Last updated: November 3, 2022.
The paths that machine learning projects take on their way to production are long and perilous. Too many projects fail to reach their full potential and end up in drift limbo, or they accrue technical debt and complexity to the point where they become nearly impossible to maintain and evolve.
Somewhere on the way to production ML (via GIPHY)
If the history of traditional software development is any indication, MLOps frameworks should be the much-needed solution that ML practitioners and AI/ML teams need to manage the extensive and often-difficult-to-navigate ecosystem of ML tools. But not all libraries and tools labeled as MLOps frameworks are created equal. Some only focus on tool orchestration while others are limited in the range of tools they can integrate with and are not easily extensible. This article begins with highlighting a set of important traits that mature MLOps frameworks should exhibit if they hope to withstand the rigors of ML projects running in production. The article concludes by evaluating ZenML as an MLOps framework that stands up well to these evaluation criteria.
The Burdens of Unhindered Development
We need a continuously growing set of features, tools and technologies if we want to advance a machine learning project past the initial stages of ideation and experimentation. Experiment tracking is required to record, recall and share results. Getting the model accuracy up to acceptable levels in decent time often involves employing distributed data processing, model training and hyperparameter search techniques. For online inference, a model serving system must be present, followed by monitoring, data profiling and drift detection. Some form of orchestration is crucial to getting all these features to interact and function in sync to achieve a continuous automated workflow. This brief description barely scratches the surface of what is required to build a successful MLOps platform and I haven’t even mentioned the infrastructure layer with all its intricacies.
Confronted with this overwhelming complexity, AI/ML teams usually go one of two ways:
- either they adopt a full fledged managed MLOps platform and benefit from the guarantees that they offer from day one
- or they construct their own custom MLOps system by incrementally connecting together the tool components mandated by their particular needs and preserving a high degree of control over tooling and infrastructure choices in the process.
While there certainly are upsides and downsides to both approaches, I argue that using an MLOps framework is a great compromise between the two pathways. It avoids inheriting the technical debt of reinventing the wheel without taking away any of the benefits of flexible and extensible tooling.
Are MLOps Platforms the Answer?
Before I begin to dive into MLOps frameworks, it will be worth revisiting the limitations of MLOps platforms to set down some basic references and expectations.
The Downsides of Managed MLOps Platforms
Adopting a managed MLOps platform has a lot of appeal, especially if we want to avoid the overhead of having to manage our own tools and infrastructure. Cloud-based platforms like Amazon SageMaker, Azure Machine Learning and Google Cloud AI Platform are immediately available and can significantly accelerate the machine learning project development. Moreover, they require a fraction of the cost of building and operating a custom-made platform. However, there are several drawbacks to adopting a managed MLOps platform, some quite subtle, that should not be easily overlooked.
Managed platforms are opinionated, general purpose solutions that are not tailored to specific needs. Adopting a managed platform when our project is still in the early stages of development and we only have a vague idea of the challenges ahead carries a high risk. Even when the requirements are clear, incompatibilities may not become evident until much later, when it’s too late to alter course.
Seemed like a good idea on paper... (via GIPHY)
Another disadvantage of managed platforms is that they are inflexible and slow to change. They might provide 80% of the functionality we require, but it is often the case that the missing 20% provides functionality that is mission critical for machine learning projects. The closed design and architecture of managed platforms makes it difficult to make even the most trivial changes. To compensate for this lack of flexibility, we often have to design custom, inefficient and hard-to-maintain mechanisms that add technical debt to the project.
The Pitfalls of DIY MLOps Platforms
Switching to the other side of the coin, MLOps platforms that are built in-house are more flexible and easier to adapt to future requirements but retain many of the disadvantages of their managed counterparts. For one, they are usually constructed to respond to a very concrete set of necessities and implemented with a focus on functionality and fast results often limited by budgetary restrictions. Flexibility is classified as a luxury that few organizations want to invest in, resulting in systems that are as opinionated and rigid as managed platforms.
My model is in here somewhere (via GIPHY)
This limitation of DIY platforms does not become apparent until the machine learning project needs to do a strategic shift to respond to a change in customer behavior or requirements, or to incorporate a newer or more performant set of tools and technologies. Refactoring any platform to reflect external changes in requirements or to adapt it to new projects can be challenging and costly.
The Zen of MLOps Frameworks
The machine learning ecosystem is still in a state of rapid evolution and you probably don’t want to be locked into a rigid platform if you want to stay ahead of the curve. Revolutionary machine learning tools and techniques are continuously emerging and evolving, and the need for a flexible and easily extensible MLOps framework is indisputable.
