20 Machine Learning Best Practices for a Robust, Scalable Pipeline

Imagine you've built a powerful machine learning model that can solve a complex problem in your organization. But once you deploy the model into production, it doesn’t deliver the expected results. Instead, it fails, or worse, it produces inaccurate predictions that negatively impact your business operations. This scenario is, unfortunately, common. But organizations can avoid this messy outcome by following machine learning best practices. This article will explore the best practices for building a scalable, efficient, and reliable machine learning pipeline that minimizes failures, accelerates deployment, and delivers high-performing models in production. Additionally, understanding AI Inference vs Training is crucial to optimizing model performance and ensuring smooth deployment.

AI inference APIs are valuable tools to help organizations achieve their objectives and implement best practices for machine learning. Inference’s APIs streamline operations so that machine learning models produce accurate predictions for real-world applications.

What are the Common Pitfalls to Avoid When Building ML Pipelines?

In real life, the machine learning model is not a standalone object that only produces a prediction. It is part of an extended system that can only provide values if we manage it together. We need the machine learning (ML) pipeline to operate the model and deliver value.

Core ML Pipeline Stages

Building an ML pipeline would require us to understand the end-to-end process of the machine learning lifecycle. This basic lifecycle includes data collection, preprocessing, model training, validation, deployment, and monitoring. In addition to these processes, the pipeline should provide an automated workflow that works continuously in our favor.

Robust Pipeline Design

An ML pipeline requires extensive planning to remain robust at all times. The key to maintaining this robustness is structuring the pipeline well and keeping the process reliable in each stage, even when the environment changes. Nevertheless, there are still a lot of pitfalls we need to avoid while building a robust ML pipeline:

Ignoring Data Quality Issues

In machine learning, data serves as the foundation for building accurate models. So, it should come as no surprise that the quality of this data directly impacts your model’s performance. Sometimes, we are fortunate enough to collect and use data from a data warehouse or source that we do not need to validate.

Data Quality Pitfalls

Still, it’s easy to overlook the quality of this data. The problem arises when we ignore the data quality and assume it is good enough to use immediately. This oversight can lead to disastrous results. Machine learning model and prediction quality equals the data quality we put in. There’s a saying you’ve undoubtedly heard: “Garbage in, garbage out.”

Ensuring Data Suitability

If we put low-quality data into the model, the results will also be low-quality. That’s why we must ensure our data suits the business problem we are trying to solve. We need the data to have a clear definition, ensure that the data source is appropriate, and ensure the data is cleaned meticulously and prepared for the training process.

Aligning our process with the business and understanding the relevant preprocessing techniques are necessary.

Overcomplicating the Model

You’re likely familiar with Occam’s Razor, the idea that the simplest solution usually works the best. This notion also applies to the model we use to solve our business problem. Many believe that the more complex the model, the better the performance. Nevertheless, this is not always true.

Sometimes, using a complex model such as deep learning is even overkill when a linear model such as logistic regression works well.

Balancing Complexity and Cost

An overcomplicated model could lead to higher resource consumption, which could outweigh the value of the model it should provide. The best advice is to start simple and gauge model performance. If a simple model is sufficient, we don’t need to push for a more complex one. Only progress to a more complicated approach if necessary.

Inadequate Monitoring of Production

We want our model to continue providing value to the business, but that would be impossible if we used the same model and never updated it. It would worsen if the model in question had never been monitored and remained unchanged. The problem situation may constantly change, meaning the model input data also does.

Model Drift and Degradation or The Need for Continuous Monitoring

The distribution could change with time, and these patterns could lead to different inferences. There could even be additional data to consider. If we do not monitor our model regarding these potential changes, the model degradation will go unnoticed, worsening overall model performance.

Use available tools to monitor the model’s performances and have notification processes in place for when degradation occurs.

Not Versioning Data and Models

A data science project must be an ever-continuous, living organism if we want it to provide value to the business. This means that the dataset and model we use must be updated. Nevertheless, updating doesn’t necessarily mean the latest version will constantly improve. That’s why we want to version our data and models to ensure we can always switch back to conditions already proven to work well.

