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Professional Certificate in MLOps

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 Hybrid Sessions (Virtual & Self-paced)
  • 60 Hours Streaming Hours & Coursework
  • 30 Hours Real-Time Industry Projects
  • Complementary Kubernetes for Beginners
  • Complementary ML on Cloud Modules
  • Complimentary DevOps for Beginners
  • Complementary Python Programming
MLOps Engineering course reviews in Malaysia- 360digitmg
485 Reviews
MLOps Engineering course reviews in Malaysia- 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
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Introduction

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 Engineering Course

MLOps course duration in malaysia- 360digitmg

Total Duration

4 Months

MLOps course pre-requisites in malaysia - 360digitmg

Prerequisites

  • Data Science - Traditional ML algorithms & DL (Neural network) algorithms
  • Programming - Beginner to Intermediate

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.

 

What is MLOps?

 

MLOps, which is also known as Machine Learning Operations, weaves DevOps philosophy into the processes and tools of machine learning. It involves the whole process of data preparation, Model training, deployment, monitoring, and management which is committed to the efficacious deployment and routine maintenance of ML models in production. Automation, scalability and governance are the key letting factors because they boost collaboration and dependability as well as decrease operational costs.

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

40 hours

Hybrid Sessions (Virtual & Self-paced)

MLOps online course - 360digitmg

60 hours

Streaming Hours & Coursework

data science with AI

30 hours

Real-Time Industry 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 Course Training

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

Online Interactive Sessions
Mode of training: Live Online
  • 10+ hours of live online doubt clarification sessions
  • Free access to USD 500 worth study materials - mindmaps, digital book on Data Science & many more
  • Blockchain security enabled tamper-proof certificate(s)
  • Real-life industry-based projects with AiSPRY
  • 100% HRD Corp-claimable courses
  • Free Learning Management System Access

MYR 4,600

Minimum instalments cost starting from: MYR 1,656

Learners 2287 Learners

Rating 629 Reviews

Employee Upskilling
Mode of training: Onsite or Live Online
  • Minimum 10 participants per batch for Onsite training
  • Pre & Post Assessment Services
  • Blockchain security enabled tamper-proof certificate(s)
  • Customised Curriculum with industry-relevant use cases
  • 100% HRD Corp-claimable courses
  • Free Learning Management System Access

Corporate Group DiscountsUp to 30% for 3 participants & above

Learners 2287 Learners

Rating 629 Reviews

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All prices are applicable with 8% 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
    LMS Access for 6 Months
  • MLOps certification
    Job Placements in Practical 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 Engineering 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 in Malaysia- 360digitmg

MLOps 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 Engineer Course Certificate is your passport to an accelerated career path.

**All certificate images are for illustrative purposes only. The actual certificate may be subject to change at the discretion of the Certification Body.

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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

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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

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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

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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

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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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FAQ's for MLOps Engineering Course

Prerequisites for MLOps include a bachelor's degree and basic understanding of maths and statistics. Familiarity with machine learning concepts and programming languages like Python is beneficial. Additionally, knowledge of data engineering techniques and software engineering practices, including version control, testing, and agile development, is essential for building scalable and maintainable ML systems.

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.

Our program includes comprehensive job placement support, offering coaching and interview preparation. We assist students in acquiring the essential skills and knowledge for MLOps roles and guide them through the job search process. This support enables candidates to effectively demonstrate their abilities and secure positions in companies implementing machine learning operations.

Yes, students have the opportunity to attend a free demo to explore our program. They also benefit from hands-on experience through three real-time projects, supported by mentor guidance. This practical approach enhances their skills and prepares them effectively for real-world challenges in MLOps roles.

Yes, our MLOps course emphasises hands-on experience with real-time projects. You'll deploy, monitor, and manage machine learning models in production settings, applying concepts directly to practical scenarios. These projects offer invaluable experience, preparing you effectively for MLOps roles. With mentor guidance, you'll gain confidence in handling real-world challenges, ensuring you're well-equipped to excel in the field of machine learning operations.

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 offer online training for professionals who cannot attend classroom sessions. Our flexible schedule allows you to access courses at your convenience, ensuring you can balance work and learning effectively. With our online platform, you can study from anywhere, at any time, and receive the same high-quality education as classroom attendees.

We'll connect you with mentors to clear up any of your doubts. Our platform offers forums and mentorship opportunities. Engaging in hands-on projects enhances understanding, while interactions with peers and mentors enrich the learning experience. Mentors provide personalised guidance for thorough comprehension, ensuring students grasp MLOps principles confidently and are well-prepared for challenges in machine learning operations.

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 Malaysia 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 Malaysia, the average salary is $147,575. The salary changes with experience and skills.

The role of an MLOps engineer involves implementation, maintenance, and fine-tuning of machine learning models in production landscapes. They fill in the lacuna between data mining and operations by facilitating seamless insertion of the models into systems, constant checking, and scalability. MLOps engineers apply automation and DevOps principles to make it easy to pass through different phases of the machine learning process.

The salary of an MLOps engineer is influenced by the environment, by their experience, and also. The scope of the company they work for. The demand of MLOps engineers in the market is soaring with the jobs paying average annual salaries of ?21.3 lakhs to ?56.7 lakhs. They typically receive their compensation based on their company and their location on the earth.

MLOps tools streamline the machine learning lifecycle, aiding model development, deployment, monitoring, and management. They incorporate version control, CI/CD, training/testing, monitoring, and infrastructure management. Popular tools include Git, Docker, Kubernetes, TensorFlow Extended (TFX), MLflow, and Kubeflow, catering to the needs of ML engineers in production environments.

Choosing the ideal MLOps platform hinges on factors like company needs, team skills, scalability, and budget. Leading systems such as KubeFlow, MLflow, TFX, Seldon, and DataRobot offer essential components: deployment, management, and scaling. Institutions like 360DigiTMG offer training and consulting, enhancing expertise. Testing interoperability, functionalities, and learning curve is vital.

Jobs in the field of MLOps Engineer in Malaysia

Jobs in the Field of MLOps Engineer Course in Malaysia

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 Malaysia for MLOps Engineer

Salaries in Malaysia for the MLOps Course

The average salary for a Machine Learning Engineer in the Malaysia 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 Malaysia, the average salary is $147,575. The salary changes with experience and skills.

MLOps Engineer course Projects in Malaysia

MLOps Engineer Course Projects in Malaysia

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 Malaysia

Role of Open Source Tools in MLOps Engineer 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 Malaysia

Modes of Training for MLOps Course

Training is provided through online mode as well as classroom sessions. Individual attention and personal mentorship are also provided for the students throughout their learning journey.

Industry Application of MLOps Engineer in Malaysia

Industry Applications of MLOps Engineer 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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360DigiTMG - Data Science, IR 4.0, AI, Machine Learning Training in Malaysia

Level 16, 1 Sentral, Jalan Stesen Sentral 5, Kuala Lumpur Sentral, 50740 Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia

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