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Deploying Machine Learning Models for Real-Time Predictions Checklist

Alexandra Quinn | April 27, 2023

Deploying trained models takes models from the lab to live environments and ensures they meet business requirements and drive value. Model deployment can bring great value to  organizations, but it is not a simple process, as it involves many phases, stakeholders and different technologies.

In this article, we provide recommendations for data professionals who want to improve and streamline their model deployment process. This list is based on our experience deploying models for large and small organizations across the globe. 

Overview of Deploying a Machine Learning Model

Deployment processes will differ between organizations, depending on their technologies, environments and use cases. However, in most organizations, there are a few steps that will always repeat themselves.

These steps include:

  • Packaging: Adding dependencies and parameters, running scripts and performing builds.
  • Scaling: Managing load balancing, data partitions, model distribution and AutoML.
  • Tuning: Performing data parallelism, managing GPUs, tuning queries and caching.
  • Instrumentation: Monitoring, logging, versioning and managing security.
  • Automation: Of CI/CD, workflows, rolling upgrades and A/B testing.

To minimize friction and errors and increase the chance of deployment success, it is recommended to streamline the machine learning deployment process by automating it as much as possible. An efficient and comprehensive model deployment framework will go a long way to support this.

Data Preparation

One of the most important steps that support successful model deployment is data preparation. In this stage, the data is pre-processed so it can be used in the deployed model. Proper data preparation ensures the model provides accurate and relevant predictions.

Data preparation includes:

  • Cleaning the data and removing any data points that aren’t relevant or are duplicate.
  • Converting the data and transforming it into a format the deployed model can consume.
  • Structuring the data and organizing it so the deployed model can easily access and use it. For example, dividing the dataset into training, validation, and testing subsets, or merging multiple datasets.
  • Validating the data and ensuring it is consistent and accurate for use by the deployed model.

Model Selection

Another critical phase for successful ML deployment is the process of selecting the best model for a given task. This is done based on the models’ performance on the validation dataset. By selecting the best and most effective model (and its corresponding hyperparameters), the organization can ensure the model achieves an optimal level of accuracy.

Model selection includes:

  • Selecting the set of candidate ML models to choose from. The candidate choice will depend on the problem being solved, the available data and the desired level of accuracy and generalization.
  • Training the candidate models on the training dataset.
  • Evaluating and assessing the candidate models’ results.
  • Selecting the best model.
  • Fine-tuning and optimizing the selected model.

Model Deployment Considerations for Your Organization

Now that we’ve covered the model deployment process and its satellite practices, let’s dive into the recommended considerations for deploying your models. Model deployment is both a science and an art. Therefore, by employing best practices and making sure you take important considerations into account, model deployment will be easier, faster, and more cost-effective.

Different professionals will have different ideas as to what constitutes important deployment considerations. This list of our recommendations is based on our expertise working with global enterprises (which of course includes lots of trial and error along the way). With those lessons learned, here are the five considerations we believe are the most essential when deploying machine learning models in production.

1. Adopt a Production-First Approach

A production-first approach means designing and building your ML pipeline with the production deployment and operational requirements in mind, rather than treating deployment as an afterthought. This means ensuring the different pipeline elements provide a continuous, repeatable and automated way to progress from research and development to scalable production pipelines.

In many cases, data scientists actually start the other way around, with model development. Once they have the model, they will train it, monitor its results and, only after they are satisfied, data engineers will proceed to the deployment process. However, this production-last approach is siloed, which might delay deployment, cause model performance degradation and increase operational costs.

Therefore, it is important to prioritize and optimize the entire ML pipeline, including the deployment and operation of machine learning models, from the get-go. This is a resource-efficient approach that will ensure models are seamlessly deployed, which will drive scalability, security, drift avoidance, and above all - business value. Without the need to refactor code, add glue logic, and spend significant efforts on data and ML engineering, models will be more likely to reach production.

2. Integrate a Feature Store

Features are the most important building blocks of ML models. However, creating a new feature is very time-consuming and difficult. A feature store solves this challenge by providing a central hub that enables data scientists to access and reuse features. 

Feature stores provide tools and APIs for ingesting, processing, transforming and serving features, for both online and offline use. They also offer a set of governance and monitoring capabilities to ensure data quality, consistency and compliance.

These capabilities accelerate model development and deployment, eliminate data silos, improve model accuracy, promote collaboration across different teams and projects and eliminate the reliance on glue integrations with training, serving and monitoring frameworks. As a result, the feature store is an important and recommended component of machine learning pipelines.

3. Deploy Models with an ML CI/CD pipeline

A CI/CD pipeline will automatically train, test ML models, optimize and deploy or update models into production. During the CI process, the ML pipeline components are built, tested and packaged for delivery every time changes are made in the source code repository. During the CD process, new ML pipeline implementations are deployed. Then continuous training, model retraining and continuous monitoring take place.

By using a CI/CD pipeline for model deployment, organizations can improve the quality, reliability, and scalability of their machine learning systems, while reducing the time and effort required for deployment and maintenance. This includes faster and more frequent production releases, at scale, while reducing the risk of errors.

Since ML models need to be continuously trained and optimized, the importance of CI/CD becomes even more paramount. Without automated monitoring, training and updating of models, they will quickly become stale, irrelevant and subject to drift.

4. Ensure You Have a Real-time/Event-driven Application Pipeline

A real-time or event-driven application pipeline ingests raw data, handles APIs, prepares and enriches data, serves models, manages ensembles, drives and measures actions, and more, for streaming data in real-time. These actions ensure pipelines can immediately process incoming data in a responsive manner, scale automatically in response to real-time changes in data volume and processing requirements, and provide real-time insights into application performance and data flow, while preventing drift.

As a result, real-time or event-driven application pipelines are recommended for use cases that require real-time decision-making, like fraud prevention and recommendation systems, and any time real-time feature engineering needs to be used.

5. Apply Real-time Data and Model Monitoring

One of the most important considerations of model deployment comes after the deployment itself. This is the phase of model monitoring. Data and model monitoring is the process of analyzing production data, identifying drift, alerting about data anomalies or quality issues and triggering retraining to ensure model freshness. This feedback loop allows for updating models to real-world data, which is constantly changing. In other words, by monitoring and retraining models, you can ensure that they continuously deliver business value when deployed in production.

Additional Deployment Considerations

In addition to these five main considerations, there are additional ML deployment practices we recommend prioritizing. These include:

  • Optimizing the deployment architecture for low latency and quick response times.
  • Ensuring the architecture is scalable and can handle large volumes of data.
  • Implementing security measures to prevent model attacks, which compromise privacy and accuracy.
  • Preparing adequate data for model training and training the model to validate performance.
  • Selecting a model that is appropriate for the business problem, while taking into account accuracy, interpretability, and scalability.
  • Ensuring the architecture can easily adjust computing resources and optimize use of CPUs and GPUs.
  • Ensuring all systems can integrate in a seamless manner with platforms like MLOps, data pipelines, data lakes, and more.

A simplified and effective deployment process depends on multiple factors, including your platforms, practices and culture. By implementing the considerations above, you can significantly accelerate and simplify model deployment and get more business value with less friction. To learn more about simplifying model deployment, book an Iguazio demo.