MLOps professionals are in high demand due to the complexity of ML projects. This career path offers a unique blend of technical and operational skills, making it an attractive option for those looking to stay up-to-date with the latest advancements in ML and software engineering.
MLOps engineers manage the entire ML journey, from data collection to model training, deployment, and monitoring. This requires a deep understanding of both ML algorithms and software engineering, making it an attractive career option for those looking to stay competitive.
To gain practical experience in MLOps, taking courses and working on projects that focus on the practical aspects of the field is recommended. It is essential to select the right Machine Learning education to pursue a career in MLOps engineering. To help guide your search, we have evaluated five good MLOps courses from world-leading organizations in the domain.
5 MLOps Certification Programs to Build Specialty Skills
These courses provide a hands-on experience in MLOps workflows, helping you take the first step towards mastering the fundamentals.
Machine Learning Engineering for Production (MLOps) Specialization
Gain a comprehensive understanding of how to build and deploy ML models in production environments. The Machine Learning Engineering for Production (MLOps) Specialization by deeplearning.ai is designed to help experienced data practitioners refine their abilities.
This MLOps Specialization training provides a comprehensive overview of how to create, deploy, and maintain integrated systems that run continuously in production. It covers how to manage changing data and optimize performance while minimizing costs. You'll learn to use established tools and techniques to do this efficiently. This will give you insight into the possibilities, challenges, and implications of machine learning engineering in production.
Is it right for you?
This specialization is ideal for those with some knowledge of AI/deep learning, intermediate skills in Python, and experience with any deep learning framework (PyTorch, Keras, or TensorFlow).
This program will help you advance your ML career prospects and equip you with production-ready skills taught by AI pioneers Andrew Ng and Laurence Moroney, enabling you to contribute to the development of cutting-edge AI technology to address real-world issues.
Machine Learning Engineering for Production (MLOps) Specialization
MLOps (Machine Learning Operations) Fundamentals
This MLOps Fundamentals training course is perfect for anyone, regardless of their cloud computing background. It provides a comprehensive overview of the tools and best practices for deploying, evaluating, monitoring, and managing ML systems in production on Google Cloud.
You'll gain the practical skills and experience you need to become a successful Machine Learning Engineer, including how to use Kubernetes for ML and Kubeflow Pipelines on AI Platform, as well as CI/CD for Kubeflow Pipelines on AI Platform.
Is it right for you?
This introductory course is ideal for those who are just starting out with the basics of data science workflows and principles and are looking to gain an understanding of MLOps.
By the end of this course, you'll be equipped with advanced skills to start your career with a solid foundation and be able to confidently handle the complexities of deploying and managing ML models in production, constructing and managing ML pipelines, dealing with and storing data, implementing monitoring, and scaling.
MLOps (Machine Learning Operations) Fundamentals
Practical Data Science on the AWS Cloud Specialization
Are you ready to take your data science skills to the next exciting level? The Practical Data Science on the AWS Cloud Specialization is the perfect way to bridge the gap between development and production environments. It equips you with specialized skills to architect and implement data science projects for scale and operational efficiency.
This specialization combines domain knowledge with mathematics, statistics, data visualization, and programming skills to help you develop practical skills to effectively deploy your data science projects and overcome challenges at each step of the ML workflow using Amazon SageMaker.
Is it right for you?
This advanced-level program is tailored for data scientists and analysts who are already familiar with Python and SQL and are looking to learn how to build, train, and deploy scalable, end-to-end ML pipelines - both automated and with human input - in the AWS cloud platform.
Upon completion of this program, you will be able to confidently apply your ML skills in real-world scenarios and be well on your way to a successful career in data science. It offers a practical approach to mastering the latest algorithms for natural language processing (NLP) and natural language understanding (NLU), such as BERT and FastText, through Amazon SageMaker.
Practical Data Science on the AWS Cloud Specialization
MLOps Concepts
The MLOps Concepts course is perfect for data scientists and ML engineers who want to learn the basics of MLOps and how to move their machine learning models from a local notebook to a functioning model in production.
This course provides a comprehensive overview of MLOps, from the basics to the more advanced stages. You'll learn about the roles and features of MLOps, as well as the different phases of the machine learning lifecycle. You'll also explore systems and tools to scale and automate MLOps, such as feature stores, experiment tracking, CI/CD pipelines, microservices, and containerization.
Is it right for you?
This course is ideal for those with a basic knowledge of machine learning and data engineering, as well as business leaders looking to gain insight into MLOps processes.
By the end of this course, you will have a fundamental knowledge of how to use MLOps principles to deploy machine learning in a dependable, efficient, and consistent manner.
MLOps Concepts
Machine Learning DevOps Engineer - Nanodegree
Udacity's Machine Learning DevOps Engineer program is the perfect way to learn the DevOps skills needed to automate the various stages of machine learning model building and monitoring.
This Nanodegree program is ideal for those who prefer a more supported learning style. With technical mentorship, you will learn how to effectively integrate machine learning models and deploy them to a production-level environment. You will acquire the advanced skills needed to become a successful MLOps or ML DevOps engineer.
Is it right for you?
It is recommended that you have prior experience with Python and machine learning, as well as an understanding of the data science process and the workflow of building machine learning models, in order to maximize the benefits of this program.
This comprehensive program will provide you with the technical skills you need to reach new heights in your career. You will gain proficiency in automating machine learning models and deploying them into production environments, making you an invaluable asset for MLOps job roles.
Machine Learning DevOps Engineer Nanodegree
FAQs — Learning MLOps
Do you have any questions? We are here to help provide more information.
Is MLOps a good career choice?
MLOps professionals are highly sought after, as they oversee the entire machine learning process, from data acquisition and pre-processing to model training and deployment. MLOps professionals can expect remuneration that is on par with the AI job market, making it an attractive option for anyone looking for a career.
How difficult is it to become an MLOps engineer?
It depends on your experience and background. If you have a strong background in software engineering, data engineering, and machine learning, then it should be relatively easy to become an MLOps engineer. However, if you don't have any of these skills, then it may take some time to learn the skills and gain the experience needed to become an MLOps engineer.
Do you need a degree for Learning MLOps?
A degree in computer science, mathematics, or statistics can be helpful for MLOps engineering job roles, but it is not a necessity. Many successful professionals in the field have achieved success without a degree, and employers tend to prioritize a candidate's experience, open source contributions, and technical skills over their educational background.
What qualifications are needed to enroll in a MLOps course?
To learn MLOps, you should have a basic understanding of machine learning concepts and technologies, be comfortable with scripting languages such as Python or R, have experience with version control systems such as Git or Subversion, and be familiar with tools such as Docker containers and Kubernetes.
Closing Note
Comprehensive MLOps training programs can help you gain a deep understanding of the MLOps lifecycle. With these specialization courses, you can learn to make informed decisions and maximize the potential of your MLOps initiatives. Pursuing a career in MLOps is an excellent choice in this era of AI-driven technology.