Let’s Discuss Your RoR ML Project

Our Valuable Clients

Our RoR ML Integration Expertise

With new ML advances daily, it takes experience to implement capabilities seamlessly into Ruby.

Custom ML Model Development

Building robust backends and APIs with:

  • Computer Vision – CNNs, YOLO, image/video classifiers
  • NLP – Transformers, BERT, sentiment analysis
  • Recommendations – Content and collaborative filtering engines
  • Anomaly Detection – Isolation forests, dimensionality reduction
  • TensorFlow – based deep neural networks
  • Python Frameworks – Mastery over tools like Scikit-learn and Pandas.
  • Machine Learning Techniques – Proficiency in dimensionality reduction, regression, and gradient boosting.

Scalable Model Deployment

Our MLOps engineers have 5+ years of experience in developing scalable solutions with:

  • Containerization – Docker, Kubernetes for portability
  • Cloud Deployment – GCP, AWS, Azure for scale
  • CI/CD Pipelines – GitHub Actions, Jenkins for automation
  • Monitoring – Grafana, Sentry for observability
  • Optimization – Quantization, pruning for efficiency
  • Governance – MLflow, DVC for reproducibility
  • Hybrid Deployments – Blending on-premises and cloud setups for versatile model deployment.

End-to-end Rails Integration

Our Ruby developers have 5+ years of experience in end-to-end Rails integration with:

  • APIs and Microservices – Building integrations between Rails and ML frameworks like TensorFlow and PyTorch
  • Background Workers – Sidekiq, Resque for asynchronous tasks
  • Frontend Integration – Fetching predictions, displaying results
  • Databases – SQL, NoSQL systems for production data
  • Testing and Debugging – RSpec, debugging for quality
  • Deployment – Heroku, cloud platforms for delivery
  • Legacy Modernization – Upgrading apps while preserving functionality

Want to Integrate ML into Your Rails App?

Adding machine learning capabilities to a Ruby on Rails application can be challenging. Our dedicated Rails and machine learning experts can help. Let’s connect to discuss:

  • checked-engagement Optimal ML algorithms
  • checked-engagement Data pipeline
  • checked-engagement Model training
  • checked-engagement Integration
  • checked-engagement Project timelines and costs
  • checked-engagement Ruby, Rails, and ML engineers
  • checked-engagement Ongoing model monitoring
  • checked-engagement Management process
  • checked-engagement Technical support after launch

Clients Love Space-O Technologies

ROR ML Integration Partner

With our extensive Rails development experience and ML ops skills, we are ideally positioned to collaborate with you. Being a leading RoR development company, our engineers have deployed custom ML models for companies across industries, integrating them seamlessly with Ruby on Rails codebases.

We take care of the entire ML lifecycle including:

  • Data preparation and pipeline creation
  • Model development, evaluation, and deployment
  • Ongoing model monitoring, retraining, and maintenance

As your experienced ROR ML integration partner, we have become an extension of your team. We work closely with you to:

  • Deeply understand your use cases, data, and technical infrastructure
  • Design the optimal ML approach and integration architecture for your Rails app and business goals

Hire our RoR developers for all your project needs

Talk to Our Experts

ROR ML Development Portfolio

Our RoR ML Integration Technology Stack

Backend Programming Languages

Building robust backends and APIs with:

  • Ruby 3.1 – For efficient and scalable server-side coding
  • Rails 7 – For quickly scaffolding out new apps and features
  • ActiveRecord – As our ORM for interacting with databases
  • Sidekiq – For background job processing and queues

JavaScript Frameworks

Creating interactive frontend experiences with:

  • React.js – For building modular component-based UIs
  • Vue.js – For reactive and performant display layers
  • Node.js – For scalable server-side JS

Machine Learning Libraries

Building, training, and deploying models with:

  • TensorFlow – For developing and deploying neural networks
  • PyTorch – For advanced deep learning capabilities
  • Scikit-learn – For generalized machine learning tasks
  • Keras – To accelerate neural network building

MLOps Tools

Streamlining development and deployment with:

  • Docker – For containerizing models and microservices
  • Kubernetes – To automate scaling and management
  • MLflow – For reproducible model packaging and deployment

Messaging and Monitoring

Facilitating communication between services and tracking model performance

  • RabbitMQ – To connect model microservices
  • Grafana – For tracking model metrics and KPIs
  • Sentry – To monitor errors and incidents

Data Infrastructure

Building scalable and reliable data pipelines with:

  • PostgreSQL – For structured relational data storage and querying
  • Redis – For high-performance in-memory data caching and messaging
  • Snowflake – For scalable cloud data warehousing

Unlock Your Rails App’s Full Potential with ML

Integrating machine learning can add new capabilities and value to your Ruby on Rails application. Our experienced Rails and ML teams can collaborate with you to build custom models and integrate them seamlessly into your codebase. Let us rapidly prototype and deploy ML-powered features that move key metrics for your Rails app.

