Certificate Course in
MLOps with Kubeflow Training in India
- 60 Hours Interactive Online Sessions
- 80 Hours Assignments & Real-Time Projects
- Complimentary ML on Cloud Modules
- Complimentary Python Programming Course
- Complementary Kubernetes for Beginners
- Complimentary DevOps for Beginners
485 Reviews
2064 Learners
Academic Partners & International Accreditations
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.
MLOps with Kubeflow
Total Duration
2.5 Months
Prerequisites
- 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.
Block Your Time
Who Should Sign Up?
- Data Scientists
- Data and Analytics Manager
- Business Analysts
- Data Engineers
- DevOps Engineers
- IT/Software 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
Trends of MLOps Kubeflow in India
The term MLOps or Machine Learning operations started emerging in 2020 and became widely used with Artificial Intelligence and Machine Learning. Many enterprises started adopting AI technologies, which led to the development and high usage of tools and techniques for the processes to work efficiently and effectively develop ML models combined with DevOps. Let’s see some of the trends that organizations expect from MLops and how these help Data Scientists to develop and deploy models from research to production. The emerging trend is that ML models have become more scalable compared to earlier where they were brittle ML pipelines. Models that are deployed by MLOPs are robust and are production-ready with high performance. From Day 1 they work efficiently and continue to grow. ML models because of their varied key product features are being used in major sectors like Healthcare, Security, Banking, etc and they are generating a lot of revenue.
Enterprises like Banking and Insurance companies use hundreds of models, by this they face different kinds of challenges. These challenges arise due to heterogeneity in workflows of ML and there is no route to track the source and assets of models across the organization. This issue can be resolved by MLOPs. MLOps tools like Kubeflow will give the organizations the visibility of the models to avoid duplication and liabilities. MLOPs brings together Data Scientists and the DevOps team to work and bring out significant outputs. . By observing the impact of MLOps on the development and deployment of ML models in various industries, we can certainly stay that it is going to stay and continue in the future creating many opportunities for the aspirants.
How We Prepare You
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Additional Assignments of over 80+ hours
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Live Free Webinars
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Resume and LinkedIn Review Sessions
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Lifetime LMS Access
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24/7 Support
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Job Placements in Data Science Fields
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Complimentary Courses
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Unlimited Mock Interview and Quiz Session
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Hands-on Experience in Capstone Projects
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Life Time Free Access to Industry Webinars
Call us Today!
+91 9989994319MLOps with Kubeflow Panel of Coaches
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
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
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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
Certificate
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
"The training was organised properly, and our instructor was extremely conceptually sound. I enjoyed the interview preparation, and 360DigiTMG is to credit for my successful placement.”
Pavan Satya
Senior Software Engineer
"Although data sciences is a complex field, the course made it seem quite straightforward to me. This course's readings and tests were fantastic. This teacher was really beneficial. This university offers a wealth of information."
Chetan Reddy
Data Scientist
"The course's material and infrastructure are reliable. The majority of the time, they keep an eye on us. They actually assisted me in getting a job. I appreciated their help with placement. Excellent institution.”
Santosh Kumar
Business Intelligence Analyst
"Numerous advantages of the course. Thank you especially to my mentors. It feels wonderful to finally get to work.”
Kadar Nagole
Data Scientist
"Excellent team and a good atmosphere. They truly did lead the way for me right away. My mentors are wonderful. The training materials are top-notch.”
Gowtham R
Data Engineer
"The instructors improved the sessions' interactivity and communicated well. The course has been fantastic.”
Wan Muhamad Taufik
Associate Data Scientist
"The instructors went above and beyond to allay our fears. They assigned us an enormous amount of work, including one very difficult live project. great location for studying.”
Venu Panjarla
AVP Technology
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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.
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 Kubeflow in India
The most in-demand job profiles for MLOps Kubeflow in India are Lead Machine learning Engineer, Machine Learning Application developer, DevOps Engineer, Data Scientist, MLOPs Manager, and so on.
Salaries in India for MLOps Kubeflow
The average salary for a Machine Learning Engineer in India is Rs 5,06,645 at an early level and at a mid-level, the average salary for a Machine Learning Engineer is Rs 12,13,554. For experienced, the average salary is Rs 20,26,043 approximately.
MLOps Kubeflow Projects in India
The ready to use MLOps tools are making the ML models less complex and are going to bring more ML products successfully. The AI-based industries are adopting MLOps in their upcoming projects.
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 will gain hands-on experience by working on live projects using these tools.
Modes of Training for MLOps Kubeflow in India
360DigiTMG, India offers world-class training in Interactive online sessions at flexible timings. Working professionals can choose the timings as per their convenience. Individual attention is provided to the aspirants by a team of dedicated mentors.
Industry Applications of MLOps Kubeflow
MLOps Kubeflow is emerging and is going to take off in the near future. It is being adapted in many sectors like Telecommunications, Robotics, Banking, Manufacturing, Healthcare, 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.
Student Voices
Sharvin Rao
2 years agoVery good exposure. Satisfied with this program. Teaching materials are complete and does not require any programming background to learn this course
Priya Gopal
2 years agoVery experienced trainer and have patience to deal with every query raised in the classroom.
Lavaniya Rajesveran
2 years agoGreat place to learn about Data Science . Trainers are knowlegable and shared lots of new terms which was easily understandable.
Nivethitha Sivaraj
2 years agoThe course I selected was Data science using Python and R. Beginning it feels quite easy but days pass by we can feel the depth of the subject. Trainers doing their awesome job. I would seriously recommend for the people who are looking for a career uplift and even for freshers. Apart from trainers it’s our job to practice and practice to make it more perfect.
Gaurav Joshi
3 years agoData Science is aptly designed to cover most of the aspects of Data Science using R and Python. Anyone who is unsure of how to step in and explore the world of Data Science should join this course. Kudos to 360DigiTMG team to come up with such an elaborated program and helping students to put a confident foot forward in Data Science space.
Anita Nook Naidu
3 years agoVery good lecturers. Good training. Plenty of guidance provided. worth the money.
