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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).

Tools Covered
MLOps with Kubeflow Training course using r studio programming
MLOps course using apache air flow
MLOps course using r studio programming
MLOps course using kube flow
MLOps course using kubernetes
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

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MLOps with Kubeflow Panel of Coaches

Artificial Intelligence & Deep Learning Course Training -360digitmg

Bharani Kumar Depuru

  • Areas of expertise: Data analytics, Digital Transformation, Industrial Revolution 4.0
  • Over 18+ years of professional experience
  • Trained over 2,500 professionals from eight countries
  • Corporate clients include Deloitte, Hewlett Packard Enterprise, Amazon, Tech Mahindra, Cummins, Accenture, IBM
  • Professional certifications - PMP, PMI-ACP, PMI-RMP from Project Management Institute, Lean Six Sigma Master Black Belt, Tableau Certified Associate, Certified Scrum Practitioner, (DSDM Atern)
  • Alumnus of Indian Institute of Technology, Hyderabad and Indian School of Business
Read More >
Artificial Intelligence & Deep Learning Course Training -360digitmg

Sharat Chandra Kumar

  • Areas of expertise: Data sciences, Machine learning, Business intelligence and Data
  • Trained over 1,500 professional across 12 countries
  • Worked as a Data scientist for 18+ years across several industry domains
  • Professional certifications: Lean Six Sigma Green and Black Belt, Information Technology Infrastructure Library
  • Experienced in Big Data Hadoop, Spark, NoSQL, NewSQL, MongoDB, Python, Tableau, Cognos
  • Corporate clients include DuPont, All-Scripts, Girnarsoft (College-, Car-) and many more
Read More >
Artificial Intelligence & Deep Learning Course Training - 360digitmg

Bhargavi Kandukuri

  • Areas of expertise: Business analytics, Quality management, Data visualisation with Tableau, COBOL, CICS, DB2 and JCL
  • Electronics and communications engineer with over 19+ years of industry experience
  • Senior Tableau developer, with experience in analytics solutions development in domains such as retail, clinical and manufacturing
  • Trained over 750+ professionals across the globe in three years
  • Worked with Infosys Technologies, iGate, Patni Global Solutions as technology analyst
Read More >
MLOps with Kubeflow Training online course certification - 360digitmg


Earn a certificate and demonstrate your commitment to the profession. Use it to distinguish yourself in the job market, get recognised at the workplace and boost your confidence. The MLOps with Kubeflow Course Certificate is your passport to an accelerated career path.

Alumni Speak

Nur Fatin

"Coming from a psychology background, I was looking for a Data Science certification that can add value to my degree. The 360DigiTMG program has such depth, comprehensiveness, and thoroughness in preparing students that also looks into the applied side of Data Science."

"I'm happy to inform you that after 4 months of enrolling in a Professional Diploma in Full Stack Data Science, I have been offered a position that looks into applied aspects of Data Science and psychology."

Nur Fatin

Associate Data Scientist

Thanujah Muniandy

"360DigiTMG has an outstanding team of educators; who supported and inspired me throughout my Data Science course. Though I came from a statistical background, they've helped me master the programming skills necessary for a Data Science job. The career services team supported my job search and, I received two excellent job offers. This program pushes you to the next level. It is the most rewarding time and money investment I've made-absolutely worth it.”

Thanujah Muniandy

Ann Nee, Wong

"360DigiTMG’s Full Stack Data Science programme equips its graduates with the latest skillset and technology in becoming an industry-ready Data Scientist. Thanks to this programme, I have made a successful transition from a non-IT background into a career in Data Science and Analytics. For those who are still considering, be bold and take the first step into a domain that is filled with growth and opportunities.”

Ann Nee, Wong

Mohd Basri

"360DigiTMG is such a great place to enhance IR 4.0 related skills. The best instructor, online study platform with keen attention to all the details. As a non-IT background student, I am happy to have a helpful team to assist me through the course until I have completed it.”

