MLOps (Machine Learning Operations) Fundamentals Course Overview

Enroll for the 1-day MLOps (Machine Learning Operations) Fundamentals training and certification course from Koenig solutions accredited by Google.

This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.

Target Audience

This course is best suited for:

  • Data Scientists looking to quickly go from machine learning prototype to production to deliver business impact.
  • Software Engineers looking to develop Machine Learning Engineering skills.
  • ML Engineers who want to adopt Google Cloud.

Learning Objectives

Upon completion of this course, participants will be able to:

  • Identify and use core technologies required to support effective MLOps.
  • Configure and provision Google Cloud architectures for reliable and effective MLOps environments.
  • Implement reliable and repeatable training and inference workflows.
  • Adopt the best CI/CD practices in the context of ML systems.
  • Operate deployed machine learning models effectively and efficiently.
  • Identify and use core technologies required to support effective MLOps.

The 1-on-1 Advantage


Flexible Dates

  • • Choose Start Date
  • • Reschedule After Booking
  • • Weekend / Evening Option

4-Hour Sessions

You will learn:

Module 1: Why and When do we need MLOps
  • Discuss Data Scientists' pain points.
  • Identify ML Engineering characteristics and challenges
  • Define how Google Cloud can help with MLOps.
  • Recognize how MLOps differs from manual ML management.
  • Compare and contrast DevOps vs MLOps.
  • Define what is a Docker container.
  • Create Docker containers.
  • Identify the architecture of Kubernetes: pods, namespaces.
  • Create Docker containers using Google Container Builder
  • Store container images in Google Container Registry.
  • Create a Kubernetes Engine cluster.
  • Manage Kubernetes deployments
  • Identify the benefits and opportunities of AI Pipelines
  • Define Access Controls within AI Pipelines.
  • Recognize pipeline components.
  • List pipeline workflows.
  • Set up AI Platform Pipelines
  • Create a machine learning pipeline.
  • Run a machine learning pipeline.
  • Connect to AI Platform Pipelines using the Kubeflow Pipelines SDK
  • Configure a Google Kubernetes Engine cluster for AI Platform Pipelines.
  • Identify the main concepts of MLOps on AI Platform.
  • Create a reproducible dataset.
  • Implement a tunable model.
  • Build and push a training container.
  • Train and tune a model.
  • Serve and query a model.
  • Recognize how Kubeflow Pipelines fits in MLOps.
  • Describe a Kubeflow Pipeline with KF DSL.
  • Use the various Kubeflow components.
  • Compile, upload, and run a pipeline build in Kubeflow Pipelines.
  • Create Cloud Build Builders
  • Configure pipelines with Cloud Build.
  • Create triggers for training models using Cloud Build Triggers.
  • Adopt the best CI/CD practices in the context of ML systems.
  • Summarize the course.
Live Online Training (Duration : 8 Hours)
We Offer :
  • 1-on-1 Public - Select your own start date. Other students can be merged.
  • 1-on-1 Private - Select your own start date. You will be the only student in the class.

750 + If you accept merging of other students.
4 Hours
8 Hours
Comfort Track
Week Days

Start Time : At any time

12 AM
12 PM

1-On-1 Training is Guaranteed to Run (GTR)
Group Training
650 Per Participant
12 Dec
09:00 AM - 05:00 PM CST
(8 Hours/Day)
16 Jan
09:00 AM - 05:00 PM CST
(8 Hours/Day)
Course Prerequisites

Completed Machine Learning with Google Cloud or have equivalent experience.

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