AI / ML Engineering
"Cloudaeon bridges the gap between data science (where models are often developed) and production (where models provide real-world value). In essence, AI/ML engineering is about bringing the power of machine learning into real-world systems, ensuring they operate efficiently, reliably, and bring tangible value to businesses."
Amol Malpani (CTO, Cloudaeon)
Why choose us?
We focus on building, deploying, monitoring, and maintaining machine learning models and systems in real-world environments.
If you're thinking about how to make AI drive better business outcomes, start with a free readiness assessment and let us know when you're ready to start the conversation.
Key Use Cases
- Customer Segmentation: Grouping customers based on behavior, demographics, or purchasing patterns.
- Recommendation Systems: Recommending products or content to users.
- Predictive Maintenance: Predicting when machinery or equipment will fail.
- Fraud Detection: Identifying suspicious transactions in real-time.
- Image and Speech Recognition: For applications ranging from medical imaging to voice assistants.
Key Benefits
- Scalability: Handling the challenges of deploying and running ML models at scale.
- Latency: Ensuring that models deliver predictions in real-time or within acceptable time frames.
- Model Drift: Monitoring and addressing situations when a model's performance deteriorates over time.
- Operational Complexity: Managing the intricate processes from model development to deployment.
- Integration: Ensuring models integrate smoothly with existing IT infrastructure and business processes.
What We Offer
- Data Ingestion and Preparation: Gathering, cleaning, and preprocessing data.
- Feature Engineering: Transforming raw data into informative signals for models.
- Model Development: Creating, training, and validating ML models.
- Model Deployment: Making models available in a production environment.
- Model Monitoring: Tracking a model's performance over time.
- Model Retraining: Periodically updating models with fresh data.
- Infrastructure Management: Ensuring that the hardware and software resources are available and optimized.
- End-to-end Pipelines: Automating the ML lifecycle processes from data ingestion to deployment.
How We Work
- Define the Problem: Clearly state the business problem you aim to address with ML.
- Gather Data: Collect relevant data from various sources.
- Clean and Preprocess Data: Remove noise, handle missing values, and convert data to a usable format.
- Feature Engineering: Enhance the data with relevant features or transformations.
- Model Development: Select an appropriate algorithm and train the model.
- Validation and Testing: Evaluate the model's performance on unseen data.
- Deployment: Integrate the model into production systems.
- Monitoring: Continuously monitor the model's predictions and performance.
- Maintenance: Retrain or refine the model as needed, based on performance metrics or incoming new data.
Managed Services
- Infrastructure Management: Setting up and managing the required hardware and software.
- End-to-End Pipeline Automation: Tools for automating the ML lifecycle.
- Model Monitoring and Maintenance: Services for ongoing performance tracking and model retraining.
- Security and Compliance: Ensuring that data and models are secure, and all processes comply with relevant regulations.
- Consultation and Support: Expert guidance on best practices, problem-solving, and optimization.
- Integration Services: Assisting with the integration of AI/ML solutions into the client's existing systems.
Readiness Check
In 10 minutes, get a score to assess your Readiness & Maturity. You'll get a clear score to help your identify areas of improvement.
Getting Started
If you are ready to engage with us and would like do dive deeper into the subject, go ahead and book in a Discovery Workshop with our Practice Leads.