VA, Stafford - Quantico Corporate

800 Corporate Drive, Suite 301, Stafford, united states, 22554

The venue is located between interstate 95 and the Jefferson Davis Highway, in the vicinity of the Courtyard by Mario...

VA, Fredericksburg - Central Park Corporate Center

1320 Central Park Blvd., Suite 200, Fredericksburg, united states, 22401

The venue is located behind a complex of commercial buildings with the Bank of America just on the corner before the turn ...

VA, Richmond - Two Paragon Place

6802 Paragon Place Suite 410, Richmond, united states, 23230

The venue is located in bustling Richmond with Hampton Inn, Embassy Suites and Westin Hotel less than a mile away.

VA, Reston - Sunrise Valley

12020 Sunrise Valley Dr #100, Reston, united states, 20191
The venue is located just behind the NCRA and Reston Plaza Cafe building and just next door to the United Healthcare building...

VA, Reston - Reston Town Center I

11921 Freedom Dr #550, Reston, united states, 20190
The venue is located in the Reston Town Center, near Chico's and the Artinsights Gallery of Film and Contemporary Art.

VA, Richmond - Sun Trust Center Downtown

919 E Main St, Richmond , united states, 23219
The venue is located in the Sun Trust Center on the crossing of E Main Street and S to N 10th Street just opposite of 7 Eleve...

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MLflow
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Kubeflow

Explore Our Courses

Kubeflow on AWS

28 Hours
This instructor-led, live training in Virginia (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.
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Kubeflow on Azure

28 Hours
This instructor-led, live training in Virginia (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.
Read more...

Kubeflow on GCP

28 Hours
This instructor-led, live training in Virginia (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to Google Cloud Platform (GCP). By the end of this training, participants will be able to:
  • Install and configure Kubernetes, Kubeflow and other needed software on GCP and GKE.
  • Use GKE (Kubernetes Kubernetes Engine) to simplify the work of initializing a Kubernetes cluster on GCP.
  • 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 GCP services to extend an ML application.
Read more...

Kubeflow on IBM Cloud

28 Hours
This instructor-led, live training in Virginia (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to IBM Cloud Kubernetes Service (IKS). By the end of this training, participants will be able to:
  • Install and configure Kubernetes, Kubeflow and other needed software on IBM Cloud Kubernetes Service (IKS).
  • Use IKS to simplify the work of initializing a Kubernetes cluster on IBM Cloud.
  • 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 IBM Cloud services to extend an ML application.
Read more...

MLOps: CI/CD for Machine Learning

35 Hours
This instructor-led, live training in Virginia (online or onsite) is aimed at engineers who wish to evaluate the approaches and tools available today to make an intelligent decision on the path forward in adopting MLOps within their organization. By the end of this training, participants will be able to:
  • Install and configure various MLOps frameworks and tools.
  • Assemble the right kind of team with the right skills for constructing and supporting an MLOps system.
  • Prepare, validate and version data for use by ML models.
  • Understand the components of an ML Pipeline and the tools needed to build one.
  • Experiment with different machine learning frameworks and servers for deploying to production.
  • Operationalize the entire Machine Learning process so that it's reproduceable and maintainable.
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Kubeflow

35 Hours
This instructor-led, live training in Virginia (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.
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Kubeflow on OpenShift

28 Hours
This instructor-led, live training in (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to an OpenShift on-premise or hybrid cloud.
  • By the end of this training, participants will be able to:
  • Install and configure Kubernetes and Kubeflow on an OpenShift cluster.
  • Use OpenShift to simplify the work of initializing a Kubernetes cluster.
  • 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.
  • Call public cloud services (e.g., AWS services) from within OpenShift to extend an ML application.
Read more...

Kubeflow Fundamentals

28 Hours
This instructor-led, live training in Virginia (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.
Read more...

MLflow

21 Hours
This instructor-led, live training in Virginia (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.
Read more...

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Testimonials (2)

I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.

Guillaume Gautier - OLEA MEDICAL | Improved diagnosis for life™
Course - Kubeflow

the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose

Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow
Online MLOps training in Virginia, MLOps training courses in Virginia, Weekend MLOps courses in Virginia, Evening MLOps training in Virginia, MLOps instructor-led in Virginia, Weekend MLOps training in Virginia, MLOps trainer in Virginia, Online MLOps training in Virginia, MLOps on-site in Virginia, MLOps coaching in Virginia, Evening MLOps courses in Virginia, MLOps instructor-led in Virginia, MLOps private courses in Virginia, MLOps one on one training in Virginia, MLOps instructor in Virginia, MLOps boot camp in Virginia, MLOps classes in Virginia
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