DO YOU KNOW?
According to Fortune Business Insights, the global MLOps market size is valued at $2.33 billion by 2025. However, it is projected to grow to $19.55 billion by 2032.
If you don’t know what MLOps is, it is the combination of machine learning, data engineering, and DevOps practices to build and maintain machine learning models reliably and consistently. With the surge of AI technology across all industries, its applications are being widely employed by organizations and companies. That’s why they require professionals adept with AIOps and MLOps, helping them to automate operations and boost productivity.
This blog thoroughly discusses the best MLOps certifications, along with the reasons for pursuing them, career paths, salary, and exam preparation.
According to Exploding Topics, the search volume for MLOps has surged from 4.7K to 95K within the span of five years.
What is an MLOps Certification?
As MIOps deals with maintaining machine learning models, an MLOps certification is a professional qualification that validates an individual’s knowledge and skills in DevOps for machine learning. It significantly focuses on the automation, management, and operationalization of machine learning models throughout their entire lifecycle.
With this certification, professionals demonstrate their expertise in deploying, scaling, monitoring, and maintaining machine learning systems in production environments, resulting in more efficient, reliable, and reproducible processes. Earning this certification exponentially helps one to boost one’s career, demonstrates expertise, meets industry demand, and equips one with the best practices.
DO YOU KNOW?
According to a Gartner report, only 54% of AI and machine learning projects make it from pilot to production phases.
Pursuing MLOps certifications is beneficial for professionals, including data engineers, ML engineers, DevOps engineers, data scientists, cloud infrastructure engineers, and AI/ML project managers.
Why Pursue MLOps Certifications?
When discussing the best MLOps certifications, understanding why they matter is important. Here are the prominent reasons why you must pursue them:
Look Further: Best Machine Learning Certifications That Will Uplift Your Career In 2026
A. To boost career growth
The main reason for pursuing MLOps certifications is to boost career growth. The demand for individuals with knowledge of ML technologies has increased tremendously. Companies now require employees who can manage their machine learning systems while automating their key tasks.
Having an MLOps certification can significantly make one more favorable and desirable to the hiring manager, increasing their chances of getting hired by leading tech firms.
B. To ensure credibility
While formal degrees show the qualification of an individual, they don’t validate their experience and expertise in a specific area. However, MLOps certifications efficiently validate skills. They demonstrate the skills and capabilities of individuals to employers in deploying and managing ML models.
MLOps certifications are highly trusted because they are only awarded to individuals who perform well not only in the exam but also on a practical level.
C. To get a competitive edge
Having an MLOps certification also exponentially gives you a competitive edge. While having a PhD opens a lot of opportunities for individuals, they might not be equipped with the expertise and experience that hiring managers seek.
Individuals with MLOps certifications have hands-on experience in deploying and managing ML systems, giving them a competitive advantage over individuals without any MLOps certification.
D. To enhance skills
Having new technical and soft skills is also very beneficial in a digital landscape. When newbies or professionals pursue an MLOps certification, they acquire enhanced knowledge and skills of ML systems and technologies.
Whether they already have any skills or not, earning this certification certainly helps them to learn a variety of skills that can be extremely helpful throughout their lives.
E. To improve the experience
MLOps certifications also exponentially enhance hands-on experience for individuals. It’s because they involve various projects simulating real-world challenges, allowing candidates to employ their critical thinking, problem-solving, and data management skills.
In this way, when they are faced with challenges with realistic conditions or real-life industry issues, they tackle them effortlessly, as it is not the first time for them.
Top MLOps Certifications To Pursue in 2026
As we have discussed the reasons for pursuing MLOps certification, now it’s time to explore the top MLOps certifications in 2026. Explore them now and start or advance your career in ML technologies.
1) AWS Certified Machine Learning - Specialty
The first MLOps certification on our list is the AWS Certified Machine Learning – Specialty. It is a professional credential that validates your expertise in building and deploying machine learning solutions in the AWS Cloud. This significantly demonstrates to employers that you can architect ML/deep learning workloads, optimize model training, and implement production-ready ML systems following AWS best practices.
To help candidates for the exam, AWS offers them a Certification Official Question Set to understand pre-exam-style questions. They also enable them to get enrolled in digital courses that can remarkably fill gaps in knowledge and skills. To register for this exam, candidates typically need at least two years of hands-on experience in building and running ML/deep learning workloads in the AWS cloud.
2) Machine Learning Operations (MLOps): Getting Started
Offered by Google Cloud, the Machine Learning Operations (MLOps): Getting Started is a beginner-friendly certification that introduces MLOps tools and best practices. It covers foundational knowledge for deploying, evaluating, monitoring, and operating production ML systems on Google Cloud.
