The Definitive Guide to Deploying ML Models with Inferrd in 2026

Developing machine learning models is one thing—deploying them to production so they can deliver real business value is a whole other challenge. The model deployment process can be complex, time-consuming, and require coordination between data scientists, ML engineers, and DevOps. Issues like environment setup, dependency management, model monitoring, and scaling can quickly bog teams down.

Fortunately, new tools like Inferrd are emerging to simplify and accelerate ML model deployment. Inferrd is a cloud platform that enables data scientists to easily deploy models and make them production-ready without worrying about infrastructure. In this comprehensive guide, we‘ll walk through everything you need to know about deploying ML models with Inferrd in 2023.

What is Inferrd?

Inferrd is a model deployment platform that allows you to host ML models in the cloud and use them to power intelligent applications. Some of the key features of Inferrd include:

• Support for deploying models from popular ML frameworks like scikit-learn, Keras, TensorFlow, PyTorch, and Spacy
• Automatic provisioning of compute resources including GPUs to serve models at scale
• Versioning and A/B testing functionality to experiment with model updates
• Real-time monitoring and logging of model predictions and performance metrics
• REST API and client libraries (Python, Node.js) for programmatic access to hosted models
• Pay-as-you-go pricing based on API requests

In essence, Inferrd provides ML teams with a simple, managed solution for moving models from development to production. Rather than dealing with the hassle of configuring model servers, load balancers, and auto-scaling, you can deploy models with a few lines of code and let Inferrd handle the rest.

Deploying Models on Inferrd

Now that we‘ve covered the basics of Inferrd, let‘s dive into the actual model deployment process. Inferrd supports several of the most popular ML frameworks, and the steps vary slightly depending on which one you‘re using.

Scikit-Learn Models

Inferrd has built-in support for deploying scikit-learn models. First, make sure you have the inferrd Python package installed:

pip install inferrd

Then authenticate with your Inferrd account API key:

import inferrd

inferrd.auth(‘YOUR_API_KEY‘)

To deploy a scikit-learn model, just pass the fitted model object to the deploy_scikit function along with a name for the deployment:

from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier()
model.fit(X_train, y_train)

inferrd.deploy_scikit(model, ‘fraud-detection-rf‘)

Inferrd will serialize the model, upload it to the cloud, and provision the necessary resources to serve it. You‘ll receive an email notification once the deployment is live.

Keras/TensorFlow Models

For Keras and TensorFlow models, you first need to export your trained model to a SavedModel format:

model = ... # define and train your model

model.save(‘path/to/model‘)

This will generate a folder containing the model architecture, weights, and assets. To deploy the exported model on Inferrd, simply drag-and-drop the folder into the Inferrd dashboard or use the Python client:

import inferrd
inferrd.deploy_tf(‘path/to/model‘, ‘keras-model-test‘) 

PyTorch Models

Deploying PyTorch models on Inferrd is very similar to Keras/TensorFlow. First, save your model using torch.save:

model = ... # define and train your PyTorch model
torch.save(model.state_dict(), ‘model.pth‘)

Then provide the path to the exported model file when deploying on Inferrd:

inferrd.deploy_pytorch(‘model.pth‘, ‘pytorch-classifier‘)

Spacy Models

For natural language models developed with Spacy, Inferrd supports deploying the entire processed pipeline. Point the deploy_spacy function to your trained nlp object:

import spacy

nlp = spacy.load(‘en_core_web_lg‘) 

inferrd.deploy_spacy(nlp, ‘spacy-ner-pipeline‘)

Inferrd will serialize the Spacy pipeline and all of its sub-components (tokenizer, tagger, parser, NER, etc.) so you can use the deployed model for end-to-end text processing.

Performing Inference on Deployed Models

Once your model is deployed and live on Inferrd, there are a few different ways to perform inference and generate predictions from it:

1. Inferrd Dashboard – The dashboard provides a simple web UI where you can manually input data and view the model‘s output. This is great for quick testing and demos.

2. Inferrd API – Every deployed model on Inferrd gets a dedicated REST API endpoint for programmatic access. You can send input data as JSON to the /predict route and get back the model‘s predictions.

