The Complete Guide to Machine Learning Deployment Success

No one wants to invest the time and resources to develop a model only to see it fail in production. Instead, you want to reliably and efficiently deploy machine learning models into production, enabling scalable, real-time AI that delivers measurable business impact. This article will offer valuable insights to help you achieve your goals and avoid the dreadful scenario of a broken model deployment.

AI inference APIs enable rapid deployment of machine learning models into production, helping businesses deliver predictive insights and value faster.

What is Machine Learning Deployment?

Deploying a machine learning model, or model deployment, simply means integrating it into an existing production environment to take in an input and return an output. The purpose of deploying your model is to make the predictions from a trained machine learning model available to others, whether users, management, or other systems. Model deployment is closely related to machine learning systems architecture, which refers to the arrangement and interactions of software components within a system to achieve a predefined goal.

Why is Model Deployment Important?

Only when a model is deployed does it actively participate in an organization’s ecosystem, automating processes, making predictions, and informing decisions, among other actions. Training a model but failing to deploy it successfully means a business never sees a return on its investment, and customers never get to experience the tangible benefits of the model.

From Prototype to Production Leadership

Being able to deploy a model is also the difference between leading the pack and falling behind in today’s AI-focused environment. According to Gartner, only 48 percent of AI projects reach the production stage, although the number of enterprises that have deployed generative AI applications could exceed 80 percent by 2026. Mastering the model deployment process is necessary if companies want to remain relevant.

Model Deployment Criteria

Before you deploy a model, there are a couple of criteria that your machine learning model needs to achieve before it’s ready for deployment:

Portability

This refers to the ability of your software to be transferred from one machine or system to another. A portable model has a relatively low response time and can be rewritten with minimal effort.

Scalability

This refers to how large your model can scale. A scalable model does not need to be redesigned to maintain its performance. This will all take place in a production environment, a term used to describe the setting where software and other products are operated for their intended uses by end users.

Related Reading

• CPU Acceleration
• AI Workloads

System Architecture for ML Model Deployment

The data layer provides access to all the data sources the model requires. This includes:

• Raw data
• Pre-processed data
• Additional data sources needed for generating features

This ensures the model has access to up-to-date and relevant data, enabling it to make accurate predictions.

Components of the Data Layer

• Data Storage: Databases, data lakes, or cloud storage solutions where data is stored and managed.
• Data Ingestion: Pipelines and tools facilitate collecting, extracting, and loading data from various sources.

The Feature Layer: Preparing Model Inputs for Production

The feature layer is responsible for generating feature data in a transparent, scalable, and usable manner. Features are the inputs used by the ML model to make predictions. This layer ensures that features are generated consistently and efficiently, enabling the model to perform accurately and reliably.

Components of the Feature Layer

• Feature Engineering: Processes and algorithms used to transform raw data into meaningful features.
• Feature Storage: Databases or storage systems where features are stored and accessed.

The Scoring Layer: Making Predictions

The scoring layer transforms features into predictions. This is where the trained ML model processes the input features and generates outputs. Based on the input features, real-time or batch predictions are produced, enabling automated decision-making and insights.

Components of the Scoring Layer

• Model Serving: Infrastructure and tools that host the ML model and handle prediction requests.
• Prediction APIs: Interfaces that allow other systems to interact with the model and retrieve predictions.
• Common Tools: Scikit-Learn is most commonly used and is the industry standard for scoring.

The Evaluation Layer: Monitoring Model Performance

The evaluation layer checks the equivalence of two models and monitors production models. It is used to compare how closely the training predictions match the predictions on live traffic. This ensures that the model remains accurate and reliable over time and detects any degradation in performance.

Components of the Evaluation Layer

• Model Monitoring: Tools and processes that track model performance in real-time.
• Model Comparison: Techniques to compare the performance of different models or versions of the same model.
• Metrics and Logging: Systems that log predictions, track metrics, and provide alerts for anomalies.