This credential is ideal for individuals who want to start a career in AI or MLOps, such as data scientists, software or ML engineers, developers, or other professionals. After completing this certification, you can earn a skill badge from Google Cloud. While some providers offer individuals a “getting started” bundle with other training, enabling them to pursue different certifications at a lower cost.
3) Professional Machine Learning Engineer Certification
Another certification on the list of MLOps is the professional machine learning engineer, which is offered by Google Cloud. It validates a professional’s ability to design, build, and productionize machine learning models using Google Cloud technologies to solve business problems.
This certification significantly assesses skills in machine learning concepts, data preparation, model development, pipeline automation, and the deployment, monitoring, and optimization of ML solutions on the Google Cloud Platform (GCP). The exam evaluates a professional’s ability to frame ML problems and architect ML solutions, prepare and process data for ML models, and develop, train, and deploy ML models on GCP.
It is designed for ML engineers with experience in designing and managing ML solutions, ideally with at least one year of experience using Google Cloud.
4) Project Pro Practical MLOps Online Course with Certification
Practical MLOps Online Course with Certification, offered by ProjectPro, is a credential that teaches individuals about the principles and tools of machine learning operations (MLOps). It is equivalent to real-world industry experience, which prepares you for the demands of the AI industry. This certification is ideal for data engineers, computer science graduates, software developers, and data science professionals.
With this remarkable certification, you’ll not only be theoretically equipped but also practically adept to deploy, manage, and scale machine learning models from a development environment to a production system. To pass the exam, you must learn all the course topics properly, such as ML pipelines, experiment tracking, deployment, orchestration, mentoring, and maintenance.
5) Microsoft AI & ML Engineering Professional Certificate
Microsoft AI & ML Engineering Professional Certificate is a five-course certification designed by Coursera. It is an intermediate-level program that teaches individuals practical skills in building, training, and launching real-world AI systems using Microsoft Azure. It widely covers foundational concepts, such as AI algorithms, data pipelines, and practical application through hands-on labs and projects.
To register for this certification, you don’t need a formal prerequisite. However, you must have a basic understanding of Python, AI, and machine learning concepts. While it offers multiple courses throughout, it develops a solid foundation of AI and ML infrastructure in candidates. Not only does it improve their theoretical understanding, but it also hones their practical and job-ready skills for designing and implementing AI solutions efficiently.
6) Duke University DevOps, DataOps, MLOps
Offered by Duke University, DevOps, DataOps, and MLOps are an excellent certification part of the Machine Learning Operations Specialization. It focuses on teaching relevant principles and expertise on automating and streamlining the entire lifecycle of software development, data management, and machine learning models. Once you enrol for this program, you’ll automatically be enrolled for its parent certification.
This certification will significantly equip you with a variety of technical skills, such as machine learning, Rust, Docker, GitHub, DevOps, and web frameworks. Moreover, you’ll learn to address various challenges in deploying, maintaining, and scaling ML models in production. Duke University offers online content and materials to help candidates prepare for the final exam and achieve a satisfactory score to complete their certification.
7) Microsoft Certified: Azure Data Scientist Associate
Azure Data Scientist Associate is also a well-known MLOps certification that teaches professionals to manage data ingestion and preparation, model training and deployment, and machine learning solutions monitoring with Python, Azure Machine Learning, and MLflow. Offered by Microsoft, this certification validates a professional’s skills and expertise in implementing machine learning workloads on the Microsoft Azure platform.
As a candidate, you’ll be able to demonstrate proficiency in using Azure tools and services, making you a strong candidate for data science roles. To achieve this certification, you must pass the DP-100 exam. The key skills you’ll learn in this certification are data wrangling, advanced machine learning techniques, data preparation, exploration, and monitoring ML models.
8) Databricks Certified Machine Learning Professional
The Databricks Certified Machine Learning Professional is an advanced credential that validates an individual’s ability to use the Databricks Machine Learning platform for production-level machine learning. It focuses on experimentation, model lifecycle management, deployment, and monitoring using tools like MLflow. The exam tests expertise in areas such as batch and streaming inference, feature store management, and model drift detection.
It is a proctored certification, but it doesn’t require any formal prerequisites. All it requires from candidates is to have one year of hands-on experience performing the machine learning tasks outlined in the exam guide. It has a total of 60 questions and is required to be completed within 120 minutes. This certification is ideal for professionals who want to showcase their ability to build, deploy, and manage machine learning models effectively.
9) DeepLearning.AI Machine Learning in Production
The Machine Learning in Production is a specialized certification that teaches to bridge the gap between theoretical ML knowledge and practical skills needed to build, deploy, and maintain machine learning systems in real-world production environments. It prepares professionals to tackle various challenges, such as evolving data, error analysis, concept drift, and continuous integration.
