requirements.
Table comparison of alternatives to MLflow based on core experiment tracking features
In the table below, I’ve summarized the key capabilities of alternatives to MLflow discussed in this article.