Maintaining Model Relevance

Without proper versioning of the data and the model, it would be hard to reproduce the desired result and understand the changes’ impacts. Versioning might not initially be part of our project’s plan, but the machine learning pipeline would eventually benefit from it. Try using Git and DVC to help with this transition.

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20 Machine Learning Best Practices for a Robust Pipeline

Objective & Metric Best Practices: Defining Success Before You Start

The first obvious step is defining the business objective before beginning the ML model design. Many times, ML models are started without clearly defined goals. Such models are set for failure because the ML models need to be clearly defined:

• Goals
• Parameters
• Metrics

Organizations may not be aware of setting specific objectives for ML models. They may want to find insights based on the available data, but a vague goal is insufficient to develop a successful ML model. You must be clear about your objective and the metric to measure success. Otherwise, you’ll waste time on the wrong thing or chase an impossible goal.

Here are some objective best practices to keep in mind when designing the objectives of your machine learning solutions:

1. Ensure The ML Model Is Necessary

While many organizations want to follow the ML trend, the machine learning model may not be profitable. Before investing time and resources into developing an ML model, you need to identify the problem and evaluate whether machine learning and MLOps will be helpful in the specific use case.

Small-scale businesses must be even more careful because ML models require resources that may not be available. Identifying areas of difficulty and having relevant data to implement machine learning solutions is the first step to developing a successful model. It is the only way to improve the organization’s profitability.

2. Collect Data For The Chosen Objective

Even though use cases are identified, data availability is the crucial driving factor in determining the successful implementation of the ML model. An organization’s first ML model should be simple, but it should choose objectives supported by a large amount of data.

3. Develop Simple & Scalable Metrics

Begin by constructing use cases for which the ML model must be created. Technical and business metrics must be developed based on the use cases. The ML model can perform better when there is a clear objective and metrics to measure those objectives.

To meet the business goal, the current process must be reviewed thoroughly. Understanding where the current process faces challenges is the key to automation. Identifying deep learning techniques that can solve the current challenges is crucial.

Infrastructure Best Practices: Getting Your House in Order

Before investing time and effort in building an ML model, you must ensure that the infrastructure is in place to support it. The infrastructure available significantly affects a machine learning solution’s building, training, and production.

The best practice is to create an encapsulated ML model that is self-sufficient. The infrastructure should not depend on the ML model, which allows multiple features to be built later. Models must be tested and sanity checked before deployment.

Here are some infrastructure best practices to keep in mind when designing your machine learning solutions:

4. Right Infrastructure Components

The ML infrastructure includes various components, associated processes, and proposed solutions for the ML models. Incorporating machine learning in business practices entails growing the infrastructure with AI technology.

Businesses should not spend money building the complete infrastructure before ML model development. Multiple aspects must be implemented stepwise to allow maximum scalability, such as:

• Containers
• Orchestration tools
• Hybrid environments
• Multi-cloud environments
• Agile architecture

5. Cloud-based vs. On-premise Infrastructure

For enterprises adopting machine learning, leveraging cloud infrastructure initially is often the most practical approach due to its:

• Cost efficiency
• Scalability
• Low maintenance

Leading providers offer robust, ML-optimized platforms with preconfigured infrastructure elements that accelerate deployment, like:

• Google Cloud Platform (GCP)
• AWS
• Microsoft Azure

While cloud-based solutions minimize upfront costs and support dynamic scaling through varied compute clusters, on-premise infrastructure, such as systems from Lambda Labs or custom-built NVIDIA workstations, offers enhanced control and security. Though it demands significant initial investment, in-house infrastructure becomes advantageous when handling sensitive data or deploying multiple ML models at scale.

A hybrid approach that strategically blends cloud and on-premise resources often delivers, for enterprise-level ML operations, the best:

• Flexibility
• Performance
• Security balance

6. Make the Infrastructure Scalable

The proper infrastructure for the ML model depends on business practices and future goals. Infrastructure should support separate training models and serving models. This enables you to continue testing your model with advanced features without affecting the deployed serving model. Microservices architecture is instrumental in achieving encapsulated models.

