Unlocking MLOps as the New Superpower for the Enterprise
In a nutshell, MLOps focuses on addressing the hurdles between planning machine learning projects and deploying, operating, retraining, and scaling these models in production.
The lower the cost of creating and rolling out machine learning models across the organizations, the higher the positive impact of machine learning can be.
Production use of multiple instances of a machine learning model throughout the organization is the critical foundation for creating feedback loops to continuously enhance model performance.
Rolling out a machine learning model across multiple teams can come with the positive “side effect” of the model’s prediction quality increasing over time.
Therefore, organizations that are able to accelerate the creation of machine learning models, integrate these models with existing and future enterprise applications, and to continuously monitor for drift and bias, should be able to obtain significant competitive advantages compared to the rest of the pack.
Achieving this competitive advantage depends on an organization’s ability to enable data scientists and data engineers to continuously supply software development teams with the machine learning capabilities they require to enhance their applications. This was exactly the key evaluation criteria for the EMA Top 3 Enterprise Decision Guide for MLOps infrastructure and data platforms. The chart impressively shows how data science and data engineering teams focus on creating learning models (see left hand chart below) that are then harnessed by software developers (right hand chart).
Three Core MLOps Challenges
To unlock the competitive advantage of rapidly rolling out machine learning and continuously enhancing model performance, organizations have to solve three critical problems.
1. Maximizing Data Scientist Productivity
Data scientists constitute the rarest and therefore most expensive factor in the MLOps process. The more we enable data scientists to focus on their core task of creating well-performing machine learning models, the more value the organization will derive from this crucial persona.
The ideal platform needs to provide a unified framework to simplify and enhance feature engineering, data access and management, experimentation, and model monitoring as the top four daily challenges for data scientists. Here it will be critical to provide consistent processes and tooling for data scientists to efficiently work across data centers, public clouds, and edge locations. Data and infrastructure resources need to “follow” the data scientist, no matter the specific project requirements. The ultimate goal here is to enable the data scientist to stay within her favorite Python IDE (integrated development environment), without having to worry about requesting infrastructure, tooling, or permissions through manual processes.
2. Maximizing the Productivity of Data Engineers
Data engineers are responsible for creating, managing, monitoring, and scaling data pipelines and machine learning models. MLOps platforms and processes need to focus on automatically providing data engineers with a consistent set of infrastructure resources required to connect, clean, normalize, combine, enrich, process, and store the data that is required to create machine learning models.
Consistency in tooling and efficient pipeline management basically share the top of the list of challenges for data engineers, followed by data access and monitoring. This leads us to the core set of requirements for an optimal data engineering platform: one single set of tools, pipelines, and monitoring tools that “follows around” each data engineer for optimal efficiency.
3. Maximizing Developer Productivity
In order to get value out of machine learning models, software developers need to match application requirements with the capabilities of current machine learning models. If there are no suitable models available, they need to be able to collaborate with data scientists to quickly determine the cost effectiveness of customizing existing models or creating entirely new models.
The ability to easily evaluate the suitability of machine learning models for a specific use case leads the list of developer challenges. To address this issue, developers need the ability to easily deploy and test the learning models they believe might work best for their purpose. Ideally, an MLOps platform would provide a model store for developers to try out existing models without previous knowledge or significant cost.
Integrating machine learning models with their existing DevOps pipeline takes the number two spot in this list, with gaining access to the required data, and deploying the final machine learning-driven application coming in third and forth on our list of challenges.
