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MLOps Course in Hyderabad

Certificate course in MLOps Engineering offers the first in the industry Machine Learning operations program which is a potent culmination of best trainers, innovative course material, and an AI-enabled LMS platform – AISPRY.
  • 60 Hours Interactive Online Sessions
  • 80 Hours Assignments
  • Complementary Kubernetes for Beginners
  • Complimentary DevOps for Beginners
  • Complementary Python Programming
MLOps Engineering course reviews - 360digitmg
485 Reviews
MLOps Engineering 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

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.

Course Fee

MLOps Course Overview

MLops is a combination of Machine Learning and IT Operations. It brings together Data Scientists and IT professionals to deploy ML models depending on the algorithms. This MLOPs course is first of a training program that is designed with the aim to fulfill the gap where industries are facing challenges in creating ML models in production and scale. This course introduces and explains to you in detail the cutting-edge tools that include Tensorflow Extended, Apache Airflow, Apache Beam, Kubernetes, and Kubeflow which are required to deploy ML models effectively. This course allows participants to work on real-time projects and gain hands-on experience and exposure to work in real-world applications. This course also prepares students to grab lucrative opportunities in giant companies.

MLOps Course Learning Outcomes

360DigiTMG offers the best MLOps training with a perfect blend of theory and practical sessions. The course curriculum is meticulously drafted including all the recent trends and prominent concepts that help students to be efficient and get hired by top-notch companies. This course is well crafted with essential topics, real-time projects, numerous assignments that help students to perceive the required knowledge and skill sets. There is a great shortage of professional MLOps engineers in the industry who can deploy and develop Machine Learning models. This course will help in building ML Engineers with the required capabilities that organizations are looking for. In this course, students will learn various tools Kubeflow, Apache Airflow, Apache Beam, and its applications. Able to deploy ML models efficiently and effectively.


Understand the need for MLOps in the world of data science
Familiarize yourself with Docker and the need for containerization
Become familiar with Tensorflow Extended (TFX) and its various components
Build data ingestion, validation pipelines using TFX
Build orchestrated ML pipelines using Kubeflow, Apache Airflow, Apache Beam
Gain a deep understanding of Kubernetes clusters and how they operate
Utilize the magic of Kubeflow pipelines to build, and deploy ML pipelines
Deploy models in the major cloud platforms - AWS, GCP, and Azure

Block Your Time

MLOps online course - 360digitmg

60 hours

Live Sessions

MLOps online course - 360digitmg

80 hours


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 Course

The following modules will take the student through the course in a step by step fashion building upon the foundations and progressing to advanced topics. Initially, the first module introduces the students to the general ML workflow and the different phases in an ML lifecycle. The subsequent chapters will introduce the participant to Tensorflow Extended (TFX) followed by a deep dive into its various components and how they facilitate the different phases of the ML lifecycle. The learner will then gain an understanding of how TFX components are used for data ingestion, validation, preprocessing, model training and tuning, evaluation, and finally deployment. Later chapters will also introduce the learner to the orchestration software Kubeflow, Apache Airflow, and Apache Beam. Using a combination of all these tools, the learner will be able to deploy models in some popular cloud platforms like AWS, GCP, and Azure.

One of the key benefits of investing in machine learning pipelines is that all the steps of the data science life cycle can be automated. As and when new data is available (for training), ideally an automated workflow should be triggered which performs data validation, preprocessing, model training, analysis, and deployment. A lot of data science teams spend ridiculous amounts of time, money and resources doing all these tasks manually. By investing in an ML workflow, these issues could be resolved. Some of the benefits include (but are not limited to):

  • Create new models, don’t get stuck maintaining Existing Models
  • Preventing and Debugging Errors
  • Audit Trail
  • Standardization

The Tensorflow Extended (TFX) library contains all the components that are needed to build robust ML pipelines. Once the ML pipeline tasks are defined using TFX, they can then be sequentially executed with an orchestration framework such as Airflow or Kubeflow Pipelines.


