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Automated Model Deployment Workshop

Seamlessly bridge the gap between the exploratory phase of selecting and training a model and production deployment

The challenge

Bridging the gap between model development and deployment

Data science teams excel at building powerful models but often face significant hurdles when it's time to put them into production:

Skill gap: Your talented data scientists shouldn't need to become DevOps experts just to deploy their work

Deployment complexity: Managing the full ML lifecycle requires specialized engineering expertise that's hard to find and expensive to maintain

Monitoring blind spots: Without proper automation, deployed models can drift or fail silently, putting your business outcomes at risk

Our solution

An end-to-end automated deployment

In this workshop you learn to set up an Automated Model Deployment, which provides a seamless bridge between model development and production environments:

Unified interface: Simple, intuitive dashboard for managing your entire model lifecycle

High-performance inference: Optimized serving with Triton inference server for maximum throughput

Automated CI/CD pipeline: Push-button deployment with comprehensive testing and validation

Flexible deployment options: Support for European clouds or major providers (GCP/AWS/Azure)

Enterprise-ready infrastructure: Built on Docker/Kubernetes for scalability and reliability

Comprehensive monitoring: Real-time insights with Grafana/Prometheus integration

Business impact

Why businesses choose automated model deployment

In today’s fast-paced AI landscape, the ability to quickly and efficiently deploy machine learning models is a key competitive advantage. However, many companies face roadblocks due to complex infrastructure requirements, a lack of specialized expertise, and the ongoing need for performance monitoring and optimization.

An automated model deployment eliminates these challenges by providing a scalable, reliable, and cost-efficient solution for model deployment. With automated CI/CD, real-time monitoring, and seamless integration into your existing infrastructure, businesses can streamline ML operations, reduce costs, and accelerate innovation.

Key business benefits:

Faster innovation & market readiness
Reduce deployment cycles from months to days, enabling your business to stay ahead of the competition with rapid iteration and feature updates.

Empower your data science team
Allow your experts to focus on model development rather than struggling with deployment logistics, boosting overall efficiency and productivity.

Reliable & scalable operations
Automate version control, rollback mechanisms, and performance monitoring to ensure consistency, stability, and reduced downtime.

Cost-effective & resource optimized
Leverage auto-scaling and efficient infrastructure management to minimize cloud and compute costs while maximizing performance.

By adopting an automated approach to ML deployment, businesses can reduce risk, improve efficiency, and gain a competitive edge in the AI-driven market.

Join our workshop!


Learn how to automate ML model deployment using industry best practices and modern cloud-native tools. Whether you're a Data Scientist or an MLOps Engineer, this workshop will equip you with hands-on knowledge to streamline deployments, enhance efficiency, and maximize model performance.

Designed for Data Scientists, ML Engineers, and DevOps Professionals, this workshop provides practical strategies to simplify and automate the model deployment process.

Contact us to reserve your spot or to discuss a private workshop just for your team!