Data Best Practices: Preparing Your Data for Machine Learning Success

Extensive data processing is critical to developing successful ML models. The data determines the system’s goal and plays a significant role in training ML algorithms. The performance and evaluation of the model can’t be completed without appropriate data.

7. Understand Data Quantity Significance

Building ML models is possible when there is a massive volume of data. Raw data is crude, but you must extract usable information before creating an ML model. Data gathering should begin with the existing system in the organization. This will give you the data metrics needed to make the ML model.

When data availability is minimal, you can use transfer learning to gather as much data as possible. Once raw data is available, you must deploy feature engineering to pre-process it. Collected data must undergo necessary transformations to be valuable as training data.

8. Data Processing Is Crucial

The first step in data processing is data collection and preparation. Feature engineering should be applied during pre-processing to correlate essential features with available data. Data wrangling metrics must be used during the interactive data analysis phase. Exploratory data analysis exploits data visualization to understand data, perform sanity checks, and validate the data. When the data process matures, data engineers incorporate continuous data ingestion and appropriate transformations to multiple data analytics entities.

Data validation is required for model training at every ML pipeline or data pipeline iteration. When data drift is identified, the ML model requires retraining. If data anomalies are detected, the pipeline execution must be stopped until the anomalies are addressed.

9. Prepare Data For Use Throughout the ML Lifecycle

Understanding and implementing best practices in data science play a significant role in preparing the data for machine learning solutions. The datasets must be categorized based on features and documented for use throughout the ML lifecycle.

Model Best Practices: Choosing the Right Model for Your Project

When data and infrastructure are ready, choosing the perfect ML model is time-consuming. Multiple teams work with various technologies, which may or may not overlap. You need to select an ML model that can support existing technologies. Data science experts don’t have programming expertise, and they may be using outdated technology stacks.

On the other hand, software engineers may use the latest and most experimental technologies to achieve the best results. The ML model must support old models while making room for newer technologies. The selected technology stacks must be cloud-ready, even though in-house servers are currently used.

10. Develop a Robust Model

Validation, testing, and monitoring of ML models are crucial in the ML model pipeline. Model validation should ideally be completed before the model goes into production. The robustness metric should become an essential benchmark for model validation. Based on the robustness metrics, model selection should be made.

If the robustness of the chosen model can’t be improved to meet benchmark standards, it must be dropped, and a different ML model must be picked. Defining and creating usable test cases is crucial for continuous ML model training.

11. Develop & Document Model Training Metrics

Building incremental models with checkpoints will make your machine learning framework resilient. Data science involves numerous metrics, which can be confusing. Performance metrics should always take precedence over fancy metrics.

An ML model requires continuous training, and the serving model data should be used with each iteration. Production data is helpful in the beginning stage, and using serving model data to train ML models will make them easier to deploy in real-time.

12. Fine-Tune The Serving ML Model

Serving models require continuous monitoring to catch errors in the early phase. This requires a human in the loop because acceptable incidents must be identified and allowed. Periodic monitoring must be scheduled in the serving phase of the ML model to ensure that the model behaves exactly as it is expected to behave.

The user feedback loop must be integrated into the model maintenance to develop a strong incident response plan.

13. Monitor and Optimize Model Training Strategy

Extensive training is required to achieve success with model production. Continuous training and integration will ensure the ML model is profitable for solving business problems. The model accuracy may fluctuate with the initial training batch, but subsequent batches that use service model data will provide greater precision. All the object instances must be complete and consistent to optimize the training strategy.

Code Best Practices: Writing Production-Ready Code for Machine Learning

Developing MLOps involves writing code in multiple languages. The code must execute effectively in different stages of the ML pipeline. Data scientists and software engineers must work together to:

• Read
• Write
• Implement ML model codes

The codebase unit tests will test the individual features. Continuous integration will enable pipeline testing, guaranteeing that coding changes will not break the model.