During this module, you will learn to install TFX and its fundamental concepts along with some literature which will make the future modules easier to understand. Additionally, you will learn Apache Beam which is an open-source tool that helps in defining and executing some data manipulation tasks. There are two basic purposes of Apache Beam in the TFX pipelines:


  • It forms the base of several TFX components for data preparation/preprocessing and validation
  • Is one of the orchestration frameworks for TFX components so a good understanding of Apache Beam is necessary if you wish to write custom components

In the previous modules, we set up TFX the ML MetadataStore. In this module, we discuss how to ingest data into a pipeline for consumption in various TFX components (like ExampleGen). There are several TFX components that allow us to ingest data from files or services. In this module we discuss the fundamental concepts, explore how to split the datasets into train and eval files and practically understand how to join multiple data sources into one all-encompassing dataset. We will also understand what a TFRecord stands for and how to interact with external services such as Google Cloud BigQuery. You will also learn how TFRecord can work with CSV, Apache Avro, Apache Parquet etc. This module will also introduce some strategies to ingest different forms of data structured, text, and images. In particular, you will learn

  • Ingesting local data files
  • Ingesting remote data files
  • Ingesting directly from databases (Google BigQuery, Presto)
  • Splitting the data into train and eval files
  • Spanning the datasets
  • Versioning
  • Working with unstructured data (image, text etc)

Data validation and preprocessing is essential for any machine learning algorithm to perform well. The old adage ‘garbage-in, garbage out’ perfectly encapsulates this fundamental characteristic of any ML model. As such, this module will focus on validation and preprocessing of data to ensure the creation of high performing ML models.


Data Validation: This module will introduce you to a Python package called Tensorflow Data Validation (TFDV) which will help in ensuring that


  • The data in the pipeline is in line with what the feature engineering step expects
  • Assists in comparing multiple datasets
  • Identifies if the data changes over time
  • Identify the schema of the underlying data
  • Identify data skew and data shift

Real-world data is extremely noisy and not in the same format that can be used to train our machine learning models. Consider a feature which has values as Yes and No tags which need to be converted to a numerical representation of these values (e.g., 1and 0) to allow for consumption by an ML model. This module focuses on how to convert features into numerical representations so that your machine learning model can be trained.


We introduce Tensorflow Transform (TFT) which is the TFX component specifically built for data preprocessing allowing us to set up preprocessing steps as TensorFlow graphs. Although this step of the model has a considerable learning curve it is important to know about it for the following reasons:


  • Efficiently preprocessing the data within the context of the entirety of the dataset
  • The ability to scale the data preprocessing steps efficiently
  • Develop immunity to potentially encountering training-serving skew

As part of the previous modules, we completed data preprocessing and transforming the data to fit our model formats. The next logical step in the pipeline is to begin the model training, perform analysis on the trained models and evaluate and select the final model. This module already assumes that you have the knowledge of training and evaluating models so we don’t dwell fully in the different model architectures. We will learn about the TFX Trainer component which helps us in training a model which can easily be put into production. Additionally, you will also be introduced to Tensorboard which can be used to monitor training metrics, visualize word embeddings in NLP problems or view activations for layers in a deep learning model.

During the model training phase, we typically monitor its performance on an evaluation set and use Hyperparameter optimization to improve performance. As we are building an ML pipeline, we need to remember that the purpose is to answer a complex business question modelling a complex real-world system. Oftentimes our data deals with people, so a decision that is made by the ML model could have far-reaching effects for real people and sometimes even put them in danger. Hence it is critical that we monitor your metrics through time—before deployment, after deployment, and while in production. Sometimes it may be easy to think that since the model is static it does not need to be monitored constantly, but in reality, the incoming data into the pipeline will more likely than not change with time, leading to performance degradation.


