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Home / MLOps / MLOps with Kubeflow Training
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MLOps is an emerging field that is gaining momentum among Data Scientists, ML Engineers, and AI enthusiasts. MLOps is considered as the next destination for Data Scientists. It is used effectively by industries to develop and deploy data models. As per the new research reports, MLOps is predicted to grow rapidly in the coming years and is estimated to reach up to $4.5 billion by the end of 2025. With this tremendous growth companies are looking forward to adopting this innovation for better production. There is an urgent need for efficient skilled individuals in this discipline. 360DigiTMG always strives ahead and tries to bring a positive change in the IT industry by launching first of its kind training programs that help students to foster in their careers and achieve success.
Total Duration
2.5 Months
Prerequisites
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 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.
Block Your Time
60 hours
Classroom Sessions
40 hours
Assignments
Live Projects
Who Should Sign Up?
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:
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).
We can expect that MLOps technology will expand and become more important in the coming years as many organizations are willing to adopt ML into their production and utilize MLOPs applications to scale Machine Learning in their operations. Let’s look at a few trends of MLOps in the near future. One of the trends is the recognition of ML bugs which are different from software bugs. For Detecting Machine Learning bugs, special techniques are required like Model performance predictors, visual debugging tools, etc. Giant companies like Facebook and Uber have emphasized the importance of Machine Learning specific metrics in largescale that range from health checks to ML specific resource utilization metrics. Another trend is the usage of Cloud-based services and SaaS, which makes ML production simple and easy.
To deploy ML models at the beginning is quite difficult, even though many open source tools are available. Cloud services play a critical role in this situation. They are used to optimize accelerators. Cloud services also help organizations to develop ML deployment models without in-house large infrastructure. The availability of open-source tools for the deployment of ML models is another emerging trend. Many vendors are depending on these tools to provide total solutions to their customers. Some of the important tools are Tensorflow, Apache Atlas which is used for Governance, Kubeflow for MLops on Kubernetes, etc. Many big and small companies started adopting these tools for their ML production. MLOps is creating a wave, and many AI-based models are going to depend on MLOps for the successful deployment of models and effective results. This proves that there is much demand for MLOps professionals in the market where they can have a promising career.
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Bharani Kumar Depuru
Sharat Chandra Kumar
Bhargavi Kandukuri
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.
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Alumni Speak
"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
"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
"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
"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
"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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And more...
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.
The most in-demand job profiles for MLOps Kubeflow in Malaysia are Machine learning Engineer, Data Scientist, MLOPs Manager, MLOps Engineer, Business Analyst, etc.
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.
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.
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.
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.
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.
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.
Student Voices
4.8
I still remember going through websites for the Data Scientist course and reading reviews. That's when I finally found 360DigiTMG. Today, I will consider 360DigiTMG as the best one because it has offered me way more than any other training institute can do: Incredible curriculum, instructors, mentorship, and most job search skills and interviews best practices. One of the best decisions I made, I strongly recommend the Data Science program at 360DigiTMG to anyone looking for a Data Scientist/Analyst role. I am happy to announce that presently I am deputed as a Specialist Data Scientist at one of the top MNC.
I attended the Certification in Data Science program by 360DigiTMG. The course was amazing. I had a great learning & networking experience. Also, trainers are highly experienced and knowledgeable in the field of Data Science. I will recommend this course to anyone who wants to become a Data Scientist.
I joined the Data Science Certification Program which is a great place to learn if you want to start your career in the Data Science industry. The trainers are very knowledgeable and will try to explain the topics until you truly understand it. I would definitely recommend others to join it.
The courses are taught by seasoned industry experts who know how to train absolute beginners and established professionals alike. Glad to have trained under 360DigiTMG.
I worked on a Survival Analysis project, which gave me good exposure and boosted my skill set in Business Intelligence with Power BI. Good to have it more and be involved in the real-time projects.
360DigiTMG - Data Science, IR 4.0, AI, Machine Learning Training in Malaysia
Level 16, 1 Sentral, Jalan Stesen Sentral 5, Kuala Lumpur Sentral, 50470 Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia
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