Machine learning operationalization (MLOps)

What is machine learning operationalization?

Machine learning operationalization, also known as MLOps, helps foster a culture and practice that aims to unify machine learning system development and machine learning system operation. 

Machine learning (ML) is the science of enabling computers to function without being programmed to do so. This branch of artificial intelligence (AI) can enable systems to identify patterns in data, make decisions, and predict future outcomes. Machine learning, for example, can help companies determine the products their customers are most likely to buy and even the online content they're most likely to consume and enjoy. With great ML comes a great amount of data, manifold models that are tried and tested in different environments, and concomitant projects galore. As such, MLOps as a discipline can be utilized to get a sense of the different stages and phases of ML, which can help create and maintain repeatable and successful ML projects. 

As MLOps is a discipline and not necessarily a reference to a particular software type, there are different tools that can assist in this process, besides just AI & machine learning operationalization (MLOps) software. For example, data science and machine learning platforms can include varying degrees of these capabilities, and so can data labeling software, which can include the ability to monitor and optimize models.

Types of machine learning operationalization

Although some tools provide end-to-end machine learning operationalization platforms, MLOps can be divided into different focus areas. There are three main groups these can fall into:

• Data management: Machine learning is nothing without data. MLOps can assist in the data management process, from data collection to data preparation and storage. This includes important tasks such as tracking data provenance and detecting data bias.

• Modeling: Good data feeds and fuels good models. However, models must be constantly and continuously updated and optimized. MLOps can help with building models, experimenting with them quickly, and providing resources for tracking the efficacy of a given model. It can also be a helpful resource for feature extraction, giving data scientists the tools they need to better understand their data and what it contains. 
• Operationalization: Models as part of an experiment are good, but models in production are great. MLOps, as the name implies, brings operationalization to the table, providing resources for bringing models from test environments into production. It can also be a great way to track their performance in these production environments and can help determine the best model for a given use case.

Key steps in the machine learning operationalization process

Machine learning is a journey, from data to predictions. Along that winding journey, MLOps can be a great way to keep track of the work and optimize the twists and turns in the road. For it to be useful, it must be embedded within a company’s broader data and machine learning initiative. The following are some of the key steps involved in the machine learning operationalization process:

• Versioning the source data and its attributes
• Building and experimenting with models
• Deploying the model
• Detecting for issues or anomalies such as model drift or data drift

Benefits of machine learning operationalization process

Machine learning operationalization presents several distinct advantages to organizations as part of their data strategy and model development. It makes it easier for data scientists, machine learning engineers, and other AI practitioners to have complete visibility over their machine learning projects and initiatives. The following are some of the benefits of machine learning operationalization:

• Faster experimentation and model development: In order to spin up an optimal model, experimentation is necessary. Everything from the dataset to the features that one would use is negotiable. However, this can often get out of hand with different teams or even different data scientists working in parallel without having access or visibility into the work of their colleagues. With MLOps, teams can fully understand where their data is coming from, where it is stored, and how it is being used. With a bird's-eye view, team members and leaders can thoroughly understand the projects they are working on and reproduce them more easily.

• Better development and deployment of models: MLOps can include feature store capabilities, which allow data scientists to manage the features that have been extracted from their datasets. This can make developing algorithms more manageable, as the building blocks are clearly provided and presented. This, in turn, can help with model deployment. When there is a well-defined process for developing models, teams can better understand what they can and should deploy. In addition, with MLOps, data scientists get tools that help them maintain and monitor their models, allowing them to tweak them when necessary and swap them out for better-performing ones.

• Scalability: If data is scattered, models are roaming free, and no one knows what to do next, machine learning projects will not succeed. If, however, an MLOps mindset is adopted as outlined above, scalability can follow suit since documentation will be in place, models will be properly cataloged, and more. This will give data scientists the tools to succeed in bringing current projects to completion and embarking on new ones.

Machine learning operationalization best practices

MLOps must become a reality, not just a vision. For this to happen, there needs to be buy-in from the data science team and beyond. The following are some best practices of machine learning operationalization:

• Training: Ensuring the right people are trained to use the software is paramount. This might include data scientists and business users who plan to benefit from the algorithms. Proper training will save time and money in the future.

• Buy-in: Besides just training, there must be buy-in and adoption from the team to ensure that the users are actually registering the models, using the feature store, etc. This will make it easier for others to look into the work and reproduce the results.
• Thinking end-to-end: MLOps covers the entire machine learning lifecycle. It might be prudent to begin in one area and slowly move into others.