In a data-driven world, there is increasing importance on the use and implementation of artificial intelligence (AI), machine learning (ML) and data science, which all help to derive insights from tremendous amounts of data. Over the last few years, there have been significant developments in these technologies, greatly supporting the growing numbers of businesses on their digital transformation journeys. According to IBM, in 2022 35% of companies reported using AI in their business, and an additional 42% reported they are exploring the technology.
In the past, it was often thought that ML – the pathway to AI – was reserved solely for academics and specialists, able to utilise their specific skill sets to develop complex algorithms and models. Nowadays, the data scientists that work on these projects require the right knowledge and tools to be able to effectively productise their ML models for consumption at scale – which can be a complicated process involving sophisticated infrastructure and multiple stages in ML workflows.
Overcoming these complications is crucial to the long-term success of AI programs, because training models to provide accurate predictions is just a small part of a much bigger process. Fortunately, this can be achieved through six key sustainability measures: repeatability, availability, maintainability, quality, scalability, and consistency. Building ML systems that deliver business value requires an effective strategy, involving regular iteration cycles with continuous monitoring, care and improvement.
This is where Machine Learning Operations (MLops) come in. MLops is a core function of ML engineering that focuses on streamlining the process of taking ML models to production, and then maintaining and monitoring them. It’s a collaborative function, often comprising data scientists, engineering and IT operations teams, allowing them to work together in harmony to increase the pace of model development and production.
Using MLops to address problems
By offering a technological foundation for managing the ML lifecycle through automation and scalability, MLops enables enterprises to address the problems they may encounter when taking AI applications into production. These are repeatability, availability, maintainability, quality, scalability and consistency. To explore in more detail:
- Repeatability – A technique that guarantees the ML model will operate successfully in a repeatable manner.
- Availability – This refers to how the ML model is deployed, done so in such a way that makes it able to provide inference services to consuming applications and provide the right level of service.
- Maintainability – This allows the ML models to remain maintainable in the long term, for instance, when retraining the model becomes necessary.
- Quality – An ML model is continuously monitored to ensure it delivers predictions of a high enough quality.
- Scalability – Scalability refers to the ability to scale the inference services, personnel and procedures needed to retrain the ML model.
- Consistency – A consistent approach to ML is essential to ensuring success on the other noted measures above.
MLops allows data teams to achieve faster model development, enables scalability and management where thousands of models can be overseen, controlled, managed, and monitored for continuous deployment. Additionally, MLops can make AI consumption simpler so that applications can employ ML models for inference (i.e., to make predictions based on data) in a scalable, maintainable way – ultimately the primary value that AI initiatives deliver to businesses.
Improving AI initiatives
The main stages of the ML lifecycle are typically covered by MLops: model development, model training and validation, and deployment. The effectiveness of AI projects can be quantifiably increased by using the six tested MLops strategies below, in terms of time to market, results, and long-term sustainability.
- ML pipelines – These typically contain multiple steps which can be complex, from gathering data to transforming it into a format that can be used to train ML models. But, MLops platforms can translate these steps into more simplified jobs, providing a cloud-native, yet platform-agnostic, interface for the component steps of ML pipelines.
- Inference services – Once selected, the appropriate model needs to be deployed to a production environment. Using the model-as-a-service aspect of ML separates the application from the model through an API, further simplifying processes such as model versioning, redeployment and reuse.
- Continuous deployment – Being able to autonomously retrain and redeploy ML models when significant model drift is detected is crucial. With powerful open source platforms, MLops pipelines running on open source job schedulers can easily be triggered.
- Blue-green deployment – Blue-green deployment is an application release model that gradually shifts user traffic from an older version of an app or microservice to a new, nearly identical version, both running in production. Creating an ML deployment with two predictors means the MLops team can observe the quality of the predictions. This can then be used to determine which predictor the traffic can be directed to.
- Automatic drift detection – As production data changes over time, model performance can be skewed because of variations in new data against that used in training. Drift detectors can be used to automatically assess model performance over time and trigger a model retrain process and automatic redeployment.
- Feature stores – These allow data scientists and engineers to reuse and collaborate on datasets that have been prepared for ML – so-called ‘features’. By sharing access, time to market can be greatly accelerated, whilst improving overall ML model quality and consistency.
As companies invest more heavily in technologies such as AI and ML, it’s likely they’ll face a number of challenges in getting the most value out of their ML models. By approaching the task with the six sustainability measures in mind – repeatability, availability, maintainability, quality, scalability, and consistency – and investing in the six MLops techniques measurably improve the efficacy of AI initiatives, data scientists and engineering teams can work collaboratively to measurably increase their long-term success and gain a competitive edge.