Our Step-by-Step ML Integration Process

We follow a proven process refined over 100+ successful ML projects to integrate predictive intelligence into your Ruby on Rails platform:


Planning & Design

We determine the ML approaches and technical architecture for your project. Our data scientists and Ruby architects prepare integration plans, deployment designs, and workflow integrations focused on performance.


Data Processing

We aggregate data from disparate sources and clean it by handling missing values and outliers. We add relevant labels and preprocess them through normalization and dimensionality reduction to create test datasets.


Model Development

Our Ruby developers use Python and TensorFlow to build ML models like CNNs and RNNs. They build the integration layer connecting Rails endpoints to ML microservices and create interfaces enabling data collection from Rails.



Our Ruby developers connect the Python-based ML model microservices to the core RoR application. This enables communication through either direct HTTP requests or asynchronous message queues like RabbitMQ.



The fully integrated machine learning RoR solution is deployed into production using CI/CD automation, with minimal downtime. Rigorous testing occurs at each stage to ensure a high-quality production system.



Post-deployment, we monitor key model performance metrics, retrain regularly as new data becomes available, and provide ongoing technical support. This ensures optimal performance and continual enhancement over time.

Key Benefits of ML Integration

Enhancing your Ruby on Rails application with machine learning capabilities infuses your platform with:

Competitive Differentiation

With ML, you can deliver experiences exceeding user expectations and ahead of competitors. Deliver hyper-personalized experiences, predictive analytics, and automation unavailable otherwise.

“Integrating ML provided capabilities we could not have developed internally in a reasonable timeframe. This differentiation was crucial to driving conversions.”

Cecil Thomas
Cecil Thomas CTO, AI Startup

Revenue Growth

ML unlocks powerful features for understanding users, segmenting audiences, predicting behavior, recommending relevant products, and more – driving increased sales.

“By integrating ML into our RoR platform, we deployed personalization that increased average order value 15%. Revenue growth achieved in weeks instead of months.”

Maria Smith
Maria Smith CSA, eCommerce Company

Operational Efficiency

Automate time-intensive tasks like data processing, decision validation, and content tagging. Optimize resource allocation and reduce costs through increased operational efficiency.

“The image tagging model integrated into our Rails app saves our editors hundreds of hours per month. This has allowed us to accelerate our production workflow.”

Mark Wilson
Mark Wilson CIO, Media Platform

Why Choose Us for ML Integration?

When evaluating Rails ML integration partners, customers choose us for:

Deep ML and Ruby Expertise

Our talented cross-functional team includes data scientists, ML researchers, and full-stack Ruby developers.

100+ Successful Integrations

We have completed over 100 production ML integrations into Ruby on Rails web and mobile applications.

Focus on High ROI

We ensure clear ROI on your project by prioritizing high-impact capabilities that drive tangible value.

Cutting-Edge Technology

We stay on top of the latest ML advances and specialize in advanced Ruby/Rails development capabilities.

Tailored Approach

You get a solution customized to your specific goals, data, and users – not a generic one-size-fits-all model.

End-to-End Capabilities

We handle the complete integration process from planning to post-deployment monitoring and maintenance.

Our Global Delivery Model

Our Global Delivery Model ensures efficient project execution, bringing together world-class expertise and localized insights for optimal results.

North America

North America

Our engineers in Toronto, New York, and San Francisco enable rapid collaboration with North American clients.

Asia Pacific

Asia Pacific

Our ML and RoR teams in Singapore, Sydney, and Tokyo are experts in supporting APAC time zone delivery.



Our European ML and Rails specialists in London, Amsterdam, and Prague align perfectly to European hours.

Latin America

Latin America

Engage with our seasoned ML and Ruby developers in Mexico City, Bogota, and Buenos Aires for LATAM-optimized delivery.

Industries We Serve

Leveraging Ruby on Rails with machine learning, we revolutionize industries by delivering dynamic solutions tailored to their unique requirements.



We facilitate early disease detection, patient risk analysis, and personalized treatment suggestions, contributing to improved patient care.



Our Rails ML integrations include personalized product recommendations, inventory predictions, and customer behavior analysis for online shopping experiences.



From fraud detection to customer spending habits insights, our integrations empower financial platforms to be more secure and user-centric.



Our solutions offer adaptive learning pathways, automated content curation, and predictive performance analytics, making education interactive.



We integrate predictive maintenance, real-time quality check analytics, and supply chain optimization tools, ensuring increased production efficiency.

And more


Our Rails ML tools analyze traffic patterns, optimize routes, and predict delivery times, guaranteeing efficient and timely service in the logistics sector.

Discover the Power of RoR ML Integrations

Interested in integrating machine learning with your Ruby on Rails application? Connect with us for a complimentary consultation, and let’s delve into crafting ML solutions precisely aligned with your objectives.