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Production Machine Learning: Challenges, Considerations, and Best Practices

Published: at 09:47 PM

When learning about machine learning, the focus is often on training and evaluating models using sample datasets. This allows you to experiment with different algorithms and achieve high accuracy on your test data. However, this is very different from deploying a model that performs reliably in a production environment and at scale.

Some key aspects that must be considered when preparing a machine learning system for production include:

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Data Quality and Availability

Having quality, reliable data is perhaps the single most important factor in successfully deploying production ML systems. Without properly vetted data pipelines, even the most advanced models will underperform.

One major difference is the expected quality and availability of data in production environments. When experimenting, you may use clean, well-labeled datasets like MNIST for image classification or synthetic data for prototyping models.

In reality, the data pipelines feeding production models tend to be more complex. Here are some data challenges to address:

Noisy, inconsistent data

Real-world data often contains noise, outliers and inconsistencies. For example:

# Sample noisy data
data = [(1, 20, 19),
        (2, 21, 'NaN'),
        (3, 19, 18),
        (4, 120, 22)]

This requires adding data validation, cleaning, and preprocessing steps:

# Check for null values
import pandas as pd

df = pd.DataFrame(data, columns=['id', 'var1', 'var2'])

# Impute missing values
from sklearn.impute import SimpleImputer

imputer = SimpleImputer(strategy='mean')
imputed_data = imputer.fit_transform(df)

Insufficient or unbalanced data

Many real datasets lack sufficient examples for certain classes or outcomes. This can degrade model performance. Strategies like oversampling minority classes or synthesizing additional training data may be required.

Data drift over time

In production, the underlying data distributions often change gradually, known as data drift. For instance, consumer preferences evolve and demographics shift. Models must be monitored and retrained to adapt accordingly.

Unavailable or delayed data

In practice, data may arrive slowly or become unavailable due to issues like server outages. Production ML systems should be robust to periods of missing data.

Limited storage and memory

Storing and processing massive datasets affordably is an ongoing challenge. Strategies like data compression, streaming analysis, and incremental learning become necessary.

Data availability and integrity

Data pipelines must reliably supply production models with the information required at low latency. Monitoring and alerting help catch upstream issues early. As a best practice, input data should be validated against schema.

By considering these data challenges upfront, you can build more reliable machine learning pipelines for production.

Model Monitoring and Maintenance

Closely monitoring and maintaining production ML models is essential to ensure they perform as expected over time. This includes tracking key performance metrics, monitoring for data drift, and having established processes to seamlessly retrain and deploy updated models.

Here are some best practices for maintaining production ML models:

Performance monitoring

Track key performance metrics like accuracy, F1 score, confusion matrix, and data distributions using ML monitoring tools. These help identify dips indicating when retraining is needed.

Data validation

Continuously validate that input data conforms to expected schema and quality standards. Catching data issues quickly prevents “garbage in, garbage out” scenarios.

Model decay testing

Proactively test models for signs of decay like data drift using techniques like A/B testing a challenger model against the existing production model.

Canary deployments

When deploying model updates, use canary deployments to serve predictions to a small percentage of traffic first. Monitor the canary before ramping up.

Model reproducibility

Containerization and ML pipelines allow retraining models consistently. Use version control for model code, parameters and test data.

Integration testing

Test models thoroughly against real-world scenarios after retraining. Conduct integration testing across services relying on the model.

Rollback procedures

Have automated procedures to quickly rollback models in the event of uncaught issues or degraded performance.

With rigorous monitoring, validation, testing and rollback procedures, production ML systems can be kept robust and current.

Latency and Scalability

Achieving low latency service levels and seamlessly scaling to high volumes of traffic is critical for production ML systems to deliver value.

Proof-of-concept models focus narrowly on maximizing accuracy metrics without regard for latency, throughput or scalability. However, for production use cases, models must meet strict latency service level agreements (SLAs) under heavy load.

Latency Optimization

Several techniques can optimize latency:

Simpler models

Use less complex models (e.g. linear models) with faster inference times when extremely low latency is required.


Reduce model precision from 64 to 8-bit floats for faster inference on specialized hardware like TPUs. However, reduced precision can lower model accuracy, so there is a trade-off between latency and accuracy that must be managed.


Remove redundant model parameters to speed up computation.

