MLOps: CI/CD for Machine Learning Training Course

This course is designed for participants who are engaged in the design, implementation, and management of DevOps deployment pipelines and toolchains that support Continuous Integration, Continuous Delivery, Continuous Testing and potentially Continuous Deployment. The course highlights underpinning processes, metrics, APls and cultural considerations with Continuous Delivery

Learn how to optimize testing, to test faster and still obtain good quality and a consistent stable result.

Learn what DevOps Engineering is, why DevOps engineering is important, and how DevOps is engineered for success.

The course opens with a short review of DevOps foundational principles and then transitions into implementing DevOps. It is designed from an engineering point of view and covers topics such as DevOps in relation to other frameworks, technologies,
application design practices, continuous integration practices, continuous delivery and deployment, continuous testing, elastic infrastructures, monitoring, metrics, observability, governance, human aspects, and future trends of DevOps engineering.

The DevOps Foundation course provides a baseline understanding of key DevOps terminology to ensure everyone is talking the same language and highlights the benefits of DevOps to support organizational success.

The course highlights the human dynamics of cultural change and equips participants with practices, methods, and tools to engage people across the DevOps spectrum through the use of real-life scenarios and case studies. Upon completion of the course, participants will have tangible takeaways to leverage when back in the office such as understanding Value Stream Mapping.

An introduction to value stream management principles, practices, and tools. Designed to optimize value flow and realization in digital value streams and develop individuals’ skills. Digital value streams include the software applications and services in addition to the platforms that support them e.g., cloud infrastructure or DevOps toolchains

Course topics covered include how DevSecOps provides business value, enhancing your business opportunities, and improving corporate value.  The core DevSecOps principles taught can support an organizational transformation, increase productivity, reduce risk, and optimize resource usage.

DevSecOps Practitioner introduces a range of practices for advancing to more comprehensive understanding of DevSecOps practices. Explore practical outcomes through finding the right mix of people, building processes to accelerate value, and comparing technological options available today. Tailored for recently transformed organizations who are looking to enhance DevSecOps skills and awareness.

This instructor-led, live training in Mexico (online or onsite) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes.

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

• Install and configure Kubeflow on premise and in the cloud using AWS EKS (Elastic Kubernetes Service).
• Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
• Run entire machine learning pipelines on diverse architectures and cloud environments.
• Using Kubeflow to spawn and manage Jupyter notebooks.
• Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training in Mexico (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to an AWS EC2 server.

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

• Install and configure Kubernetes, Kubeflow and other needed software on AWS.
• Use EKS (Elastic Kubernetes Service) to simplify the work of initializing a Kubernetes cluster on AWS.
• Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
• Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
• Leverage other AWS managed services to extend an ML application.

This instructor-led, live training in Mexico (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to Azure cloud.

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

• Install and configure Kubernetes, Kubeflow and other needed software on Azure.
• Use Azure Kubernetes Service (AKS) to simplify the work of initializing a Kubernetes cluster on Azure.
• Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
• Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
• Leverage other AWS managed services to extend an ML application.

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.

This instructor-led, live training in Mexico (online or onsite) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes.

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

• Install and configure Kubeflow on premise and in the cloud.
• Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
• Run entire machine learning pipelines on diverse architectures and cloud environments.
• Using Kubeflow to spawn and manage Jupyter notebooks.
• Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.

This instructor-led, live training in (online or onsite) is aimed at data scientists who wish to go beyond building ML models and optimize the ML model creation, tracking, and deployment process.

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

• Install and configure MLflow and related ML libraries and frameworks.
• Appreciate the importance of trackability, reproducability and deployability of an ML model
• Deploy ML models to different public clouds, platforms, or on-premise servers.
• Scale the ML deployment process to accommodate multiple users collaborating on a project.
• Set up a central registry to experiment with, reproduce, and deploy ML models.

The SRE (Site Reliability Engineering) Foundation course is an introduction to the principles & practices that enable an organization to reliably and economically scale critical services. Introducing a site-reliability dimension requires organizational re-alignment, a new focus on engineering & automation, and the adoption of a range of new working paradigms.