TFX has produced the Tensorflow Model Analysis (TFMA) module which is a fantastic and super-easy way to obtain exhaustive evaluation metrics such as accuracy, precision, recall, AUC metrics and f1-score, RMSE, MAPE, MAE among others. Using TFMA, the metrics can be visually depicted in the form of a time series spanning all the different model versions and as an add-on, it gives the ability to view metrics on different splits of the dataset. Another important feature is that by using this module it is easy to scale to large evaluation sets via Apache Beam. Additionally in this module, you will learn

  • How to analyse a single model using TFMA
  • How to analyse multiple models using TFMA
  • Checking for fairness among models
  • Apply decision thresholds with fairness indicators
  • Tackling model explainability
  • Using the TFX components Resolver, Evaluator and Pusher to analyze models automatically

This module is in many ways the crux of the MLOps domain because the original question was - ‘I have a great ML model prototype, how do I deploy it to production?’. With this module, we answer that question with - here is how: using Tensorflow Serving which allows ML engineers and data engineers to deploy any TensorFlow graph allowing them to generate predictions from the graph through its standardized endpoints. TF Serving takes care of the model and version control allowing for models to be served based on policies and the ability to load models from various sources. All of this is accomplished by focussing on high-performance throughput to achieve low-latency predictions. Some of the topics discussed in this module are:

  • How to export models for TF (TensorFlow) Serving
  • Signatures of Models
  • How to inspect exported models
  • Set up of TF Serving
  • How to configure a TF Server
  • gRPC vs REST API architecture
  • How to make predictions from a model server using
    • gRPC
    • REST
  • Conduct A/B testing using TF Serving
  • Seeking model metadata from the model server using
    • gRPC
    • REST
  • How to configure batch inference requests

Pipeline orchestration tool is crucial to ensure that we are abstracted from having to write some glue code to automate an ML pipeline. Pipeline orchestrators usually lie under the components introduced in the previous modules.


  • Decide upon the orchestration tool - Apache Beam vs Apache Airflow vs Kubeflow
  • Overview of Kubleflow pipelines on AI Platform
  • How to push your TFX Pipeline into production
  • Pipeline conversion for Apache Beam and Apache Airflow
  • How to set up and orchestrate TFX pipelines using
    • Apache Beam
    • Apache Airflow
    • Kubeflow
Tools Covered
MLOps course using r studio programming
MLOps course using apache air flow
MLOps course using apache tenserflow
MLOps course using r studio programming
MLOps course using kube flow
MLOps course using kubernetes

Course Fee Details

Virtual Instructor-led Training (VILT)
Mode of training: Live Online
  • Live online classes - weekends & weekdays
  • 365 days of access to online classes
  • Avail Monthly EMI At zero Interest Rate
  • Lifetime validity for LMS acces
  • 20+ live hours of industry masterclasses from leading academicians and faculty from FT top 20 universities
  • Career support services

INR 71,500

2454 Learners

136 Reviews

Employee Upskilling
Mode of training: Onsite or Live Online
  • On site or virtual based sessions
  • Customised Course
  • Curriculum with industry relevant use cases
  • Pre & Post assessment service
  • Complimentary basic Courses
  • Corporate based learning management system with team and individual dashboard and reports

2454 Learners

136 Reviews

Payment Accepted

All prices are applicable with 18% taxes.

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 course with USP
    24/7 Support
  • 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 Course 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 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 Engineer Course Certificate is your passport to an accelerated career path.

Alumni Speak

Pavan Satya

"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

Chetan Reddy

"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

Santosh Kumar

"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

Kadar Nagole

"Numerous advantages of the course. Thank you especially to my mentors. It feels wonderful to finally get to work.”

Kadar Nagole

Data Scientist

Gowtham R

"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

Wan Muhamad Taufik

"The instructors improved the sessions' interactivity and communicated well. The course has been fantastic.”

Wan Muhamad Taufik

Associate Data Scientist

Venu Panjarla

"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 Course Training

Prerequisites for learning MLOps include understanding ML basics, proficiency in programming (Python), knowledge of data engineering and cloud platforms (AWS, Azure), familiarity with DevOps principles, deployment tools (Kubernetes), security, compliance, workflow orchestration, model versioning, and strong communication skills for collaboration across teams.

