MLOps Processes and Principles - The Complete Guide

Introduction to ML Project Life Cycle

The steps involved in the ML Life cycle and MLOps are all about advocating for automation and monitoring at all the above steps. Machine learning project development is an iterative process that means we continue to iterate from each of the above processes (except scoping) during the life cycle of a model to improve the efficiency of the process.

• For instance, we improve the data when new data comes in or feature engineer new features from existing data.

• We iterate through the modelling process according to its performance in production.

• Accordingly, the deployed model gets replaced with the best model developed during iteration.

• This process goes on with the iteration, but one should follow some best practices while iterating. We will talk about these here.

Mixed data scientists and services designed to provide automation in ML pipelines and get more precious insights in production systems. Click to explore about, MLOps Platform - Productionizing Machine Learning Models

MLOps Process for Continuous Delivery

Developing a machine learning model, deploying it fast and cheaply, and maintaining it over time becomes difficult. Any team developing Machine Learning solutions must follow best practices to get the most out of the models. This helps avoid “machine learning technical debt.”

The best practice that needs to be followed while developing ml solutions:

Data Validation

Data is the most crucial part of the ML system. If it does not validate correctly, it may cause various issues in the model. Therefore, input data that is fed to the pipeline must be validated. Otherwise, as data science says, garbage in, garbage out. As a result, data must be considered a top priority in the ML system. It should be continuously monitored and validated at every execution in the ML pipeline.

Experiment and track experiments

To get the best accuracy, one needs to do experiments. Machine learning is all about experimentation. It may involve trying out different combinations of code, preprocessing, training, evaluation methods, data, and hyperparameter tuning. Each unique combination produces different metrics to compare with other experiments and keep track of. Later, you can compare which combination is performing better.

Model validation across segments

Machine learning models' performance can degrade over time, and they need to be retrained to maintain good performance. Before deploying a model into production, it needs to be validated. Model validation includes producing metrics (e.g., accuracy, precision, rose, etc.) on the test datasets to check the model's performance so it can fit business objectives.

The model should also be validated on various data segments to ensure they meet requirements. Otherwise, the model can be biased in the data, and several incidents have happened where the model was biased and performed inadequately for some users.

Reproducibility

Reproducibility means that in machine learning, every phase should produce the same results, given the same input. It can be data preprocessing, model training, or model deployment. Reproducibility is challenging and requires tracking model artefacts such as code, data, algorithms, packages, and environment configuration.

Monitoring predictive service performance

The practice mentioned above can help you deliver a robust ML model. In operations, different metrics need to be measured to evaluate the performance of the deployed model. These metrics evaluate model performance regarding business objectives. Users might need good performance and better accuracy of the model, but they also need as fast as possible and availability all the time. To monitor operational metrics such as:

• Latency: measured in milliseconds,
• Scalability: how much traffic can the service handle at the expected latency?
• Service update: how much downtime is introduced during the service update?

For instance, delaying any service can impact the user, and it can cause loss to the business.

Automate the process

Managing machine learning tasks manually becomes difficult and time-consuming when the models get into production. Data preprocessing, model training and retraining, hyperparameter tuning, and model deployment can be automated. If data drift, model drift or the model's performance degrades. So it can be retrained automatically. It just needs to be triggered. After automating the process, the error margin becomes less and more models can be deployed. An ML pipeline can be used to automate the process. So, the model can follow continuous training and continuous delivery. 

Support the organization as they begin to climb MLOps practices through an organization's AI / ML workflows. Taken From Article, MLOps Roadmap for Interpretability

MLOps Best Practices Organizations should follow

Scope Management in MLOps

Scoping is a crucial initial step that involves defining project goals aligned with machine learning objectives. For instance, if the business team requests a conversational AI to handle FAQs on a website, this goal needs to be translated into a machine learning objective, such as developing a question-answering model.

Key Steps for Effective Scoping

1. Understand the Business Problem: To avoid wasted development effort, fully comprehend the business problem and verify your understanding with stakeholders before proceeding.

2. Team brainstorming: Gather and explore potential solutions from the team, encouraging diverse and innovative ideas.

3. Conduct Research: With a defined problem and initial ideas, research solution-oriented approaches to outline a roadmap.

4. Define the Development Roadmap: Create a visual flow of the development process with steps, timelines, and special dependencies (e.g., a data dependency from a data engineering team). Verify the roadmap with stakeholders.

5. Prepare an Approach Document: This document should outline the approach to solving the business problem, including any initial algorithms. Obtain stakeholder input to ensure alignment on the development strategy.

Data Processing Best Practices

Data processing is foundational before modelling. The following best practices help ensure data quality and integrity:

1. Understand Data Types and Issues - Classify datasets (structured vs. unstructured) and address specific data processing needs accordingly.

