MLOps Training in Tashkent

This instructor-led, live training in Tashkent (online or onsite) is designed for advanced-level AI engineers and data scientists with intermediate-to-advanced experience who wish to enhance DeepSeek model performance, minimize latency, and efficiently deploy AI solutions using modern MLOps practices.

By the end of this training, participants will be able to:

• Optimize DeepSeek models for efficiency, accuracy, and scalability.
• Implement best practices for MLOps and model versioning.
• Deploy DeepSeek models on cloud and on-premise infrastructure.
• Monitor, maintain, and scale AI solutions effectively.

MLOps on Kubernetes is a framework for automating the training, validation, packaging, and deployment of machine learning models using containerized pipelines and GitOps workflows.

This instructor-led, live training (online or onsite) is aimed at intermediate-level practitioners who wish to build automated, scalable MLOps pipelines on Kubernetes.

Upon completing this training, participants will be equipped to:

• Design end-to-end CI/CD pipelines for machine learning.
• Implement GitOps workflows for model deployment and versioning.
• Automate training, testing, and packaging of ML models.
• Integrate monitoring, alerting, and rollback strategies.

Course Format

• Instructor-led presentations and in-depth technical sessions.
• Practical exercises for building real-world CI/CD workflows.
• Live lab sessions for deploying ML workloads on Kubernetes.

Course Customisation Options

• Organisations may request customised content aligned with their internal MLOps tools and infrastructure.

Kubeflow is an open-source platform designed to streamline building, training, and deploying machine learning workloads on Kubernetes.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to build reliable ML workflows using Kubeflow.

Upon completion of this training, attendees will gain the skills to:

• Navigate the Kubeflow ecosystem and core components.
• Build reproducible workflows with Kubeflow Pipelines.
• Run scalable training jobs on Kubernetes.
• Serve machine learning models efficiently using Kubeflow Serving.

Format of the Course

• Guided presentations and collaborative discussions.
• Hands-on labs with real Kubeflow components.
• Practical exercises to build end-to-end ML workflows.

Course Customization Options

• Customized versions of this training can be arranged to align with your team’s technology stack and project requirements.

Docker serves as a containerization platform designed to create reproducible, portable, and scalable environments for machine learning systems.

This instructor-led live training, available both online and on-site, targets intermediate to advanced technical professionals seeking to containerize and operationalize complete ML pipelines using Docker.

Upon completing this training, participants will be able to:

• Containerize ML training, validation, and inference workloads.
• Design and orchestrate end-to-end ML pipelines using Docker and complementary tools.
• Implement versioning, reproducibility, and CI/CD practices for ML components.
• Deploy, monitor, and scale ML services within containerized environments.

Course Format

• Interactive lectures supplemented by practical demonstrations.
• Hands-on exercises focused on constructing real-world ML pipeline components.
• Live-lab implementation for end-to-end containerized workflows.

Customization Options

• For tailored training aligned with specific ML infrastructure requirements, please contact us to explore options.

This instructor-led, live training in Tashkent (online or on-site) is tailored for engineers seeking to evaluate current approaches and tools to make informed decisions about adopting MLOps within their organizations.

By the end of this training, participants will be able to:

• Install and configure various MLOps frameworks and tools.
• Assemble the right team with the necessary skills to build and support an MLOps system.
• Prepare, validate, and version data for use by ML models.
• Understand the components of an ML Pipeline and the tools required to build one.
• Experiment with different machine learning frameworks and servers for deploying to production.
• Operationalize the entire Machine Learning process to ensure it is reproducible and maintainable.