As machine learning matures into a more advanced, sophisticated subset of business technology, it becomes necessary to explore ways of transitioning existing business processes into new ML-enabled technologies. Much like the process of digital transformation, machine learning transitions must be handled effectively to be productive.
The Basics of MLOps
Much like DevOps, MLOps seeks to refine the methods by which organizations are able to implement new machine learning technology. Machine learning technology brings with it some tremendous advantages in terms of collecting and processing data. But when poorly handled, organizations can run into major roadblocks:
- C-suite executives and senior management may not understand the benefits that machine learning technology can bring.
- Machine learning initiatives may be applied within an organization disjointedly, working apart from the organization’s core processes.
- Data access and data management may not be fine-tuned or optimized for the ML process, leading to inconclusive ML results.
- Organizations may not be able to properly provide for the projected overhead or timelines regarding their ML implementation.
Machine learning is an advanced technology that is becoming more well-developed every year, but it does require some concerted effort for a complete transition. Organizations need to be able to devote the time, resources, and data to their machine learning solutions, and they have to be prepared to provide their ML solutions with high-quality data if they are to achieve high quality results.
The Core Tenets of MLOps
MLOps takes much of its language from traditional DevOps. The goal of MLOps is to create a structure through which an organization is able to better implement their ML technology. A few core advantages of MLOps include:
- The ability to appropriately determine the areas in which machine learning will be most suitable for an organization.
- Creating core business processes to procure, manage, and maintain the high quality of data necessary for machine learning.
- Automating and streamlining services to return the best possible results from machine learning in the least disruptive way.
- Creating a machine learning system that will continue to be useful and sustainable for the organization in the future.
In short, MLOps is a way to implement machine learning most effectively for an organization. An organization interested in developing its machine learning solutions can use MLOps to proactively integrate machine learning throughout its business processes, in a way that provides the most possible value.
The MLOps Maturity Model
As organizations continue to develop their proficiency with machine learning operations, it becomes necessary for organizations to be able to “self test” and audit their own levels of competency. The MLOps maturity model rates an organization based on five important factors:
- Strategy. Is the organization able to address its major pain points through the adoption of machine learning? Is machine learning properly addressing the problems that it was meant to address?
- Architecture. Is the organization’s machine learning architecture able to operate sufficiently as a whole, including data, models, and the deployment environments?
- Modeling. Does the organization have the skills and the experience to accurately manage its delivery of models within the domain?
- Processes. Are the organization’s processes optimized, effective, and measurable, to the extent that their performance can be measured and their effectiveness can be improved?
- Governance. Is the organization able to maintain the security and stability of its machine learning system, insofar as it must both secure it and ensure the reliability of its input and its output?
By measuring the organization on these metrics, the organization will be able to see whether it is improving its MLOps and whether it is becoming a more mature, competent organization in terms of its MLOps adoption.
Without the ability to measure MLOps maturity, organizations aren’t able to pare down to the areas in which their ML deployment needs to be improved.
The DevOps and MLOps Shared Pipeline
The ultimate goal under DevOps and MLOps is for both to share a pipeline. The DevOps Pipeline includes ideation, development, and integration, whereas the MLOps Pipeline includes the preparation, training and evaluation, and registration of data. Both the DevOps and MLOps processes will have a shared repository, which will then push toward packaging and certifying a model, and ultimately releasing an app.
A singular shared pipeline is critical for the success of a project, as it reduces the complications that otherwise would arise. A closed operational loop ensures that MLOps are integrated throughout the development process, and drives value.
Organizations need to proactively adopt MLOps if they are to leverage machine learning to a consistent and sustainable degree. While any organization can include machine learning platforms in their existing infrastructure, they may not be able to do it well unless they are doing it with the correct process management in place.