MLOps frameworks are a particular category of integration frameworks. To put it simply, they coordinate the movement of code, data, models and other machine learning artifacts between software components that are otherwise not directly compatible and interoperable.
The defining characteristic of integration frameworks could be summarized as:
- identifying functional and architectural patterns repeated in a large set of libraries or systems
- leveraging these patterns to define a representative set of high-level abstractions
- providing implementations for these abstractions through a modular and extensible set of integration adapters
This strategy of extracting simplicity out of complexity is key to the success of any integration framework and has been proven time and time again by democratizing remarkable technological advances such as virtualization, containerization, cloud, cloud-native and serverless computing.
The recent 0.8.0 ZenML release marks a major change in perspective that the ZenML team has undertaken for the past couple of months. We shifted our focus away from merely orchestrating a growing list of available integrations and towards making ZenML a vastly extensible MLOps framework that addresses all the shortcomings of MLOps platforms. In the second part of this article, I define some important principles that all mature MLOps frameworks should abide by and reveal how they are reflected in the ZenML framework.
The various phases that ML projects go through from early prototyping to production have varying requirements and constraints concerning the set of involved tools and technologies. Early-stage experimentation probably doesn’t need access to centralized storage or powerful distributed processing systems. In contrast, a project running in production might need to use not one, but many different tooling and infrastructure configurations targeting different production environments suitable for large-scale data processing pipelines, distributed storage, orchestration and model deployment.
MLOps frameworks must make the transition from experimentation to production as smooth as possible, to reduce or even eliminate the need for costly code refactoring.
ZenML does a stellar job of decoupling the ML pipeline code from the tooling and infrastructure required to run it. With ZenML, you can start experimenting with a local Stack from day one, and then gradually switch to using more elaborate Stack configurations that are fit for production, as your project grows and its requirements get more demanding. All this with nearly zero code changes!
MLOps frameworks detach the user experience from the underlying complexity by defining relatable and easy to use high-level machine learning abstractions. These abstractions must be easy to understand and use. Above all they must be evocative of the set of concepts that ML and practitioners deal with on a daily basis
ZenML uses an expressive range of machine learning concepts that are continuously distilled and evolved from the classes of problems tackled by the ML tooling ecosystem.
At the same time, the ZenML team acknowledges that AI/ML teams are an amalgam of diverse roles and responsibilities and their challenges are not only technical but equally organizational. We design the ZenML framework abstractions as the foundation of a common machine learning software development language that all personas involved in the machine learning project lifecycle can learn, understand and relate to, from Data Scientists and ML Engineers to Operations and Site Reliability Engineers. More than an MLOps framework, ZenML is also a collaboration framework.
Highly Customizable through Extensible Design
MLOps frameworks must be extensible by definition. Integration boundaries must be clearly delineated and modeled as separate libraries or modules, with well-defined interfaces that can be extended and tailored to specific functionality needs.
At ZenML, we follow the S.O.L.I.D. and Clean Architecture design principles to maintain a loose coupling between core and external components and ensure that the framework is easy to extend and customize.
All ZenML Stack components have well-defined and well-documented interfaces. This makes it possible to extend ZenML to add new Stack component implementations without having to modify the core code and without invalidating any of the interoperability guarantees that ZenML provides. The following are some examples of the extensibility options supported by ZenML:
- you can add a new Artifact Store flavor by defining a few basic IO operations and seamlessly transition your existing code to use the storage backend of your choice
- if you need to use a custom pipeline orchestration tool that is not already included in ZenML’s list of integrations, you can write your own Orchestrator plugin
- to serve models using the model deployment tool of your choosing, the Model Deployer interface can be extended to accommodate any library or platform that serves that function
The extensibility of ZenML gives ML practitioners the freedom of DIY with all the benefits of interoperability and makes ZenML a powerful tool for building MLOps systems specifically tailored to the needs of any ML project.
The Future is Now
In any discipline, best practices and tools are being developed, they evolve and eventually converge. There comes a moment when a critical point is reached where a crystallization of the underlying principles and patterns occurs. This also happens to be tied to another generic principle that differentiates successful frameworks from short-lived ones: timing.
In machine learning, this turning point is happening right now and it is time for MLOps frameworks to shine and do what they do best: to further nourish the convergence of ML tools and best practices and to bring more structure to an ecosystem that is ripe for standardization.
Talk to Us
MLOps frameworks are created for and shaped by their users. If you would like to be part of the future of MLOps and ZenML, make sure to join our Slack to be part of the growing ZenML community and to contribute your feedback and ideas. We would love to talk to you and see if ZenML is helping you, and get your input as to where it should go next!