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Navigating the World of MLOps Certifications

Explore the dynamic world of MLOps certifications: key career benefits, certification vs. certificate insights, and how to choose the right path for you.
Updated Apr 2024  · 10 min read

MLOps (Machine Learning Operations) has emerged as a critical discipline, bridging the gap between ML model development and operational deployment. This article aims to explore the promising career trajectory within MLOps, the distinction between certifications and certificates in this domain, and guide you through selecting the right educational path to enhance your skills in MLOps.

Why MLOps is a Great Career Path

The field of MLOps is not just burgeoning; it's exploding. The sector is predicted to grow at a compound annual growth rate of 39.3% between 2023 and 2030. As businesses increasingly rely on machine learning models to drive decision-making and innovation, the demand for skilled MLOps Engineers has skyrocketed. Here's why a career in MLOps is more relevant than ever:

  • High Demand: MLOps professionals are essential in bridging the gap between data scientists and operation teams, making them invaluable in today's tech landscape.
  • Competitive Salaries: Salaries for MLOps roles are among the highest in the tech industry, reflecting the demand and the specialized skill set required.
  • Diverse Opportunities: From startups to tech giants, companies across various industries are seeking MLOps talent.
  • Innovation at the Forefront: Working in MLOps puts you at the cutting edge of applying AI and machine learning in real-world scenarios.


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MLOps Certifications & Certificates

While the MLOps field is relatively new, the importance of formal education and credentials cannot be overstated. Understanding the landscape of certifications and certificates is crucial for anyone looking to advance their career in MLOps.

Certifications vs. Certificates

When navigating through the world of certifications, you will often see “certifications” and “certificates” being used interchangeably. However, there are clear differences, which are the following:

  • Certifications are typically awarded by recognized professional bodies and often involve passing rigorous exams. They are designed to validate one's expertise in a specific field.
  • Certificates, on the other hand, are usually completion acknowledgments for a course or series of courses focusing on specific skills or technologies.

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Image created with DALL-E 3

Top MLOps Certifications

While MLOps-specific certifications are still emerging, there are several machine learning and data science certifications that encompass critical MLOps skills. These certifications ensure a comprehensive understanding of machine learning lifecycle management, from model development to deployment and monitoring. You can learn more about them in our Guide to The Top Machine Learning Certifications For 2024. However, for the sake of brevity, they are outlined in the table below:


Target Audience




AWS Certified Machine Learning - Specialty

Individuals in a development or data science role with AWS ML/deep learning experience

Two years of hands-on AWS ML/deep learning experience, knowledge of basic ML algorithms

180 minutes exam


Google Cloud Certified - Machine Learning Engineer

Individuals maintaining ML models on Google Cloud

3 years of industry experience recommended, 1+ years with Google Cloud

Two-hour exam, 50-60 questions


Microsoft Certified: Azure Data Scientist Associate

Professionals applying data science and ML on Azure

Experience in Azure Machine Learning and MLflow

Includes an exam (duration not specified)


Databricks Certified Machine Learning Professional

Individuals performing advanced ML tasks using Databricks Machine Learning

Related training and hands-on experience with relevant ML tasks recommended

120 minutes exam

$200 USD

eCornell Machine Learning Certificate

Programmers, developers, data analysts, statisticians, data scientists, software engineers

Experience in math, Python, probability theory, statistics, multivariate calculus, and linear algebra recommended

Approximately 3.5 months, 6-9 hours per week

$3,750 USD

Top MLOps Certificates

For those looking to gain practical skills in MLOps, numerous online courses offer specialized certificates. At DataCamp, we provide a range of MLOps-related courses tailored to various skill levels.

MLOps Fundamentals Skill Track

  • What is it?: A series of courses covering the lifecycle of machine learning applications, from design to deployment and maintenance, emphasizing MLOps principles.
  • Duration: Approximately 14 hours.
  • Key skills learned: CI/CD, CM/CT, experiment tracking, model registries, feature stores, deployment strategies, handling data and model drift.
  • Instructors: Folkert Stijnman, Sinan Ozdemir, Nemanja Radojković, Arturo Opsetmoen Amador.
  • Cost: Included as part of DataCamp Premium, starting at $12.42/month
  • Certificate earned: Statement of Accomplishment upon completion.

