Maximizing Your Team's Capabilities
In today's rapidly evolving digital landscape, organizations across virtually every industry are racing to harness the power of machine learning (ML) to drive innovation, enhance decision-making, and gain a competitive edge.
However, deploying and managing ML models at scale can be complex, time-consuming, and resource-intensive.
Aya Data is here to support businesses and organizations with the end-to-end process of training, tuning, and deploying models, from data collection to training and CI/CD.
Our goal is to seamlessly augment internal teams, or provide entire managed teams, to deliver end-to-end ML services from data pipeline building to model training and deployment.
What is ML-Ops-as-a-Service?
A branch of Dev-Ops-as-a-service, ML-Ops-as-a-service involves a collection of methodologies that support efficient, cost-effective, and dependable management of ML models.
This requires a blend of expertise in the end-to-end ML process, from data collection through to training, optimization, deployment, and ongoing monitoring.
Aya Data streamline the integration of new models and ML infrastructure, ensuring seamless delivery to your application.
We can also consult and assist on matters of governance, monitoring and compliance.
Our ML-Ops Services
Aya Data provides experienced data scientists and data engineers with a broad range of skills to plug into your in house teams wherever needed. We can collect and label class-leading data, prepare it for models, and assist in the ML value chain to deploy, test and optimize models.
- Master Data Management: Aya Data can expertly gather, store, and preprocess data, transforming it into data capable of training accurate, cutting-edge models.
- Streamlined Model Training and Validation: Revolutionize model training with automated processes, hyperparameter tuning, and validation, ensuring only top-quality models are deployed.
- Efficient Model Versioning: We will assist in tracking and managing the iterations of your ML models, compare performance, and ensure compliance throughout the ML value chain.
- Seamless Model Deployment: Deploy ML models seamlessly, utilizing containerization, serverless computing, and top-tier API management.
- CI/CD: Automate the building, testing, and deploying of ML models to supercharge iterations and minimize manual effort with Continuous Integration (CI) and Continuous Deployment (CD).
- Comprehensive Model Monitoring: Aya Data can monitor the performance of your deployed ML models, and leverage metrics, logs, and alerts to ensure peak performance.
- Dynamic Retraining and Updating: Keep your ML models up-to-date by automating retraining with new data or upgrading them with new algorithms,
- Experiment Tracking and Management: Our team will sharpen decision-making by tracking, managing, and comparing multiple ML experiments, all in one place.
- Scalability and Performance Optimization: Enhance the full potential of deployed ML models, ensuring they can scale to meet growing demands effortlessly.
- Model Governance and Compliance: We can assist in the navigation of AI regulations and industry standards, ensuring your ML models and processes remain compliant and trustworthy.
The ML-Ops process encompasses three main stages:
- Designing and training the model application
- Development, experimentation and optimization
- Deployment, operation and monitoring.
Firstly, we seek to understand the organization, business, data, and the value machine learning can provide.
Here, we identify the target users and understand how ML can solve the proposed problems. Problems can either be commercial (e.g. reducing customer churn or increasing sales), or research-based, non-commercial or novel, (e.g. developing a model to identify environmental changes).
Understanding the organization and its objectives provides a path towards developing ML solutions.
Then, we start defining and building appropriate and effective ML solutions. The design phase aims to examine available data for training the model and outlining its functional requirements of our ML model. This informs the application's architecture and development, including aspects such as data collection, feature engineering, etc.
The next phase involves developing proof-of-concept algorithms. This stage involves iterative steps such as identifying or refining the appropriate algorithms, and involves expertise in data science and engineering.
The final phase involves deploying the previously ML model in production, including testing, versioning, continuous delivery, and monitoring. Ongoing monitoring has become extremely important for AI models in regulated sectors.
Aya Data consults with organizations at every stage of the process, ensuring the eventual model meets specifications.