14. Follow Naming Conventions

Development engineers keen on making their code run often ignore naming conventions. As ML models require continuous modifications in coding, changing anything anywhere results in changing everything everywhere.

The naming conventions will help the entire development engineering team to understand and identify multiple variables and their roles in model development.

15. Ensure Optimal Code Quality

Code quality checks are mandatory to ensure the code does what it should. The code shouldn’t introduce errors or bugs in the existing system. Depending on the ML model requirement, it should also be easy to read, maintain, and extend. Throughout the ML pipeline, a Uniform coding style will help catch and eliminate bugs before the production stage.

Dead and duplicate codes are easily identifiable when the engineers follow a standard coding style. Constant experimentation with different code combinations is unavoidable in improving the ML model. A proper code tracking system should be in place to correlate experiments and their results.

66. Write Production-Ready Code

The ML model requires complex coding, but you should write production-ready code to make the model competent. Reproducible code with version control is easier to deploy and test. Pipeline framework adaptation is crucial to creating modular code that allows continuous integration.

The best ML model code uses a standard structure and coding style convention. Every aspect of coding must be documented using appropriate documentation tools. The systematic coding approach should be stored to identify code versions easily:

• Training code
• Model parameters
• Data sets
• Hardware
• Environment

17. Deploy Models in Containers for Easier Integration

A clear understanding of the working model is crucial to integrating the ML model into company operations. Once the prototype is complete, there should be no delay in deploying the model. The best practice is to use containerization platforms to create multiple services in isolated containers. The containers’ instances are deployed on demand and trained using real-time data.

Limit one application per container for easier debugging. A containerized approach makes the ML models reproducible and scalable across various environments. If the features are encapsulated, engineering teams can easily start producing models. It also allows for individualized training without affecting the existing production.

18. Incorporate Automation Wherever Possible

The ML models require consistent testing and integration when new features are included or new data becomes available. Multiple unit tests with varying test cases are essential to ensure the machine learning application works as intended.

Automated testing dramatically reduces the manual labor required to complete the coding. Integration testing automation helps ensure a single change is reflected in the ML model code.

19. Low-Code/ No-Code Platform

Low-code and no-code machine learning platforms reduce the coding involved, enabling data scientists to introduce new features without affecting development engineers.

While these platforms provide flexibility and quick deployment, the level of customization achieved is still low compared to handwritten code. As the complexity of ML models increases, development engineers become more involved in writing machine learning code.

20. Understanding the Production Environment

It is essential to design models and deployment pipelines compatible with the production environment, whether:

• Cloud-based
• On-premise
• Edge devices
• Hybrid system

Best practices:

• Environment Compatibility: Ensure your model’s dependencies are compatible with production environments. Test in environments that closely mimic production to avoid unforeseen issues when moving from development to production.
• Containerization: Docker and other containerization technologies allow you to package models and dependencies into portable, isolated environments. Containers enable consistency across different platforms and ease scaling, rollout, and rollback.
• Framework and Language Consistency: Use the same ML frameworks and versions across development and production. For instance, a model trained in TensorFlow should ideally be deployed using a TensorFlow Serving-compatible setup.

21. Model Versioning and Reproducibility

Ensure each model version is traceable and reproducible to facilitate:

• Troubleshooting
• Auditing
• Rollback

Best practices:

• Model Versioning: Assign unique versions to each model iteration, and store all metadata associated with training, such as hyperparameters, training data snapshots, and evaluation metrics. Tools like MLflow, DVC, or internal model registries help manage this effectively.
• Code Reproducibility: Use version control for your code, dependencies, and environment configurations (e.g., requirements.txt for Python). Ideally, integrate CI/CD pipelines to test each change in code, ensuring new versions are tested rigorously.
• Data Versioning: The data used to train each version of the model should be versioned and stored. Platforms like DVC (Data Version Control) make handling data snapshots in sync with model versions easier, ensuring reproducibility and data consistency.