Related Reading

• AI Cloud Computing
• Edge AI vs. Cloud AI
• GPU vs. CPU for AI
• Edge Inference

5 Machine Learning Deployment Methods to Know

1. One-Off Deployment: A Simple Approach for Static Data

One-off deployment allows a deployed model to generate predictions on a specific dataset at a particular point in time. This deployment method is often used for initial testing or when predictions are needed for a static dataset. It’s simple and requires minimal infrastructure but it is unsuitable for ongoing or real-time predictions.

2. Batch Deployment: Efficient Predictions for Large Datasets

Batch deployment processes a large set of data at regular intervals. The model is applied to batches of data to generate predictions, which are then used for analysis or decision-making. This method is efficient for handling large volumes of data and is easier to manage and monitor.

Nevertheless, there is a latency between data collection and prediction, making it unsuitable for real-time applications.

3. Real-Time Deployment: Get Instant Predictions on New Data

Real-time deployment involves making predictions instantly as new data arrives. This requires the model to be integrated into a system that can handle real-time data input and output. It is ideal for applications like live customer support chatbots, real-time recommendations, and autonomous vehicles.

The main advantage is immediate predictions and actions, which enhance the user experience and engagement. Still, it requires robust infrastructure and low-latency systems, so it’s more complex to implement and maintain.

4. Streaming Deployment: Continuous Predictions for Data in Motion

Streaming deployment is designed for continuous data streams, processing data as it flows in and providing near-instantaneous predictions. This method is used in financial market analysis, real-time monitoring, and IoT sensor data processing. The downside is that it requires high infrastructure and maintenance costs, specialized tools, and technologies.

5. Edge Deployment: Localized Predictions for Enhanced Security

Edge deployment involves deploying the model on edge devices like smartphones, IoT devices, or embedded systems. The model runs locally on the device, reducing the need for constant connectivity to a central server. This method is used for predictive maintenance, personalized mobile app experiences, and autonomous drones.

Reducing Data Exposure

Processing data locally on the edge device minimizes the need to transmit sensitive data to external servers or the cloud. This reduces the attack surface and potential points of vulnerability. Even if an attacker gains access to the communication channel, they would only have access to the limited data being transmitted rather than the entire dataset stored on a central server.

Enhanced Control and Regulatory Compliance

Edge deployment also allows organizations to maintain better control over their data and ensure compliance with data protection regulations, such as GDPR or HIPAA. Organizations can avoid the complexities and risks associated with storing and processing data in external environments by keeping data within the device and processing it locally.

Making ML Model Deployment More Efficient

Deployed ML models provide incremental learning for online learning machines that adapts models to changing environments to make predictions in near real-time. As we alluded to above, the general ML model deployment process can be summarized in four key steps:

1. Develop and Create a Model in a Training Environment

You must build your model before deploying a machine learning application. ML teams tend to create several models for a single project, but only a few make it to the deployment phase.

These models will usually be built in an offline training environment, either through a supervised or unsupervised process, where they are fed with training data as part of the development process.

2. Optimize and Test Code, then Clean and Test Again

When a model has been built, the next step is to check that the code is of good enough quality to deploy. If it isn’t, cleaning and optimizing it before re-testing is essential, and this should be repeated where necessary.

Transparency in Deployment

Doing so ensures that the ML model will function in a live environment and allows others in the organization to understand how the model was built. This is important because ML teams do not work in isolation; others must look at, scrutinize, and streamline the code as part of the development process. Therefore, accurately explaining the model’s production process and results is key.

3. Prepare for Container Deployment

Containerization is an essential tool for ML deployment, and ML teams should put their models into a container before deployment. Containers are predictable, repetitive, immutable, and easy to coordinate, making them the perfect environment for deployment.

Over the years, containers have become highly popular for ML model deployment because they simplify deployment and scaling. Containerized ML models are also easy to modify and update, which mitigates the risk of downtime and makes model maintenance less challenging.

4. Plan for Continuous Monitoring and Maintenance

The key to successful ML model deployment is ongoing monitoring, maintenance, and governance. Merely ensuring that the model is initially working in a live setting is not enough; continuous monitoring helps to ensure that the model will be effective for the long term.

