Home / MLOps / MLOps with Kubeflow Training

Certificate Course in

MLOps with Kubeflow Training

MLOps is the new emerging technology that helps Data Science leaders to build, deploy, and monitor data models. MLOps, at its best, incorporates many efficient tools to speed development and foster production.
  • 60 Hours Classroom & Online Sessions
  • 80 Hours Assignments & Real-Time Projects
  • Complimentary Python Programming Beginners Course
  • Complementary Kubernetes for Beginners
  • Complimentary DevOps for Beginners
MLOps with Kubeflow Training course reviews - 360digitmg
485 Reviews
MLOps with Kubeflow Training course reviews - 360digitmg
2064 Learners
Academic Partners & International Accreditations
  • MLOps course with Microsoft
  • MLOps course with Nasscomm
  • MLOps course with Innodatatics
  • MLOps Certification Course with SUNY
  • MLOps Course with NEF

Calendar-for-Virtual Interactive Classes

Start Date

MLOps with Kubeflow

MLOps with Kubeflow Training course duration Malaysia - 360digitmg

Total Duration

2.5 Months

MLOps with Kubeflow Training course pre-requisites- 360digitmg


  • Data Science - Traditional ML algorithms & DL (Neural network) algorithms
  • Programming - Beginner to Intermediate

MLOps with Kubeflow Course Overview

Machine Learning Operations a.k.a MLOps is fast gaining steam as one of the most sought after skills in the Data Science and Artificial Intelligence domain. The MLOps with Kubeflow course is a first-in-the-industry offering to help Data Scientists and ML Engineers deploy ML models into production at scale and efficiently. This course focuses on the best in class tools and frameworks such as Kubernetes, Kubeflow, Istio, Tensorflow Extended, and Apache Beam among others.

A few years ago, if a professional knew about machine learning, he would have easily got a job in any company of choice. It may still be the case that ML Engineers and Data Scientists are in demand, but there is also an increasingly available supply which will make it difficult to stand out from the competition. Also, enterprises across all industries now have some capability in Data Sciences and are investing in machine learning technologies. However, the industry currently is struggling not to create models, but deploy them into production and monitor them efficiently with optimal use of resources. This has given rise to the intriguing skill called Machine Learning Operations which is quite simply, DevOps for Machine Learning. While it may sound very trivial to perform DevOps for ML Models, it rarely is. MLOps differs prominently from traditional DevOps in the following ways-

1. As part of Continuous Integration (CI), MLOps requires that testing and validation be performed not just on code and its components but also on the train and validation datasets, the data schemas, and the models themselves.

2. Continuous Delivery for MLOps also means that the effort not just applies to a sole software or service but an entire ML pipeline which in turn could be automatically deployed to another microservice.

3. Continuous Training - a trait that is unique to MLOps which focuses on automatically retraining the models periodically and guides how the models are served.

The Rise and Rise of Kubeflow

MLOps with Kubeflow program is a natural extension of the other program offered by 360DigitMG. Previously MLOps courses were being developed using TensorFlow Extended (TFX). Kubeflow is now emerging as the de facto implementation and orchestration mechanism of machine learning model deployment. Kubeflow started as a simple mechanism to facilitate basic ML infra up and running on Kubernetes. Kubeflow’s development was majorly accelerated by two driving forces - the meteoric rise of ML across enterprises and the emergence of Kubernetes as the gold-standard in the infra management layer.

MLOps with Kubeflow Learning Outcomes

MLOps with the Kubeflow is the culmination of years of experience and months of hard work to put together a course that could serve as a guide for production-grade model deployment. As such the participants can expect to know about Machine Learning Life Cycle, common pitfalls while attempting to deploy them effectively, and how to address them. Participants are expected to have a working knowledge of Machine Learning algorithms, lifecycle, and intermediate level programming skills. After completing this course the participants should be able to clearly articulate the need for a robust MLOps strategy and be able to architect, design, and deploy them on on-premise and cloud infrastructure using Kubeflow. As a bonus interested participants will also be exposed to other popular frameworks like MLflow and Apache Airflow.

