MLOps – definition
MLOps is short for machine learning operations. It refers to a set of rules that ensure the effective implementation of ML solutions in production systems. We can understand MLOPs as a method that combines ML with DevOps and Data Engineering. In other words, it is a combination of these three areas. With this merger, it is possible to create automated, reliable, and efficient ML systems. Besides, MLOps aim to bridge the gap between data scientists who develop models and the teams responsible for deploying and maintaining those models in real-world applications.
Key components of MLOps
DATA MANAGEMENT
The MLOps process begins with the appropriate preparation of data that will be used to train and test models. This includes the collection, purification, transformation, and standardization of data.
MODEL DEVELOPING
Data scientists and engineers are developing ML models. They are experimenting with different algorithms, hyperparameters, and model architectures.
MODEL DEPLOYMENT
This phase includes the preparation of the model to run in the production environment. It is converted into a suitable format and then deployed on the required infrastructure.
MODEL MONITORING
The model is continuously monitored to track its performance in the production environment. This includes the following elements:
- Monitoring the quality of input data
- Detecting data drift
- Evaluating model accuracy
- Responding to anomalies
- Taking corrective action in case of problems
AUTOMATED MODEL RETRAINING
MLOps aims to automate as many model management processes as possible. This applies to both implementation and monitoring processes.
VERSIONING AND TRACKING
MLOps assumes that models should be versioned to be able to track changes and revert to earlier versions if needed. Versioning makes it easier to manage the model in different environments and track changes made by different people.
INTEGRATION WITH DEVOPS
MLOps works closely with DevOps practitioners. Deploying and managing models is treated like deploying and managing software. MLOps uses various tools used in DevOps, including version control systems, CI/CD tools, and test automation.
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Why MLOps are important?
ENHANCED PRODUCTIVITY
MLOps allows you to effectively manage all ML lifecycle processes. This has a positive impact on the successful work of ML engineers and data analysts. MLOps increases productivity by:
- Creating automated pipelines
- Providing a framework and tools for efficient experimentation with models
- Seamless deployment
- Facilitates collaboration between teams
RELIABILITY
Reliability is another feature of MLOps. Incorporating CI/CD practices ensures higher quality, consistency, and faster implementation. What’s more, such an automated ML lifecycle is able to minimize human errors. In this way, companies can gain real insights into the data.
REDUCTION OF MODEL IMPLEMENTATION TIME
MLOps ensures the automation of the model implementation process. It leads to a reduction in the time needed to transfer the model from the development phase to the production environment. Thus, organizations can implement and use the potential of their models faster.
MONITORING THE BEHAVIOR AND PERFORMANCE OF ML MODELS
Models tend to drift with changes in the environment. This means that their effectiveness and efficiency may deteriorate over time. With changes in input data, business context, or infrastructure, models can lose their original quality. Therefore, model monitoring is a necessary step. How do MLOps enable companies to gain insight into model performance? Primarily by:
- Performing model retraining regularly
- Sending information signals in case of model drift
Conclusion
Introducing MLOps to machine learning model management practices can bring numerous benefits. With MLOps, organizations can achieve better business results and gain a competitive advantage. Its implementation is, therefore, crucial for those who strive to effectively use ML in its operations.
