Model Deployment: 3 Steps & Top Tools in 2024

Developing a machine learning model is just the first step. The hard part? Actually deploying it to deliver business impact.

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Unfortunately, most models never make it that far. According to McKinsey, only 36% of companies successfully move models beyond pilot projects to full production deployment.

As an MLOps consultant who has helped dozens of teams deploy models, I‘ve seen firsthand how difficult it can be. But with the right strategy, it doesn‘t have to be.

In this post, I‘ll share the 3 steps I guide clients through to smoothly deploy models to production. Follow this plan, and you can avoid headaches down the road.

Why Models Get Stuck in Pilot Purgatory

Before diving into the steps, it‘s worth exploring why most models fail to fully deploy. Through my experience, I‘ve seen 4 main pitfalls teams encounter:

1. Lack of monitoring and maintenance

Many teams believe their job ends once a model is deployed. But like any software, models need ongoing monitoring and care. Without this, they degrade rapidly.

2. No deployment automation

Attempting to manually deploy models is labor-intensive and error-prone. Lacking automation, updates take forever and often break things.

3. Not designed for production data

Models trained on clean datasets falter when fed real-world data. If you don‘t test with production data, surprises await.

4. Leadership support evaporates

Without leadership buying in long-term, funding and priorities shift. Teams are left maintaining models no one cares about.

Avoiding these pitfalls is possible, but only with deliberate effort across the full deployment process.

Step 1: Choosing Your Deployment Method

The first decision is whether to use batch or real-time deployment.

Batch inference runs models on batches of accumulated data periodically. This works well when low latency isn‘t required.

For example, an ecommerce site could reforecast inventory demand nightly based on the previous day‘s orders.

Real-time inference generates predictions immediately with no delay. This is necessary when results must be instantaneous.

A product recommendation model on a retail app needs real-time deployment to suggest products as customers browse.

Determining the right deployment approach (Source: Microsoft)

To pick between them, first consider your business needs:

  • Do you need continuous predictions, or can they be batched? Real-time is better if you need always-on capabilities.

  • Do predictions need to be made for individuals, or for groups? Batch works well for population-level predictions.

  • Can you compromise on model complexity? Simpler models allow real-time deployment with fewer resources.

Also factor in technical constraints like networking, compute resources, and software architectures.

Choosing the right method from the start prevents painful rework down the road.

Step 2: Automate Training, Testing, and Deployment

Automating deployment workflows should be a top priority. Manual processes don‘t scale well beyond simple models.

I guide teams through building MLOps pipelines for automation using DevOps principles like continuous integration and delivery.

For example, we can automatically retrain models when new data arrives, test for performance regressions, and deploy the updated model – all with minimal human involvement.

Here‘s what comprehensive automation provides:

  • Reduced costs: No time wasted on mundane deployment tasks.

  • Improved agility: Update models frequently without delays.

  • Enhanced reliability: Automated testing ensures changes don‘t break things.

  • Future-proofing: Automation makes scaling to more models easy.

For real-time deployment, we containerize models using Docker then serve them via Kubernetes. This scales easily to handle unpredictable traffic spikes.

# Example Python prediction code
import bentoml

@bentoml.env(auto_pip_dependencies=True) 
@bentoml.artifacts([PickleArtifact(‘model‘)])
class MyModel(bentoml.BentoService):

  @bentoml.api(input=DataframeInput())
  def predict(self, df):
    results = self.artifacts.model.predict(df)
    return results

For batch workflows, Apache Airflow is a great orchestration tool. We can define workflows to refresh data, retrain, evaluate, and deploy on a schedule.

The right automation makes developing new models and updating existing ones trivial. Don‘t take shortcuts here.

Step 3: Rigorously Monitor Models

deployed model maintenance.

Without monitoring, models slowly degrade from concept drift as data changes. Metrics and alerts allow catching drift early.

We also monitor for silent failures in the ML infrastructure itself – if the deployed model isn‘t even being used, you‘re wasting time and money.

Key signals to watch include:

  • Prediction quality: Is model accuracy decreasing over time?

  • Data drift: How is your data changing compared to the training data?

  • Application performance: Are prediction requests being handled adequately?

  • Application usage: How often is the model being called?

Tooling like Prometheus makes collecting and visualizing these metrics easy. Data quality tests can run continuously to catch issues before they impact customers.

With rigorous monitoring, you can detect degradation before it becomes a problem and improve models over time.

Top Open-Source Deployment Tools

If you‘re looking for deployment tools, here are some popular open-source options:

  • TensorFlow Serving: Deploy TensorFlow models via REST and gRPC
  • TorchServe: Model serving for PyTorch models
  • Seldon Core: Language agnostic model deployment
  • BentoML: Package models for prediction services
  • KFServing: Serverless model deployment on Kubernetes
  • FastAPI: Build performant model APIs in Python

For full MLOps capabilities, commercial platforms like Algorithmia, Comet, and H20.ai are worth exploring.

The key is choosing a tool aligned with your tech stack and use case rather than overengineering. Start simple and evolve as needed.

Deploying models delivers real impact, but most teams struggle to get over the finish line. By following the 3 steps outlined here, you can avoid common pitfalls:

1. Pick the right deployment method for your needs.

2. Automate training, testing, and deployment for efficiency and reliability.

3. Monitor model performance rigorously to detect degradation.

Combined, these best practices set your models up for real-world success. The results? Faster development cycles, lower costs, and models that improve over time through built-in feedback loops.

What challenges have you hit deploying models to production? I‘d love to hear your experiences in the comments below.

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