With this certification, you’ll learn how to design a production machine learning system with its many processes, such as project scoping, data needs, modeling strategies, and deployment patterns and technologies. Moreover, you’ll follow a framework for developing, deploying, and continuously improving a productionized ML application. It prepares you for roles like MLOps engineer, machine learning engineer, and data engineer.
10) Intel Certified Developer - MLOps Professional Curriculum
Offered by Intel, the Certified Developer – MLOps Professional curriculum is a self-paced training program that teaches the skills needed to build and manage machine learning models in production, covering MLOps principles, CI/CD practices, model deployment, and infrastructure optimization for Intel hardware and cloud platforms.
The curriculum includes self-paced modules with video lessons and hands-on labs, a live workshop for a capstone project, and a proactive exam to prepare for the proctored certification exam. It is ideal for those professionals who are planning to start their MLOps career, as it equips them with extensive knowledge and experience deploying and managing machine learning solutions and technologies.
11) eCornell Machine Learning Certificate
Offered by Cornell University, the eCornell Machine Learning Certificate is an online professional program that teaches individuals or organizations how to implement machine learning algorithms using Python. You will use a combination of math and intuition to practice framing machine learning problems and construct a mental model to understand how data scientists approach these problems programmatically.
This program covers key machine learning concepts like supervised and unsupervised learning. It provides hands-on experience with real-world datasets through projects and includes topics like data preparation and feature engineering. As a result, you will be well-prepared to transform your raw data into a format suitable for modeling. This certificate aims to equip you with skills that are in high demand by employers in the rapidly growing AI field.
12) Pearson MLOps for Machine Learning
MLOps for Machine Learning is an online certification offered through Coursera that teaches the best practices for managing the full machine learning (ML) lifecycle. It covers the end-to-end process, from data collection and model creation to deployment and monitoring in a production environment. This credential is designed for machine learning engineers to enhance automation and reproducibility in real-world projects.
The key topics that candidates must be equipped with are the MLOps pipeline, data handling, model creation, production models, and workflow automation. You’ll also be required to submit five assignments to meet your obligations for this certification. After completing this certification, you’ll have the skills and expertise to ensure reliability, enable continuous improvement, and promote collaboration between teams.
What to Do After Earning an MLOps Certification?
After you have completed your MLOps certification, these are some career paths that you can pursue:
⭕MLOps Engineer: An MLOps engineer is a professional who efficiently bridges the gap between machine learning (ML) model deployment and production. He focuses on deploying, managing, and scaling ML models to improve efficiency in production environments.
⭕Machine Learning Engineer: A machine learning engineer is an individual who designs, builds, and deploys artificial intelligence systems by writing production-level code that brings models to life. In short, he provides tangible business value beyond theoretical exploration.
⭕Data Scientist: A data scientist is an expert who extracts actionable insights from raw data. He efficiently blends different fields, such as statistics, computer science, and domain knowledge, to solve complex problems and guide business decisions.
⭕NLP (Natural Language Processing) Engineer: A professional who employs machine learning technologies to design, develop, and implement algorithms and models that allow computers to understand, interpret, and generate human language.
⭕Data Analyst: A data analyst is an individual who collects, cleans, and interprets complex data sets. This helps identify trends, solve business problems, and help organizations make informed decisions through data-driven insights.
⭕AI Research Scientist: A professional who conducts cutting-edge research to advance the field of artificial intelligence by developing new algorithms, techniques, and methodologies for machine learning and natural language processing.
⭕Business Intelligence Developer: A Business Intelligence (BI) developer is an individual who designs, builds, and maintains systems that transform raw business data into actionable insights through dashboards, reports, and data models.
⭕ML Product Manager: An ML project manager is a professional who oversees the planning, execution, and completion of AI/ML projects. He bridges the gap between technical teams, such as data scientists and engineers, and business stakeholders.
Click here: AWS Machine Learning Engineer Associate Certification: Cost, Jobs, And Salary (Updated 2026)
How to Choose the Right MLOps Certification?
With multiple options given, choosing a specific MLOps certification can be challenging for some individuals. However, by taking these steps, you can efficiently select a certification that meets all your career goals:
Step 1: Determine your goals
The first step when choosing an MLOps certification is to determine your goals. Are you a beginner who wants to gain foundational knowledge in MLOps? Or are you an existing professional in machine learning aiming to enhance your understanding and expertise?
See why you’re taking a specific certification. Also, think about whether you have long-term or short-term goals.
Step 2: Understand the prerequisites
Once you are clear about your goals, check out the prerequisites or experience required for those certifications. Some MLOps certifications are entry-level and foundational, so they don’t need any formal requirements.
However, advanced-level certifications require specific qualifications, a defined skill set, and relevant credentials. They might also require familiarity with specific tools and languages.