22. Model Pipeline Automation (CI/CD)

Establish CI/CD pipelines to automate the deployment process, from code validation to model deployment and monitoring setup.

Best practices:

• Pipeline Stages: Split your pipeline into stages:
  • Data extraction
  • Preprocessing
  • Training
  • Testing
  • Deployment
  • Monitoring
• Automated Testing: Automate unit, integration, and model performance tests for each pipeline stage. Unit tests validate code functionality, integration tests ensure modules work together, and performance tests evaluate model quality.
• Deployment Triggers: Use triggers for retraining and redeployment based on model drift or new data availability. For example, schedule retraining or updating based on a regular cadence (e.g., daily or weekly) or specific triggers (data thresholds, concept drift).

23. Scalability and Reliability

Design systems that scale elastically and are resilient to failures.

Best practices:

• Horizontal Scaling: Use horizontal scaling to handle an increase in demand. For instance, load balancers can help distribute traffic across multiple instances of your model service.
• Batch vs. Real-Time Inference: Decide if your model needs to handle real-time predictions or if batch processing is feasible. Real-time processing may require low-latency architectures like gRPC APIs, while batch jobs could handle batch processing on a schedule.
• Redundancy and Failover: Incorporate redundancy to mitigate failures. For example, replicate model instances across multiple availability zones using cloud services and have failover mechanisms for high availability.
• Autoscaling: Configure autoscaling based on demand, so resources are allocated efficiently. Cloud providers like AWS, GCP, and Azure offer autoscaling policies to help scale resources according to traffic.

24. Security and Compliance

Ensure models and data handling comply with industry regulations and are protected from unauthorized access.

Best practices:

• Data Encryption: Use encryption protocols for data at rest and in transit. Ensure sensitive information is adequately anonymized or masked to prevent leakage.
• Access Control: Define roles and permissions to restrict access to model services and data. Use identity and access management solutions to authenticate and authorize users and applications accessing your model.
• Audit Trails: Implement audit logging for every action on models and data. This is particularly crucial in regulated industries where compliance is essential.
• Regular Security Audits: Schedule periodic security audits and vulnerability scans. Penetration testing and security assessments help identify and fix potential vulnerabilities.

25. Retraining and Continuous Learning

Adapt your models over time with new data and changing user patterns through continuous learning mechanisms.

Best practices:

• Scheduled Retraining: Based on model performance monitoring, define a schedule for retraining. Retraining workflows are initiated when significant data drift or performance degradation is detected.
• Active Learning: Identify low-confidence predictions and use them in active learning loops, where only these predictions are sent for manual labeling and used for retraining.
• Feedback Loop Integration: Collect feedback from users (where applicable) to help retrain and improve models. For instance, in recommendation systems, user clicks and engagement can act as feedback for future updates.

26. Model Governance and Ethical Considerations

Define ethical, unbiased, and transparent model governance processes, especially for high-stakes applications.

Best practices:

• Bias Detection and Mitigation: Conduct bias checks on the model to ensure fairness and prevent discriminatory patterns. Use metrics like demographic parity or disparate impact to measure potential biases in model outcomes.
• Explainability and Transparency: Implement model interpretability techniques to make predictions understandable to stakeholders. Use SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), or feature importance for this purpose.
• Documenting Decisions and Changes: Document every major model change, including the reasoning behind decisions. This documentation should cover how the data was sourced, preprocessing choices, and any ethical considerations taken into account.

27. Managing Technical Debt

Address the accumulation of shortcuts and hacks over time that could hinder model performance and maintainability.

Best practices:

• Refactoring: Regularly refactor code to improve readability and maintainability. Remove deprecated libraries, streamline feature engineering code, and ensure model pipelines are clean and modular.
• Addressing Feature Creep:
• Edge Inference
• LLM Inference Optimization

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Related Reading

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Use only relevant features to avoid unnecessary complexity. Track and periodically assess feature importance and remove those with little to no predictive value.
• Regular Debt Reviews: Schedule regular reviews to assess technical debt and allocate time and resources to manage debt effectively.

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