2. Define the Dataset for Structured Data - Gather detailed information on each data column to avoid ambiguities. Clearly distinguish features and labels before proceeding.

3. Ensure Consistent Labeling for Unstructured Data - When multiple labellers are involved, provide clear labelling instructions to maintain consistency across the dataset.

4. Data Versioning - Use data versioning tools like DVC to track dataset versions or maintain versioning records manually if tools are unavailable. This allows for reproducible experiments.

5. Consistency in Data Pipelines - Ensure consistency in data pipelines across development, testing, and production stages. Make pipelines fault-tolerant to handle exceptions in production.

6. Balanced Train/Validation/Test Splits - Ensure that train/dev/test splits represent the overall dataset distribution, preserving the class balance (e.g., 30% positive samples across all splits).

7. Prevent Data Leakage - Avoid exposing target information in training data that would be unavailable during prediction, as this can lead to overestimated performance during training.

Data Modeling Best Practices

The following best practices ensure the effective development and evaluation of machine learning models:

1. Define a Baseline and Benchmark the Model: Establish a baseline using a simple algorithm or human-level performance for unstructured data, ensuring a reference point for model comparisons.

2. Model Versioning and Tracking: Use model versioning tools like MLflow to track experiments. Alternatively, track versions manually in text files if tools are unavailable.

3. Error Analysis: Post-training, perform error analysis to identify areas where the model performs poorly, particularly in specific classes. Use metrics like precision, recall, and F1 score in addition to accuracy.

4. Data-Centric Approach over Model-Centric:  Prioritize data improvements over model complexity. Simple models on high-quality data often outperform complex models with poor data quality.

• Data Augmentation for Unstructured Data: Create additional examples in areas with higher error rates.

• Feature Engineering for Structured Data: Adding new features if creating new samples is impractical.

Developing Fair and Unbiased ML Algorithms

To prevent biases in ML applications, especially those used in sensitive areas (e.g., credit approvals), apply the following fairness and bias mitigation strategies:

• Bias Analysis in Data - Conduct thorough data analysis to identify and reduce representational biases. Ensure all demographics are fairly represented to avoid discrimination in model outcomes.

By following these practices, MLOps teams can build robust, scalable, and fair machine-learning solutions that are production-ready.

XenonStack for MLOps

For the ideal adoption of ML across organizations, there is a standardization of the machine learning workflows, so there is no difficulty in implementation.

• ML Model Lifecycle Management: Akira AI provides MLOps capabilities that help build, deploy, and manage machine learning models to ensure the integrity of business processes. It also provides a consistent and reliable means of moving models from development to production environments.

• Model Versioning & Iteration: As models are utilized in a particular industry, they must be iterated and versioned. To deal with new and emerging requirements, the models change based on further training or real-world data. MLOps solutions provide capabilities that can create a version of the model as needed, notify users about changes in the version, and maintain model version history.

• Model Monitoring and Management: As the real world and its problems continuously change, it is challenging to keep up with the world where Data scientists still struggle with small data. MLOps solutions help monitor and manage the model's usage, consumption, and results continuously to ensure that accuracy, performance, and other results generated by that model are acceptable.

• Model Governance: Models used in the real world need to be trustworthy. MLOps platforms provide capabilities for audit, compliance, access control, governance, testing and validation, change, and access logs. The logged information can include details related to access control, such as publishing models, why modifications are made, and when models were deployed or used in production.

• Model Security: Models need to be protected from unauthorized access and usage. MLOps solutions can provide the functionality to protect models from being corrupted by infected data, being destroyed by denial of service attacks, or being inappropriately accessed by unauthorized users.

• Model Discovery: The MLOps platform provides model catalogues for produced models and a searchable model marketplace. These model discovery solutions will provide sufficient information to track the data origination, significance, quality transparency of model generation, and other particular model circumstances.

A Holistic Approach 

Now that the list of the most excellent MLOps tools is compiled, all you have to do is figure out how to put them to use in the setup. These tools make it easier to keep track of modifications and model performance, allowing us to focus on domain-specific tuning and model performance. It will continue to improve in the future, with new functionality added to the tools to make the life of data science teams handling the operational side of machine learning projects that much easier.

MLOps helps teams deploy, monitor, and improve models more efficiently, boosting performance and collaboration

• Explore Streamlining ML Projects with MLOPs
• Click to learn about Data Wrangling in Machine Learning
• Read more about Machine Learning in Healthcare

Next Steps for MLOps

Talk to our experts about implementing MLOps practices and how industries and various departments leverage machine learning tools and methodologies to enhance model deployment and management. By adopting a data-driven approach, MLOps streamlines the testing, monitoring, and updating of ML models, optimizing workflows and improving collaboration across teams. This leads to faster model iterations, higher-quality predictions, and more efficient operations.

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