Yes, as part of our MLOps training program, we provide assignments to reinforce learning. These assignments are designed to offer practical tasks like setting up CI/CD pipelines, deploying models on cloud infrastructure, and managing workflows. We also teach our students how to apply theoretical concepts in real-world scenarios, enhancing their understanding and practical skills in MLOps implementation.

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 prioritize our students' career advancement and provide support in job placement endeavors. While some MLOps training programs offer job placement assistance or partnerships with hiring companies, it's essential to research our specific program offerings thoroughly to understand the level of support we provide in terms of job placement.

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

Yes, we provide opportunities to handle real-time projects as part of our MLOps curriculum. Our platform and courses emphasize hands-on experience and practical application. We offer internships and entry-level positions for gaining further experience in MLOps projects , solidifying practical skills.

After course completion, students will receive interview readiness support, including mock interviews and questionnaires. These sessions aim to boost confidence and refine communication skills. With personalized guidance, students gain practical experience and learn how to effectively articulate their qualifications. This comprehensive approach ensures they are well-prepared to excel in interviews and secure positions aligned with their career goals.

We even provide online courses tailored for busy professionals. Our flexible schedules, remote learning options, and self-paced modules enable you to balance work and education seamlessly. Accessible from anywhere, you can download resources and enhance your skills at your convenience, advancing your career on your terms.

You can resolve doubts through personalized explanations from in-person instructors in class. Additionally, for those studying online, our experts provide assistance through forums. We also offer reference materials to supplement learning, aiding in understanding and clarifying concepts effectively.

In addition to course access, you'll receive LMS access for course revision and viewing recorded classes if you miss any. Enjoy lifelong access to free webinars on trending topics and other benefits such as ongoing learning opportunities, ensuring you stay updated and informed even beyond course completion.

Jobs in the field of MLOps Engineer in Hyderabad

Jobs in the Field of MLOps Course in Hyderabad

The field of MLOps in Hyderabad offers diverse roles like Data Scientist, Machine Learning Engineer, Lead Design Manager, Business Analyst, and Machine Learning Scale Specialist. Candidates with varied skills in deploying ML systems and strong teamwork are highly sought after for successful implementation.

Salaries in Hyderabad for MLOps Engineer

Salaries in Hyderabad for MLOps Course

In Hyderabad, MLOps course graduates' salaries vary by experience and skills. Entry-level Machine Learning Engineers earn about Rs.5,02,135, mid-career professionals around Rs.11,59,032, and Experienced Engineers approximately Rs.19,48,728. Those skilled in specific ML areas may earn up to Rs.19,96,000. Salary depends on factors like experience, expertise, and industry demand.

MLOps Engineer course Projects in Hyderabad

MLOps Course Projects in Hyderabad

Hyderabad's MLOps sector is rapidly growing, with IT firms leveraging its potential. A key project utilised MLOps to develop AI software for laptop servicing estimation and automatic updates, tested on 3100 notebooks, demonstrating practical application.MLOps is going to change the future of the IT industry generating more useful and innovative projects depending on AI.

Role of Open Source Tools in MLOps Engineer course in Hyderabad

Role of Open Source Tools in MLOps Course

360DigiTMG delivers training in interactive live online sessions. The online training is scheduled at flexible timings as per the interest of participants. 1:1 mentorship is provided to the participants to guide throughout their learning process.

Modes of Training for MLOps Engineer course on in Hyderabad

Modes of Training for MLOps Course

360DigiTMG, our program exclusively provides comprehensive online training, ensuring flexibility for diverse schedules. Our platform supports self-paced learning and 1:1 mentorship, enriching students' course experience. Proficiency in vital tools such as DVC, Pachyderm, and Kubeflow is emphasised.

Industry Application of MLOps Engineer in Hyderabad

Industry Applications of MLOps Course

MLOps finds widespread application in diverse sectors including Manufacturing, Telecommunications, Robotics, Banking, Finance, Retail, Education, and Research and Development. It optimises processes, enhances decision-making, and drives innovation, contributing to efficiency improvements and transformative advancements across various industries.

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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360DigiTMG – Data Analytics, Data Science Course Training Hyderabad

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