Cortex consultants

Q: What types of machine learning models can Cortex deploy?

Cortex supports models built with most major ML frameworks including TensorFlow, PyTorch, scikit-learn, and ONNX. It can serve both batch and real-time prediction workloads, making it suitable for a wide range of inference tasks from image classification to time-series forecasting.

Q: How does Cortex handle autoscaling for ML models?

Cortex automatically scales model replicas based on incoming request volume. You define minimum and maximum replica counts along with a target requests-per-second threshold, and Cortex adjusts capacity accordingly. This means you only pay for compute when predictions are actually being requested.

Q: Can Cortex be integrated with existing data pipelines?

Yes. Cortex exposes models as standard REST APIs, so any system that can make HTTP requests can send data for prediction. We typically connect Cortex endpoints with workflow automation tools like n8n to build complete pipelines that handle data ingestion, prediction, and result routing.

Q: What infrastructure does Cortex run on?

Cortex runs on Kubernetes and is typically deployed on AWS. It manages the underlying cluster resources including load balancing, rolling updates, and container orchestration. You can run it on existing Kubernetes clusters or let Cortex provision its own infrastructure.

Q: How do you monitor model performance after deployment?

We set up monitoring for both operational metrics (latency, error rates, throughput) and model quality metrics (prediction drift, accuracy degradation). When model performance drops below defined thresholds, automated alerts trigger so your team can investigate and retrain if needed.

Q: What is the typical timeline for deploying a model with Cortex?

For a trained model that is ready for production, initial Cortex deployment usually takes one to two weeks. This includes environment setup, API configuration, testing, and integration with your existing systems. More complex multi-model architectures may take longer depending on the data pipeline requirements.

We can help you automate your business with Cortex and hundreds of other systems to improve efficiency and productivity. Get in touch if you’d like to discuss implementing Cortex.

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About Cortex

Cortex is an open-source platform for deploying, managing, and scaling machine learning models in production. It handles the infrastructure complexity of serving ML models as APIs, so data teams can focus on building rather than wrestling with Kubernetes configs and autoscaling policies.

Organisations use Cortex when they need reliable, low-latency predictions from trained models without dedicating engineering resources to infrastructure management. Common use cases include real-time recommendation engines, fraud detection pipelines, and natural language processing services that need to scale with demand.

At Osher, we connect Cortex deployments into broader automation workflows. A typical integration might route incoming data through preprocessing steps, send it to a Cortex-hosted model for inference, then push predictions into downstream systems like CRMs, dashboards, or alerting tools. Our AI agent development team builds these end-to-end pipelines so your ML models actually deliver business value rather than sitting idle in a notebook.

We handle the full setup: configuring model endpoints, setting up monitoring for prediction drift, and building the data plumbing that connects your models to the rest of your tech stack.

Cortex FAQs

Frequently Asked Questions

Common questions about how Cortex consultants can help with integration and implementation

What types of machine learning models can Cortex deploy?

How does Cortex handle autoscaling for ML models?

Can Cortex be integrated with existing data pipelines?

What infrastructure does Cortex run on?

How do you monitor model performance after deployment?

What is the typical timeline for deploying a model with Cortex?

How it works

We work hand-in-hand with you to implement Cortex

As Cortex consultants we work with you hand in hand build more efficient and effective operations. Here’s how we will work with you to automate your business and integrate Cortex with integrate and automate 800+ tools.

Step 1

Assess Your ML Model Readiness

We review your trained model, its dependencies, and performance benchmarks to confirm it is production-ready. This includes checking input/output formats and identifying any preprocessing requirements.

Step 2

Configure the Cortex Environment

We set up the Cortex cluster on your preferred cloud infrastructure, defining compute resources, autoscaling policies, and networking rules to match your expected workload.

Step 3

Package and Deploy Your Model

Your model is containerised with its dependencies and deployed as an API endpoint through Cortex. We run validation tests to confirm predictions match expected outputs.

Step 4

Build the Integration Pipeline

We connect the Cortex API to your upstream data sources and downstream systems, creating an automated flow from raw data input through to actionable predictions.

Step 5

Set Up Monitoring and Alerts

Monitoring dashboards track latency, throughput, error rates, and prediction quality. Alert rules notify your team when any metric falls outside acceptable ranges.

Step 6

Test, Optimise, and Hand Over

We run load tests to verify scaling behaviour, optimise resource allocation for cost efficiency, and document the full setup so your team can manage it independently.

Transform your business with Cortex

Unlock hidden efficiencies, reduce errors, and position your business for scalable growth. Contact us to arrange a no-obligation Cortex consultation.

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Book a call