3. Inferrd Client Libraries – Inferrd provides official client SDKs in Python and Node.js so you can easily integrate deployed models into your applications. Here‘s an example of using the Python client to perform inference:

import inferrd

model = inferrd.get(‘MODEL_ID‘)

inputs = ... # prepare input data 
prediction = model(inputs)

The SDK handles the process of serializing inputs, sending the request to the deployment endpoint, and parsing the response.

Integrations

Along with its core deployment features, Inferrd also offers built-in integrations with other popular ML tools and platforms. One notable integration is with Hugging Face, the leading community for natural language processing models.

You can directly deploy pre-trained models from the Hugging Face Hub on Inferrd with a few clicks. This allows you to leverage state-of-the-art NLP models like BERT, GPT, T5, etc. without worrying about hosting or scaling them yourself.

Inferrd also integrates with experiment tracking and workflow orchestration tools like MLflow and Kubeflow. You can configure these tools to automatically kick off model deployment on Inferrd as the final step in your training pipeline.

Use Cases

To further illustrate the capabilities of Inferrd, let‘s walk through a couple real-world examples of models that were deployed using the platform:

1. Anomaly Detection – A Fortune 500 manufacturing company used Inferrd to deploy an unsupervised anomaly detection model for predictive maintenance of factory equipment. The model ingests sensor data from the equipment in real-time and flags potential failures before they occur. Inferrd handles the streaming inference and automatically scales the model to handle spikes in throughput.

2. Image Classification – A agriculture technology startup used Inferrd to deploy a convolutional neural network for detecting crop diseases from smartphone photos. The model was built using TensorFlow and trained on millions of labeled images from fields. With Inferrd, the team was able to seamlessly integrate the trained model into their mobile app so that farmers could quickly diagnose issues with their crops.

These are just a couple examples, but Inferrd is applicable across industries and use cases. From fraud detection to content recommendation to predictive analytics, if you have an ML model, Inferrd can help you get it production-ready.

Advantages and Tradeoffs

So what are the main benefits of using Inferrd compared to other ways of deploying models? Here are some of the key advantages:

• Simplified workflow – Inferrd abstracts away the tedious, time-consuming parts of model deployment like provisioning servers, setting up Docker containers, configuring Kubernetes, etc. With Inferrd, you can go from trained model to live API endpoint in minutes.

• Autoscaling – Inferrd automatically scales deployed models based on traffic, ensuring high availability and low latency even under heavy loads. You don‘t have to worry about over or under-provisioning resources.

• Flexibility – Because Inferrd supports all the major ML frameworks, you can deploy models built with a variety of different tools and libraries. You‘re not locked into a particular ecosystem.

• Cost efficiency – With Inferrd‘s consumption-based pricing, you only pay for the inference requests you use. There‘s no need to maintain expensive GPU instances when you‘re not actively serving predictions.

That said, there are a few potential drawbacks to consider with Inferrd:

• Vendor lock-in – While Inferrd does let you export deployed models, it‘s not always trivial to port them to another platform. Once you‘re relying on Inferrd for mission-critical deployments, migrating away can be challenging.

• Limited customization – Inferrd provides a streamlined deployment experience out-of-the-box, but that comes at the cost of flexibility. If you have very specific performance or security requirements for your model serving infrastructure, you may need more control than Inferrd allows.

• Added latency – Deploying models on Inferrd means you‘re communicating with an external API to generate predictions. For use cases that require extremely low inference latency (e.g. autonomous driving), the added network hop could be problematic.

Overall though, for most common ML deployment scenarios, Inferrd‘s ease-of-use and automated infrastructure management makes it a compelling option.

Conclusion

As machine learning becomes more widely adopted, the challenge of deploying models to production is only going to grow. While there‘s no one-size-fits-all solution, platforms like Inferrd are making it increasingly easy for data scientists to get their work into the hands of end-users.

By providing managed infrastructure, automatic scaling, and support for all the popular ML frameworks, Inferrd drastically simplifies the deployment workflow. In this guide, we covered the key features of the platform and walked through how to deploy models with a few lines of code. We also discussed real-world use cases and the tradeoffs to consider when evaluating Inferrd.

If you‘re looking to put ML models into production quickly and without the hassle of infrastructure management, Inferrd is definitely worth checking out. With its intuitive interface and powerful backend, it can help you go from experimentation to value faster than ever before.