Sustaining Model Performance

Beyond ML model development, ML teams need to establish processes for effective monitoring and optimization to keep models in the best condition. Once continuous monitoring processes have been planned and implemented, data drift, inefficiencies, and bias can be detected and rectified.

Depending on the ML model, it may also be possible to regularly retrain it with new data to avoid the model drifting too far away from the live data.

Potential ML Model Deployment Challenges

ML model development is invariably resource-intensive and complex. Taking a model that has been developed in an offline environment and integrating it into a live environment will always bring with it new risks and challenges, including:

Knowledge

An ML model is typically not built and deployed by the same team; data scientist teams build it while developer teams deploy it. Bridging the gap between the two is a significant challenge because skills and experience may not overlap between the two distinct areas.

Infrastructure

A lack of robust infrastructure can make ML deployment more challenging. This slows the process because infrastructure must be built, which can lead to a model being unnecessarily retrained with fresh data.

Scale

It is essential to recognize that your model will likely need to grow over time, and scaling it to meet the need for increased capacity adds another level of complexity to the ML deployment process.

Monitoring

The model's ongoing effectiveness also presents a potential challenge. As mentioned, models should be continuously monitored and tested after deployment to ensure accurate results and drive performance improvements.

Simplifying the ML Model Deployment Journey

What if we told you that deploying your ML models could be as easy as following three simple steps? It’s true! Here are our tips for deploying your model and avoiding many of the challenges at the same time:

1. Decide on a Deployment Method

The first step is to decide which deployment method to use. There are two main ones: batch inference and online inference.

Batch inference

This method runs periodically and provides results for the batch of new data generated since the previous run. It generates answers with latency and is, therefore, useful where model results are not needed immediately or in real time. The main benefit of batch inference is the ability to deploy more complex models.

Online inference

Also known as real-time inference, this method provides results in real time. While this sounds like the better method, it has an inherent latency constraint limiting the type of ML models deployed. Since results are provided in real time, deploying complex models with online inference is impossible.

When deciding which method to use, consider questions like:

• How often do we need our model to generate predictions?
• Should model results be based on batch data or individual cases?
• How much computational power can we allocate?
• How complex is our model?

2. Automate Deployment and Testing

It is possible to manually manage the deployment and testing of a single, small model. Nevertheless, you should automate for larger or multiple models at scale.

Orchestrated Efficiency

This will enable you to manage individual components more easily, ensure that ML models are automatically trained with consistently high-quality data, run automatic testing (e.g., data quality and model performance), and automatically scale models in response to current conditions.

3. Monitor, Monitor, Monitor

As we have already covered, a successful deployment process lives and dies with continuous monitoring and improvement. This is because ML models degrade over time, and constant tracking means you can highlight potential issues such as model drift and training-serving skew before they cause damage.

Important considerations

• Initial Data Flow Design: Before collecting data, you must architect a data flow that can handle training and prediction data. This involves deciding how data will be ingested, processed, and eventually fed into Evidently for monitoring.
• Data Storage Strategy: Where you store this integrated data is crucial. You'll need a storage solution that allows for easy retrieval and is scalable, especially when dealing with large volumes of real-time data.
• Automated Workflows: Consider automating the data flow from Seldon and your training data source to Evidently. This could involve setting up automated ETL jobs or utilizing orchestration tools to ensure data is consistently fed into the monitoring tool.

Related Reading

• Pros and Cons of Serverless Architecture
• Edge AI Examples

Start Building with $10 in Free API Credits Today!

OpenAI is the undisputed leader in text generation. Their flagship GPT-3 family of large language models (LLMs) has several versions, including Codex for code generation and fine-tuned models for various other applications. Inference delivers OpenAI-compatible serverless inference APIs for top open-source LLM models, offering developers the highest performance at the lowest cost in the market.

Leveraging Inference for Scalable AI and RAG Applications

Beyond standard inference, Inference provides specialized batch processing for large-scale async AI workloads and document extraction capabilities designed explicitly for RAG applications.

Start building with $10 in free API credits and experience state-of-the-art language models that balance cost-efficiency with high performance.