# Prune model during training
import tensorflow as tf

model = tf.keras.Sequential([...])

pruning_params = {
  'pruning_schedule': tf.keras.optimizers.schedules.ExponentialDecay(initial_sparsity=0.50,
  								   decay_steps=100, decay_rate=0.90)

              metrics=['accuracy']), pruned_by=tfmot.sparsity.keras.prune_low_magnitude,


Group incoming requests into batches amortizing the overhead of each model query. However, this adds latency for queued requests.


Use performance profiling to identify and optimize bottlenecks in data preprocessing, feature engineering etc.

Hardware acceleration

Leverage GPUs, TPUs and specialized chips for faster inference. Inference on dedicated hardware like NVIDIA T4 GPUs or Google Cloud TPUs reduces latency.


To handle heavy production load, ML systems must scale out efficiently:

Asynchronous processing

Use message queues like Kafka or RabbitMQ to decouple and parallelize steps in the ML pipeline.


Decompose monoliths into independently scalable microservices for data processing, model training, serving etc.

Load balancing

Add load balancers to distribute inference requests across multiple model instances efficiently.


Automatically spin up additional compute resources to handle spikes in traffic using Kubernetes autoscaling.

Batch processing

Use distributed systems like Spark to train models on very large datasets that don’t fit in memory.

Optimizing for low latency and high scalability ensures production ML systems can fulfill their operational requirements.

Explainability and Auditability

With machine learning being used increasingly in sensitive domains like healthcare and finance, model explainability and auditability is becoming crucial to ensure fairness, accountability and transparency.

In applied settings, it’s crucial for stakeholders to understand, audit and trust model behavior. However, many advanced machine learning techniques act as “black boxes”.

Several strategies can improve model transparency:

Explainable AI (XAI) techniques like LIME and SHAP provide local explanations about individual predictions:

import lime
from lime import lime_tabular

explainer = lime_tabular.LimeTabularExplainer(training_data=X_train,

exp = explainer.explain_instance(data_row, predict_fn, num_features=10)

This surfaces the most influential features behind each prediction. However, many advanced models like deep neural networks are complex black boxes. In contexts requiring explainability, choosing transparent, interpretable models from the start is crucial.

Model cards provide details like performance across subgroups, intended use cases and ethical considerations.

MLOps testing includes bias testing, error analysis, and edge case identification as part of model validation.

Regulatory compliance may require retaining explanation data alongside models for auditing. In regulated industries like healthcare and finance, regulations like HIPAA and GDPR may impose requirements for model explainability, fairness, and retaining audit trails.

Algorithmic transparency using inherently interpretable models like linear regression, decision trees or rule-based systems. However, these simpler and more interpretable models may not always provide state-of-the-art performance, so the choice depends on the use case priorities.

Prioritizing explainability helps ensure stakeholders can audit model behavior and minimize risks.

Operationalization and Infrastructure

A solid operational infrastructure combining robust CI/CD pipelines, monitoring tools, and deployment orchestration is fundamental to ensuring production ML systems run reliably.

The final step of taking a model to production is operationalization - the process of deploying the model reliably at scale. This requires an MLOps infrastructure integrating with various systems:

Containers and microservices

Package models and processing logic into containers like Docker for portable deployment across environments.

Kubernetes and REST APIs

Deploy containers onto Kubernetes clusters and expose predictions via REST APIs for integration.

CI/CD pipelines

Automate building, testing and deployment of model changes using Github Actions, Jenkins etc. The choice of deployment tools and infrastructure varies based on the organization’s cloud platform, resources, and specific requirements.

Inference optimization

Use specialized libraries like ONNX Runtime to optimize deployed models for faster inference.


Monitor API requests, model accuracy, data drift and other metrics with tools like Prometheus and Grafana.

Cloud infrastructure

Leverage scalable cloud infrastructure on platforms like AWS SageMaker, GCP AI Platform etc for both training and deployment.


Secure access to training data, models and predictions using mechanisms like role-based access, encryption, VPCs, and authentication. For instance, access controls, network segmentation, encryption, and identity management help protect confidential data, models, and predictions. Adversarial attacks and data poisoning must also be guarded against.

By leveraging MLOps tools and techniques, models can be deployed reliably and cost-effectively at production scale.


This guide outlined several key ways in which developing production-grade machine learning fundamentally differs from simple prototype models:

By considering these applied ML challenges early on, you can develop robust, maintainable and trustworthy ML systems ready for the complexities of production environments. The tools and strategies discussed provide a starting point to build reliable machine learning pipelines.