MLOps Deployment and LifeCycling Course

  • What is it?: This course delves into the modern MLOps framework, focusing on the lifecycle and deployment of machine learning models, highlighting the importance of minimizing technical debt, and exploring tools for deployment and monitoring.
  • Duration: 4 hours.
  • Key skills learned: Writing ML code for minimal technical debt, using deployment and monitoring tools, understanding different environments and analytics.
  • Instructor: Nemanja Radojković, a Senior Data Scientist at Euroclear.
  • Cost: Included as part of DataCamp Premium, starting at $12.42/month
  • Certificate earned: Statement of Accomplishment upon completion.

MLOps For Business Course

  • What is it?: A course focusing on automating and scaling ML applications in business.
  • Duration: 3 hours.
  • Key skills learned: Understanding MLOps essentials, lifecycle from design to operations, and challenges in deploying ML models.
  • Instructor: Arne Warnke, Head of Emerging Curriculum at DataCamp.
  • Cost: Included as part of DataCamp Premium, starting at $12.42/month
  • Certificate earned: Statement of Accomplishment upon completion.

MLOps Concepts Course

  • What is it?: An intermediate-level course designed to introduce MLOps, focusing on scaling and automating machine learning projects.
  • Duration: Approximately 2 hours.
  • Key skills learned: Basics of MLOps, machine learning lifecycle, MLOps roles, design and development phase, deployment, and maintaining ML in production.
  • Instructor: Folkert Stijnman, a graduate data scientist with expertise in machine learning and data science, working freelance for a variety of companies.
  • Cost: Included as part of DataCamp Premium, starting at $12.42/month
  • Certificate earned: Statement of Accomplishment upon completion.

Introduction to MLFlow Course

  • What is it?: A course on using MLflow to manage the machine learning lifecycle, including tracking, reproducibility, and deployment of ML models.
  • Duration: 4 hours.
  • Key skills learned: MLflow Tracking, Projects, Models, Model Registry, model versioning, evaluation, customization, and automation.
  • Instructor: Weston Bassler, Senior MLOps Engineer.
  • Cost: Included as part of DataCamp Premium, starting at $12.42/month
  • Certificate earned: Statement of Accomplishment upon completion.

Choosing the Right Certification

Choosing the right certification can be daunting. That said, the choice of certification should align with your current skill level, career aspirations, and the specific focus areas you wish to master. Consider the following:

Skill Level & Career Goals

Assess where you are in your career and where you aim to be. Entry-level professionals might focus on foundational courses, while experienced practitioners may seek advanced certifications.

Prerequisite Knowledge

Some certifications require a baseline understanding of machine learning, programming, or data science. Ensure you meet these prerequisites before enrolling.

Certification’s Focus

Select a certification that best aligns with your career goals, whether it's more on the operational side, model development, cloud tools, or full-stack MLOps proficiency.

Getting MLOps Certified with DataCamp

DataCamp offers a range of courses and resources that can be instrumental in your journey toward gaining MLOps certification. Here’s how DataCamp can help you prepare:

Varied MLOps course content

DataCamp curates a comprehensive array of MLOps courses catering to varying expertise levels. Courses like MLOps Fundamentals lay the groundwork, while specialized offerings such as MLOps Deployment and LifeCycling delve into the practicalities and advanced techniques of MLOps.

Interactive learning approach

The platform advocates for an interactive, hands-on learning experience. This strategy proves especially effective for mastering intricate MLOps concepts and their application in tangible situations. Courses such as MLOps for Business enable learners to hone their skills through engaging exercises and projects.

Rich supplementary resources

In addition to its courses, DataCamp presents an extensive library of tutorials, webinars, and blog posts. These resources offer deep dives into various MLOps topics and the latest trends within the field, enriching learners' understanding well beyond the structured course material. For example, guides like Machine Learning Piplelines, Deployment and MLOps, A Practical Guide to MLOps, and Getting Started with MLOps broaden your knowledge base.

Project-centric learning

Applying theoretical knowledge to real-world projects is essential for grasping the applied aspects of MLOps. DataCamp encourages this through project-based learning opportunities, allowing you to tackle real datasets and address genuine MLOps challenges. This practical exposure is crucial for translating learning into action.

Leveraging DataCamp's exhaustive course repository, engaging learning modes, and broad-ranging resources equips you with the skills and assurance to pursue diverse MLOps certifications and excel in the MLOps arena.

