Chattanooga, TN – Regus Tallan Financial Center

200 W M.L.K. Blvd Center, Suite 1000, Chattanooga, United States, 37402

The venue sits in downtown Chattanooga’s financial district and is accessible by car via Interstates 24, 75, and 59, with on-site parking and nearby public garages. From Chattanooga Metropolitan Airport (CHA), approximately 8 miles away, a taxi or rideshare takes about 15 minutes via Highway 27 North. Local transit includes CARTA buses, plus a free electric shuttle downtown stopping within a few blocks of the building.

Knoxville, TN – Regus First Horizon Plaza

800 S Gay St, Knoxville, United States, 37929

The venue is located in downtown Knoxville, easily accessible by car via I‑40 and I‑275, with ample on-site and nearby metered parking. From McGhee Tyson Airport (TYS), about 15 miles away, a rideshare or taxi takes around 20–25 minutes via Alcoa Highway and I‑275 North. Knoxville Area Transit (KAT) buses serve Gay Street, with routes stopping just steps from the entrance.
NobleProg Training Place, City Memphis, TN - Regus at International Dr

Memphis, TN - Regus at International Dr

1661 International Dr, Suite 400 , Memphis, United States, 38120

The venue is located in the East Memphis business park, accessible by car via I‑240 and Poplar Avenue, offering free parking onsite. From Memphis International Airport (MEM), around 13 miles southwest, rideshare or taxi via I‑240 East and Poplar Ave takes about 25–30 minutes. Memphis Area Transit Authority (MATA) buses serve Poplar Avenue with stops within walking distance.

Nashville, TN – Regus Fifth Third Center

424 Church St, Nashville, United States, 37219

The venue is in the heart of downtown Nashville, steps from I‑40 and I‑24 with multiple public parking garages nearby. From Nashville International Airport (BNA), about 8 miles north, a taxi or rideshare via I‑40 West takes roughly 15–20 minutes. Nashville MTA buses and the Music City Star commuter rail serve stations within walking distance on Church and Broadway.

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Category for Kubeflow

Kubeflow
Category for MLflow

MLflow

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DeepSeek: Advanced Model Optimization and Deployment

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This instructor-led, live training in Tennessee (online or onsite) is aimed at advanced-level AI engineers and data scientists with intermediate-to-advanced experience who wish to enhance DeepSeek model performance, minimize latency, and deploy AI solutions efficiently 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.
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Kubeflow

 35 Hours

This instructor-led, live training in Tennessee (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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MLOps for Azure Machine Learning

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This instructor-led, live training in (online or onsite) is aimed at machine learning engineers who wish to use Azure Machine Learning and Azure DevOps to facilitate MLOps practices.

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

  • Build reproducible workflows and machine learning models.
  • Manage the machine learning lifecycle.
  • Track and report model version history, assets, and more.
  • Deploy production ready machine learning models anywhere.
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MLOps: CI/CD for Machine Learning

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This instructor-led, live training in Tennessee (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 on AWS

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This instructor-led, live training in Tennessee (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 Tennessee (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.
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Kubeflow Fundamentals

 28 Hours

This instructor-led, live training in Tennessee (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.
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MLflow

 21 Hours

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.
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Testimonials (2)

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

Guillaume GAUTIER - OLEA MEDICAL

Course - MLflow

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 TM

Course - Kubeflow

Upcoming Courses

MLOps: CI/CD for Machine Learning

2025-08-25 9:30 am
35 hours
Chattanooga, TN – Regus Tallan Financial Center
$ 6757 (Online)
$ 6757 (Classroom)

MLOps: CI/CD for Machine Learning

2025-09-08 9:30 am
35 hours
Chattanooga, TN – Regus Tallan Financial Center
$ 6757 (Online)
$ 6757 (Classroom)

Kubeflow

2025-09-22 9:30 am
35 hours
Chattanooga, TN – Regus Tallan Financial Center
$ 6757 (Online)
$ 6757 (Classroom)

DeepSeek: Advanced Model Optimization and Deployment

2025-10-06 9:30 am
14 hours
Chattanooga, TN – Regus Tallan Financial Center
$ 2703 (Online)
$ 2703 (Classroom)

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