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Highlights
- About 26% of enterprises globally use AI and machine learning (ML) applications, while 43% are still evaluating them.
- At present, many AI/ML processes such as testing, deploying, and monitoring models require manual intervention, which is time-consuming and prone to errors.
- AI/ML process automation through machine learning operations (MLOps) can streamline processes, reduce costs for enterprises, and help data scientists focus on developing AI/ML solutions.
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Automate automation
Enterprises are looking for new ways to automate their AI and ML processes.
With the widespread adoption of AI and ML worldwide, organizations have now run into a new challenge. A significant amount of manual effort goes into developing and delivering AI and ML solutions such as gathering and pre-processing data, developing iterative models, testing, and deploying models, as well as versioning and monitoring models. To reduce manual intervention and speed up implementation, organizations are looking at automating processes involved in creating AI and ML models. However, the journey to automated data extraction, pre-processing, model development, testing deployment, and monitoring is not an easy one.
In this paper, we focus on the tools and best practices that enterprises can use to test, deploy, maintain, and monitor AI and ML models in an automated manner, from development to production.
Key challenges
From identifying problems to developing models, ML processes are highly iterative.
ML processes consist of two stages. The first stage involves identifying problems or scenarios in which ML can help. This is where businesses look for ML use cases, identify data sources (to feed data into ML systems), and develop the right-fit algorithms. In the second stage, data engineers prepare data for ML model development using one or more algorithms and then perform iterative experimentation and testing.
At present, many practitioners perform these activities manually, which is inefficient and prone to errors. MLOps addresses this by deploying, managing, and monitoring ML models in an automated manner. This improves the reliability and efficiency of ML models. MLOps implementation is growing and will continue to grow in the future. A survey indicated that 39% of enterprises are using MLOps tools, while 39% are looking forward to adopting them, and the remaining 22% are yet to adopt them for model serving. But how do enterprises ensure they get maximum value from their MLOps investments?
A phased approach
To get the best results, enterprises need to adopt a phased approach to MLOps.
Here are four key phases to get MLOps right for enterprises:
- Ingesting data: Make a variety of structured, semi-structured, and production data for training, testing, and production to ensure data availability based on use cases.
- Engineering data: Ensure that the transformed data is ready for processing since it is important to pre-process data to ensure algorithms are trained well.
- Developing models: Prepare the final models to be deployed iteratively by choosing the right algorithm and optimizing the model building process.
- Implementing MLOps: Deploy, manage, and monitor models in an automated manner using MLOps tools.
Once deployed, the AI/ML models can learn through continuous supervised or unsupervised learning. This will help improve deployed models through incremental learning based on the latest data. If incremental learning is not possible, the models can be retrained using the entire training data. Why focus on building incremental learning capabilities? Because algorithms that support incremental learning perform better over time as compared to algorithms that support static learning.
MLOps is not DevOps
We need to bust the misconception that MLOps is a DevOps activity.
Often, DevOps and MLOps are thought to be the same because they have a common goal: deploying software in the production environment. On one hand, DevOps involves automation of the software development lifecycle to provide continuous delivery of high-quality software. MLOps, on the other hand, deals with automating ML applications and workflows. Simply put, in MLOps, the deployed software also has an AI/ML component. This requires a special method of model development, comparison, management, and deployment that is not present in DevOps.
Figure 1: MLOps vs DevOps
Achieving excellence
MLOps enables self-service in deployment and management of ML models and automated monitoring of ML processes.
With MLOps, automated model testing, deployment, management, and monitoring is made possible. This means developers can focus solely on developing AI and ML solutions without worrying about testing or deployment. MLOps also automates process governance and plays a key role in bringing talent from several teams together to help enterprises achieve excellence with their AI/ML applications. Different roles that are included in AI/ML development are supported by MLOps as described in the table below.
Figure 2: MLOps contribution to AI/ML development
Enterprises need to keep a few things in mind when adopting MLOps:
- The same model may perform differently under different conditions.
- Rapid changes in versions of AI and ML code libraries can create a continuous need for library version upgrades.
- Inconsistent data can cause fluctuations in performance of AI and ML systems which would require models to continuously learn and adapt.
- One algorithm does not fit for all applications and the same algorithm may behave differently over time and for different applications.
When a phased approach is implemented well, MLOps can enhance efficiency, improve processes, and reduce development costs.
