Practical MLOps — MLflow

• 25 Apr, 2024
• By: Sudhir
• 23 Comments

Practical MLOps — MLflow

MLflow is a popular open-source tool that plays a key role in implementing MLOps, which stands for Machine Learning Operations. MLOps is essentially the application of DevOps practices to the machine learning lifecycle. In simpler terms, it’s about streamlining the process of developing, deploying, and managing machine learning models.

The machine learning community is currently working towards establishing a standardized process model for the development of machine learning projects. Consequently, many projects in the realms of machine learning and data science lack proper organization, leading to challenges in reproducibility of results. Typically, these projects are conducted in an ad-hoc manner. In order to provide guidance to machine learning practitioners throughout the development lifecycle, a recent proposal introduced the Cross-Industry Standard Process for the development of Machine Learning applications with Quality assurance methodology (CRISP-ML(Q)). In this review, we delve into the fundamental phases of the machine learning development process model. While there exists a specific sequence for the individual stages, it’s essential to acknowledge that machine learning workflows are inherently iterative and exploratory. Therefore, based on outcomes from subsequent phases, there might be a need to revisit earlier steps.

Overall, the CRISP-ML(Q) process model describes six phases:

• Business Understanding and Data Understanding: This initial phase combines understanding the business problem and the available data. You’ll define the business objectives, identify success metrics, and explore the data to understand its characteristics and potential for machine learning.

• Multi — Agent Problem Solvers (collaborative role playing is key) — these systems rely on collaborative and automated build up of solutions based on outputs of agents being fed into each other with a well defined role and purpose. Each agent has access to it’s own set of tools and can assume a very specific role while reasoning and planning it’s actions.

• Modeling and Tuning: Here, you select and experiment with different machine learning algorithms. You’ll train models on the prepared data and use techniques like hyperparameter tuning to optimize their performance.

• Evaluation: This phase is crucial for assessing the effectiveness of the trained models. You’ll use various evaluation metrics to judge the model’s performance on unseen data and compare different models to select the best one.

• Deployment: If a model performs well, it’s time to deploy it into production. This involves packaging the model, integrating it with your system, and setting up infrastructure to serve predictions.

• Monitoring and Maintenance: Just like in the traditional lifecycle, monitoring the model’s performance over time is essential. You’ll track its accuracy, address any degradation, and retrain the model with new data as needed to maintain its effectiveness.

CRISP-ML offers several advantages:

• Standardization: It provides a common language and framework for machine learning projects, fostering collaboration and knowledge sharing across teams.

• Structured Approach: By following its phases, you ensure a comprehensive and well-organized development process, reducing the risk of overlooking crucial steps.

• Improved Communication: CRISP-ML terminology facilitates communication between data scientists, business stakeholders, and other project members.

While CRISP-ML is not a rigid prescription, it serves as a valuable guideline for developing robust and successful machine learning models.

For every stage outlined in the process model, CRISP-ML(Q) mandates a quality assurance approach that involves several key components. These include defining requirements and constraints, such as performance benchmarks and data quality standards, instantiating specific tasks like selecting machine learning algorithms and conducting model training, identifying potential risks that could hinder the effectiveness and achievement of the machine learning application, such as bias, overfitting, or reproducibility issues, and implementing quality assurance methods to mitigate these risks when necessary.

Examples of such methods include employing cross-validation techniques and thoroughly documenting both the process and the results obtained.