Machine learning is quickly evolving and playing crucial roles in companies of all sizes. To get the full benefit of machine learning models, you must first put them in the production pipeline; this comes with many challenges, especially when done on a large scale.
These challenges cannot be fully managed by existing DevOps practices because difficulties with machine learning systems are fundamentally different. That’s where Machine Learning Operations (MLOps) comes into play.
In this article, we will discuss MLOps and the different frameworks available for MLOps.
What is MLOps?
In simple terms, MLOps is a set of best practices to successfully run AI-driven products for industries. MLOps combines well-established DevOps practices with the continuously growing field of machine learning, finding new solutions to create an automated platform (or environment) for data gathering, model training, model testing, model deployment, model monitoring, model retraining, feedback system, and so on.
Challenges of the industry before MLOps
Here are some challenges encountered in current machine learning systems, before MLOps:
- Dependency on the data changes with models and business objectives. This results in a lack of regularity and performance deterioration.
- Periodic training is required for machine learning models as real-world data changes with time. Therefore, this periodic model training requires setting up complex pipelines that feed data from data lakes.
- Difficulty managing configurations for machine learning projects like determining the data and features used, model parameters, verification methods, and so on.
- Risk of machine learning models not performing well in real-time, which will require monitoring metrics continuously.
- Collaboration issues can occur among business analysts, data engineers, data scientists, operations teams, and so on due to their competing needs. For example, business analysts focus on KPIs, whereas data scientists focus on the accuracy of models.
6 benefits of MLOps
Here are six potential benefits of using MLOps systems:
- MLOps makes the orchestration of different pipelines possible. This is useful for developing and updating the machine learning model continuously. For example, data pre-processing, feature engineering, model training, inference, and so on.
- MLOps help manage the lifecycle of machine learning projects. The lifecycle of a machine learning project includes different parts that can be considered as different entities, which are challenging to manage by traditional DevOps practices. MLOps brings technology, people, and processes together to resolve this problem.
- MLOps regulates deployment at scale. Currently, the problem lies not in development but in the deployment of machine learning models, especially when required to deploy and manage thousands of models into production.
- MLOps maintains the health of ML models at scale. MLOps establishes a system that autonomously maintains the health of machine learning models at scale. Sustaining the health of deployed machine learning models is a crucial job. Deployed models need continuous monitoring for behavior on real-world data and should be able to detect problems like model drift, data drift, and so on, automatically.
- MLOps provides CICD for machine learning projects. Continuous integration and continuous deployment (CICD) were made possible by DevOps practices for software products; however, for machine learning products, the traditional DevOps practices cannot be used for CICD, due to scale and its manner of operation. This generates the need for MLOps systems to provide CICD for machine learning projects.
- MLOps systems provide model governance solutions. This is crucial, especially in big organizations where machine learning models are deployed on a global scale and manually tracking rules, regulations, and compatible versions of the machine learning model become almost impossible.
Available MLOps framework and platforms
Here are some well-known MLOps framework and platforms:
- Algorithmia. Algorithmia helps to deploy, manage, and scale machine learning projects. It retains model versioning, maintains pipelines, and supports deploy models at scale. However, for now, it doesn’t offer assistance for model training.
- SageMaker. SageMaker is a fully managed service offered as part of AWS. It lends support for model training, debugging, hyper-parameter tuning, deployment, and monitoring as part of its studio environment.
- Azure Machine Learning. Azure Machine Learning is provided as part of Microsoft Azure. It includes assistance for training, hyper-parameter tuning, deployment, and monitoring. In addition, it also supports drag and drops functionality to define machine learning workflows.
- Google Cloud AI Platform. Google Cloud AI platform is a part of Google Cloud. It provides end-to-end support from model training to deployment and management. It also features an AI hub that includes many public resources, such as trained models, datasets, guides, notebooks, etc.
- MLflow. MLflow is an open-source platform supported by Databricks and used to manage the lifecycle of machine learning projects. It has different modules, namely tracking, projects, models, and registry.
To clarify, Algorithmia and MLflow are MLOps frameworks, whereas SageMaker, Azure Machine Learning, and Google Cloud AI are complete platforms for MLOps.
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