Step 3: Assess the focus of certifications
When choosing an MLOps certification, you must also assess the focus of all. Every certification has a unique focus and equips candidates with different skills and experience. Therefore, understand what each certification offers and ensure that they match your learning needs.
For instance, AWS and Azure certifications are more aligned with cloud services, whereas the eCornell certification provides a more academic approach.
Step 4: Consider your budget
Cost is also a crucial factor to consider when selecting an MLOps certification. Some certifications are free of cost and don’t require any cost from candidates, while others require a certain amount as a registration cost.
Remember, a certification is a way to start or advance your career. So, choose one that you can easily afford without straining your finances.
Step 5: Think about the exam setting
While most MLOps certifications offer online training, their exam setting can differ. They include proctored exams that require candidates to appear in exam centers or attempt the exam online.
Therefore, select a certification that provides exam settings tailored to your specific circumstances.
What Do You Earn After MLOps Certifications?
As the demand for professionals with MLOps is high, companies are paying substantial amounts to candidates who meet their criteria. According to Glassdoor, the average annual salary of an MLOps engineer ranges from $132,000 to $199,000, which rounds up to a median of $166,000 per year.
This salary applies to starting or intermediate roles; however, it may be higher for senior or leadership positions. Moreover, the factors that impact the salaries of MLOps-certified professionals are experience level, geographical location, company size, industry, and skill set.
Preparing for Your MLOps Certification Exam
Choosing and getting registered for a specific MLOps certification is not enough; you must also prepare for your exam to complete your preferred certification efficiently.
Many organizations offer official resources, materials, and training programs related to MLOps certifications, helping individuals thoroughly prepare for their exams. Many providers also offer video resources featuring top industry professors who give lectures on machine learning topics and trends, further enhancing preparation.
Practicing your exam is also an effective way to prepare. For that purpose, use Dumpsgate’s well-crafted, well-structured exam dumps and prepare your MLOps certification exam efficiently.
Final Thoughts
In this blog, we have explored how the field of MLOps is thriving, and the need for professionals with related knowledge and experience is on the rise. However, if you want to kick-start or boost your career in MLOPs, getting access to the best MLOps certifications is essential. Along with the top certifications, we have checked what career paths one can pursue upon certification completion.
Frequently Asked Questions (FAQ's)
Is an MLOps certification worth it?
Yes, MLOps certifications validate your skills in managing and deploying machine learning efficiently. As a result, you become an essential and valuable asset to operational and development teams.
Are there any prerequisites for pursuing MLOps certifications?
Whether there are any prerequisites or not, it all depends on the MLOps certification that you have chosen. Some basic-level programs might not require them, while expert-level programs might require them. However, one must have a basic understanding of relevant concepts.
How can I choose the best MLOps certification for me?
To choose the best MLOps certification, determine your goals, understand the prerequisites, assess the focus of certifications, consider your budget, and think about the exam setting.
Why must you go for an MLOps certification?
There are several reasons to pursue MLOps certifications, including boosting career growth, ensuring credibility, gaining a competitive edge, enhancing skills, and improving the overall experience.
What is the difference between AIOps and MLOps certifications?
While AIOps certifications focus on applying AI to automate and improve OT operations, MLOps certifications focus on the lifecycle management of machine learning models, from development to deployment and monitoring.
Sources
- FortuneBusinessInsights. “MLOps Market Size, https://www.fortunebusinessinsights.com/mlops-market-108986#:~:text=The%20global%20MLOps%20market%20size%20was%20valued%20at%20USD%C2%A01.58%20billion%20in%202024.%20The%20market%20is%20projected%20to%20grow%20from%20USD%C2%A02.33%20billion%20in%202025%20to%20USD%C2%A019.55%20billion%20by%202032%2C%20exhibiting%20a%20CAGR%20of%C2%A035.5%25%20during%20the%20forecast%20period”. Accessed September 2025.
- ExplodingTopics. “MLOps, https://explodingtopics.com/topic/mlops?period=60”. Accessed September 2025.
- Gartner. “Gartner Survey Reveals 80% of Executives Think Automation Can Be Applied to Any Business Decision, https://www.gartner.com/en/newsroom/press-releases/2022-08-22-gartner-survey-reveals-80-percent-of-executives-think-automation-can-be-applied-to-any-business-decision#:~:text=The%20Gartner%20survey%20revealed%20that%20on%20average%2C%2054%25%20of%20AI%20projects%20make%20it%20from%20pilot%20to%20production”. Accessed September 2025.
- Glassdoor. “MLOps Engineer Salaries, https://www.glassdoor.com/Salaries/mlops-engineer-salary-SRCH_KO0,14.htm”. Accessed September 2025.