Mohd Basri

Ashner Novilla

"I think the Full Stack Data Science Course overall was great. It helped me formalize and think more deeply about ways to tackle the projects from a Data Science perspective. Also, I was remarkably impressed with the instructors, specifically their ability to make complicated concepts seem very simple."

"The instructors from 360DigiTMG were great and it showed how they engaged with all the students even in a virtual setting. Additionally, all of them are willing to help students even if they are falling behind. Overall, a great class with great instructors. I will recommend this to upcoming deal professionals going forward.”

Ashner Novilla


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FAQs for MLOps Engineering Course Training

Basic Degree is required. Basic knowledge of Maths and Statistics is needed to learn the tools.

More than 20 assignments are provided for the students to make them proficient. A dedicated team of mentors will guide the students throughout their learning process.

MLOps is needed to enhance the machine learning-driven applications in business. This enables Data Scientists to concentrate on their work and empowers MLOps engineers to take responsibility and handle machine learning in production.

Machine Learning operations are considered to be the most valuable practices any company can have. It helps in improving quality and delivering better performance.

We provide a career coach to help you to build your portfolio and prepare you for facing interviews. Job placement Assistance.

Yes, you can attend a free demo class and can interact with the trainer to clarify your queries.

Yes, students will be given more than 3 real-time projects under the guidance of industry experts.

Students after completing the course will be prepared for interviews. Guidance will be given by conducting mock interviews and questionnaires. This session will help students to boost their confidence and improve their communication skills.

We provide online training with flexible timings along with classroom sessions.

You can clarify your doubts with trainers, and mentors are provided for the students to whom you can approach at any time.

You will be given LMS access, which helps you to revise the course and if you miss any class you can see the recorded version of the class. You can attend webinars for free that will be conducted on trending topics for a lifetime and many more.

Jobs in the field of MLOps with Kubeflow Training in Malaysia

Jobs in the Field of MLOps Kubeflow in Malaysia

The most in-demand job profiles for MLOps Kubeflow in Malaysia are Machine learning Engineer, Data Scientist, MLOPs Manager, MLOps Engineer, Business Analyst, etc.

Salaries in Malaysia for MLOps with Kubeflow Training

Salaries in Malaysia for MLOps Kubeflow

The average salary for a Machine Learning Engineer is RM 48,610 at early level and at a mid-level, the average salary for a Machine Learning Engineer is RM 57,100 in Malaysia. The salary increases with experience and skills.

MLOps with Kubeflow course Projects in Malaysia

MLOps Kubeflow Projects in Malaysia

Many projects based on AI and Deep learning are in the process that is dependent on the applications of MLOps. MLOps can deploy AI models effectively. This is great news for AI-based industries to generate ROI from AI investments.

Role of Open Source Tools in MLOps with Kubeflow Training course in Malaysia

Role of Open Source Tools in MLOps Kubeflow

One of the prominent tools for MLOps is Kubeflow. and other tools are DVC( Data Version Control), Kubernetes, Tensorflow, and Pachyderm. Students are required to gain adequate knowledge of the varied applications of these tools. These tools become more prominent and users are also generating new patterns and recording end-to-end use cases, that can be helpful and can be used as best practices.

Modes of Training for MLOps with Kubeflow Training course in Malaysia

Modes of Training for MLOps Kubeflow in Malaysia

360DigiTMG, Malaysia offers world-class training in both online and classroom sessions at flexible timings. The timings are scheduled as per the feasibility of students and working professionals. Mentors are provided to students to help them throughout their learning process.

Industry Application of MLOps with Kubeflow in Malaysia

Industry Applications of MLOps Kubeflow

MLOps Kubeflow is gaining a lot of traction and many industries have realized its potential. It is being adapted in many sectors like Manufacturing, Telecommunications, Robotics, Banking, Finance, Retail, Education, Research and Development, and so on.

Companies That Trust Us

360DigiTMG offers customised corporate training programmes that suit the industry-specific needs of each company. Engage with us to design continuous learning programmes and skill development roadmaps for your employees. Together, let’s create a future-ready workforce that will enhance the competitiveness of your business.


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