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Best MLOps Course Training in Nigeria

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.
  • 40 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

Tab Four Description

Machine Learning - this is the buzzword that has everyone talking! Over the past few years, there has been a steady transition of Machine Learning from being strictly an academic discipline to a very exciting technological domain. The use cases are innumerable from analyzing videos from an autonomous vehicle (AV)s to providing highly personalized medical care, Machine Learning has become ubiquitous in every industry. However, most companies still have not been able to standardize the Machine Learning systems to become fully automated in a way that produces the models and results automatically. This has led to the birth of a new kind of discipline - Machine Learning Operations or MLOps for short. This field is still emerging but as companies look to leverage Machine Learning and Deep Learning to improve their business processes, MLOps Engineers will become one of the most sought after roles. It is estimated that 85% of most Machine Learning projects fail because among other things there is no standardized way of deploying these models to ‘production’. With this course, we aim to bridge the gap between and train MLOps Engineers that can deploy any model to production efficiently and quickly.

MLOps Course

MLOps course duration in Nigeria- 360digitmg

Total Duration

40 Hours

MLOps course pre-requisites in Nigeria- 360digitmg


  • Computer Skills
  • Basic Mathematical Concepts
  • Analytical Mindset

MLOps Course Overview

The MLOps Engineering course is a first of its kind program which tackles the subject of deploying the Machine Learning models in production and at scale. This program is born out of a frustration that we experienced while working on consulting engagements and trying to deploy Machine Learning projects into production. The challenges that any ML project faces is to ‘operationalize’ and ‘productionalize’ the code. There is no platform or guidelines that usually exist in other software engineering projects which makes it very difficult to deploy ML models quickly and efficiently. As part of this course, you will learn to deploy models into production environments using cutting edges open-source frameworks like Tensorflow Extended, Apache Beam, Apache Airflow, Kubernetes, and Kubeflow.

MLOps Course Learning Outcomes

This course has been meticulously and laboriously designed to be one of the pioneering works in the field of MLOps. While there is both a lot of demand and supply of Data Scientists, the market is experiencing a crushing shortage of MLOps engineers who can then convert the models into products and services that can be automatically deployed. This course is one of the first to offer MLOps training and will help the learners land coveted jobs as ML Engineers. ML projects have a lot of hidden technical debt as referenced in this wonderful paper. Unfortunately, the ML code will only be a very insignificant part of the entire codebase required to put an ML project into operation as shown in the below picture. So, this course addresses how an ML project can be quickly deployed into production with highly reusable pipelines.

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

40 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
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
    24/7 Support
  • MLOps certification
    Job Placements in Data Science Fields
  • MLOps course
    Complimentary Courses
  • MLOps course
    Unlimited Mock Interview and Quiz Session
  • MLOps Engineering training with placements
    Hands-on Experience in Capstone Projects
  • MLOps training
    Life Time Free Access to Industry Webinars

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MLOps Course Panel of Coaches

data scientist trainers

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 >
data scientist trainers

Sharat Chandra Kumar

  • Areas of expertise: Data sciences, Machine learning, Business intelligence and Data Visualization
  • 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 >
data scientist trainers

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

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 Course

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. 100% job placement is guaranteed.

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.

The average salary for a Machine Learning Engineer in Nigeria is up to $111,165 in early career and at midlevel, the salary would amount to $135,506 per annum.

For an experienced Machine Learning Engineer in Nigeria, the average salary is $147,575. The salary changes with experience and skills.

Jobs in the field of MLOps Engineer in Nigeria

Jobs in the Field of MLOps Course in Nigeria.

The popular job profiles for MLOps include Data Scientist, Machine Learning Engineer, Business Analyst, Principal Design Manager, Machine Learning Scale specialist, and henceforth.

Salaries in Nigeria for MLOps Engineer

Salaries in Nigeria for the MLOps Course

The average salary for a Machine Learning Engineer in the Nigeria is up to $111,165 in early career and at mid level, the salary would amount to $135,506 per annum. For an experienced Machine Learning Engineer in the Nigeria, the average salary is $147,575. The salary changes with experience and skills.

MLOps Engineer course Projects in Nigeria

MLOps Course Projects in Nigeria

ML is emerging and making better decisions in business-critical use cases, from sales to Business intelligence, Marketing, R&D, Executive, Production and Management.

Role of Open Source Tools in MLOps Engineer course in Nigeria

Role of Open Source Tools in MLOps Course

The most important and major tools of MLOps are DVC( Data Version Control), Pachyderm, and Kubeflow. Students are required to be proficient in these tools.

Modes of Training for MLOps Engineer course on in Nigeria

Modes of Training for MLOps Course

Training is provided through interactive live online sessions. Individual attention and personal mentorship are also provided for the students throughout their learning journey.

Industry Application of MLOps Engineer in Nigeria

Industry Applications of MLOps Course

MLOps is driving rapidly in many sectors that include IT, Finance, Telecommunications, Manufacturing, Retail, Education, Health care, 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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Enroll by April 10th

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