The need for a MLOps strategy and why organizations need to succeed
Understand containers and get a better understanding of Docker
The role of Kubernetes and how to set up, configure, and operate Kubernetes pods
An introduction to Tensorflow Extended (TFX) and how it forms the foundation of Kubeflow
Understand Kubeflow and its role in the ML Model Development Lifecycle (MLDC)
Install and configure Kubeflow pipelines on-prem and cloud- based solutions
Build pipelines for data ingestion, data preprocessing and feature extraction/engineering using Kubeflow
Deploy and Monitor ML models on major cloud platforms like GCP and AWS using Kubeflow

Block Your Time

MLOps online course - 360digitmg

60 hours

Classroom Sessions

MLOps online course - 360digitmg

40 hours


MLOps online course - 360digitmg

40 hours

Live Projects

Who Should Sign Up?

  • Data Scientists
  • Data and Analytics Manager
  • Business Analysts
  • Data Engineers
  • DevOps Engineers
  • Machine Learning Architects
  • Model Risk Managers/Auditors

Modules for MLOps with Kubeflow Course

The course modules are designed in a step by step manner to ensure the participants gain a deeper understanding of the MLOps concepts. Firstly, the initial modules will focus on the ML Model Development Lifecycle (MLDC) and why MLOps is necessary. Participants will also be able to understand the project management methodology which is based on the Cross-Industry Standards for Data Mining (CRISP-DM) framework. Then the participants will be introduced to Kubernetes clusters and their inner workings. Participants will slowly work up their way towards Kubeflow and understand how to install and configure it in different environments such as cloud- native, on-prem, hybrid, etc. They will also be introduced to multiple other lower -layer abstractions like Istio, KNative (which are part of the Kubeflow framework) to gain a deeper understanding of Kubeflow operations. Finally, the participants will deploy Kubeflow pipelines across various cloud platforms such as AWS, GCP, etc.

In today’s world data science has penetrated across all industries and domains and has become ubiquitous. Most data scientists and machine learning engineers are able to come up with amazing models as proof of concepts but they are unable to deploy them in production and at scale. This has given rise to something called MLOps which basically means DevOps for Machine Learning. This chapter dwells deep into this need.

This module offers a complete overview of everything you need to know about Kubernetes. This module introduces Kubernetes and then explains why containers are required. The module explains the basic building blocks of Kubernetes such as pods and how they can be used in applications and finally wraps it up with an explanation of the Kubernetes API.

This module will introduce the need for Kubeflow even when Kubernetes is already existing. This module will also attempt to answer how Kubeflow should be installed. It details the security constraints, infrastructure requirements, scalability, and reliability.

This module will touch up some concepts that may be familiar to users in the DevOps space. It will introduce concepts such as public key infrastructure (PKI), authentication, authorization, and role- based access control, (RBAC), Kerberos, and transport layer security (TLS). It will also introduce service mesh management with Istio.

This module will begin with an introduction of Kubeflow pipelines - which is a platform that comprises:

  • A user interface that tracks, manages, and executes pipelines
  • A pipeline execution scheduling engine
  • Python SDK for managing these pipelines
  • Leveraging Jupyter Notebooks for using the Python SDK

Data preprocessing is a multi-stage process which consists of collecting data from disparate sources, augmenting it, calculating basic statistics, handling missing values, and outliers. Feature engineering is the process of deriving additional features or removing unnecessary features to add more predictive power to the ML model. This chapter will introduce participants to the methods provided by Kubeflow to construct easily repeatable data processing and feature engineering pipelines.

Model training is the process of creating logical relationships between ‘training data’ and using it to make predictions on ‘unseen’ data. This chapter will focus on how to train models on Kubeflow using two different frameworks - Tensorflow and Scikit-learn.

This chapter discusses how to deploy, serve models, and continuously monitor and update them. Model serving means hosting the model which can be interfaced via a service. The models can be served through two approaches - embedded serving and model serving as a service (MaaS).

How We Prepare You
  • MLOps course with placements
    Additional Assignments of over 80+ hours
  • MLOps course with placements training
    Live Free Webinars
  • MLOps training institute with placements
    Resume and LinkedIn Review Sessions
  • MLOps course with certification
    Lifetime LMS Access
  • MLOps certification with USP
    Job Placements in Data Science Fields
  • MLOps course with USP
    Complimentary Courses
  • MLOps course
    Unlimited Mock Interview and Quiz Session
  • MLOps training with placements
    Hands-on Experience in Capstone Projects
  • MLOps course
    Life Time Free Access to Industry Webinars

Call us Today!

Limited seats available. Book now

Make an Enquiry
Call Us