Role-Specific Certification with DataCamp

DataCamp delivers specialized certifications tailored to the unique roles within the data profession, ensuring you can validate your expertise and bolster your position in your chosen career path. DataCamp offers these certifications to Premium subscribers at no extra cost.

Data Analyst Certification

  • Intended Audience: This certification is perfect for professionals keen on mastering data analysis through Python, R, SQL, and Tableau.
  • Core Learning Domains: It encompasses critical areas such as data manipulation, visualization, and statistical analysis, arming participants with the capability to extract valuable insights from data.
  • Program Composition: Featuring a mix of instructional courses, practical exercises, and project work, this certification guarantees a thorough grasp of data analysis principles.

Data Scientist Certification

  • Intended Audience: Aimed at aspiring data scientists, this certification focuses on essential skills such as statistical analysis, machine learning, and data wrangling.
  • Core Learning Domains: The curriculum spans a broad spectrum, from exploratory data analysis, machine learning, and deep learning, to specialized areas like natural language processing (NLP).
  • Program Composition: It offers a deep dive into data science practices and tools, complemented by hands-on project experience to cement the learning.

Data Engineer Certification

  • Intended Audience: Geared towards individuals passionate about data engineering, this certification concentrates on developing scalable data infrastructures and pipelines.
  • Core Learning Domains: Participants will explore big data processing, utilize data engineering tools (such as Spark), and learn about database management.
  • Program Composition: The certification merges theoretical insights with practical applications, equipping learners to construct effective data storage and processing systems.

Through these certifications, DataCamp not only verifies your skills but also significantly enhances your professional credibility, offering a structured pathway to mastering your role-specific capabilities in the data field.

Get certified in your dream data role

Our certification programs help you stand out and prove your skills are job-ready to potential employers.

Get Your Certification
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Final Thoughts

Embarking on a career in MLOps is a promising journey that combines the excitement of cutting-edge technology with the stability and demand of a critical operational role. With the right mix of certifications and practical experience, you can position yourself as a highly sought-after professional in this dynamic field. Whether you're just starting out or looking to deepen your expertise, the path to becoming a certified MLOps professional is rich with opportunities for growth and advancement.

Get started on your MLOps journey today with our MLOps Fundamentals skill track, which covers the complete life-cycle of a machine learning application.


What prerequisites should I have before pursuing an MLOps certification?

It depends on the MLOps certification you're pursuing. For any certification, you should have a basic understanding of machine learning concepts, familiarity with data science workflows, and some experience with programming, preferably in Python. Knowledge and experience in specific cloud platforms, such as Google Cloud Platform, is required for each platform-specific certification.

How can MLOps certifications enhance my career in machine learning?

MLOps certifications validate your skills in managing and deploying machine learning models efficiently, making you a valuable asset to operational and development teams. They demonstrate your ability to handle real-world machine learning applications, enhancing your employability and potential for career advancement in AI-driven organizations.

What is the difference between an MLOps certification and a machine learning certification?

An MLOps certification focuses on the operational aspects of deploying, monitoring, and maintaining machine learning models efficiently in production environments, whereas a machine learning certification typically concentrates on developing, training, and evaluating models.

Can I pursue MLOps certifications if I have a background in software engineering but not in machine learning?

Yes, software engineers can pursue MLOps certifications to bridge their technical skills into the machine learning domain, focusing on the deployment, scalability, and maintenance of machine learning models.

What skills are required to excel in MLOps?

To excel in MLOps, one needs a blend of skills in machine learning, software engineering, and data engineering. Proficiency in programming (especially Python), understanding of ML algorithms, familiarity with deployment tools (like Docker and Kubernetes), and knowledge of cloud platforms are essential.

How can a company start integrating MLOps into its operations?

A company can start integrating MLOps by first assessing its current machine learning capabilities and infrastructure, then gradually implementing MLOps practices, such as automated data validation, model monitoring, and CI/CD pipelines for machine learning models, starting with small, manageable projects.

What other certifications should I consider?

There are many relevant certifications covering different technologies and career paths. Check out some of our other guides for an overview:


Start Your MLOps Learning Journey Today!


MLOps Fundamentals

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Dive into the foundations of Machine Learning Operations (MLOps), learning the concepts of productionizing and monitoring machine learning models!
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