Learn MLOps with these 10 Courses

MLOps was born at the junction of DevOps, Data Engineering, and Machine Learning.

ML systems are experimental, have a more significant number of components, and are more challenging to construct and maintain.

If you have been into machine learning or deep learning, you might have encountered MLOps.

Like DevOps, Machine Learning Operations (MLOps) is the next significant move modern companies are taking.

Although it shares a similar concept with DevOps, it is implemented differently.

MLOps is expected to grow tremendously and reach $4 billion by the end of 2025.

With the growing demand for MLOps, more and more aspirants are looking forward to taking up MLOps courses to have better insights into these operations.

So, if you are one of them, let’s understand what MLOps is and then get into the list of the top 9 MLOps courses to help you learn and master it.

What Is MLOps?

MLOps acts as a medium where data scientists and operations specialists can work together and communicate using a set of techniques.

Implementing machine learning and deep learning models in expansive production environments can be automated to enhance quality and streamline the management process. In addition, MLOps makes it simpler to align models with business demands and regulatory standards.

You can even release high-performing ML apps continuously into production at scale, thanks to MLOps. There are specific requirements for ML to create a new lifecycle alongside the current SDLC and CI/CD processes, which results in a more effective workflow and better outcomes.

The different phases of MLOps are:

• Data analysis
• Data transformation/preparation
• Model training and development
• Model validation

To improve at MLOps, you need comprehensive knowledge, which becomes more accessible with the help of an MLOps specialization course.

Why Consider an MLOps Specialization Course?

Since data is the foundation of all programs, machine learning is distinct from traditional software. Data servicing, rather than application functionality, is the focus of the code’s design. Additionally, ML is an open-loop, dynamic system.

The task doesn’t start until the models are put into use. In addition, production models must be tracked, retrained, and redeployed in response to altering data signals to maintain optimum performance. Machine learning deployment demands skills more frequently in technical domains like software engineering and DevOps.

In a typical MLOps course, the fundamental ideas of machine learning are combined with the practical knowledge of contemporary software development and engineering roles to create machine learning engineering for production.

You will learn about the potential, difficulties, and effects of machine learning engineering in different areas of development and production. Upon completion of the course, you would be able to create cutting-edge AI solutions and tackle real-world issues. Other than this, you will be prepared to:

• Create a complete ML production system, including project planning, data requirements, modeling techniques, and deployment
• Create a baseline model, resolve idea drift, and construct a prototype.
• Learn to design data pipelines by acquiring, scrubbing, and validating datasets
• Utilize TensorFlow Extended to carry out feature engineering, transformation, and selection
• Use tools for data provenance and lineage metadata, create a data lifecycle and utilize business data schemas to track data evolution

Students pursuing an online MLOps course can also get MLOps ZoomCamp, wherein they can complete solutions in different files. It covers the introduction, deployment, practices, projects, etc.

Significance of MLOps in Career Prospects

There is a massive competition in data science, and every year, hundreds and thousands of students choose this field as it offers an excellent job scope.

Although MLOps is less well-known than data science, the pay scale is comparable. A data scientist in the US has a median base salary of $119,000, whereas MLOps engineers typically make around $90,529.

MLOps can support organizations of all shapes and sizes in developing effective plans, managing, and succeeding in the future. As a result, the significance of this technique is increasing as data and technology continue to surpass new heights.

So, as an MLOps engineer, you will be exposed to different tasks, such as installing machine learning models and ensuring they are operational in production. To put the model into use, you must be able to comprehend the machine learning algorithm that underlies it.

How Does an MLOps Specialization Course Work?

MLOps specialization courses can help learners acquire both practical and theoretical skills. Here’s how taking up a course works.

Enroll

You can directly enroll in an online MLOps specialization course after choosing the course you feel is right for you. Once you enroll in a course, you are a registered member. 

The best part is that you can finish just one course, permanently halt your studies, or cancel your subscription. You will also be provided with a dashboard to visualize and track all your enrollments in courses and advancement.

Do a Practical Project

Each specialization comes with a practical project that you must do to complete the MLOps specialization course and receive your authentic certificate. However, before beginning the hands-on project course, you must complete all the other courses to have knowledge you can apply to the project.

Get the Certificate

Upon completion of the course, you can get a certificate from the course provided, which you can show to potential employers and land your dream job.

Now, let’s look at some of the best MLOps courses you can take.

MLOps Fundamentals: CI/CD/CT Pipelines of ML: Udemy

This Udemy course will teach you the fundamentals of MLOps and covers how MLOps is resolving problems of traditional ML while enhancing the ML process’s adaptability and automation.

It covers:

• The standards and values that MLOps is built around
• Pipelines for continuous delivery, continuous training, and continuous integration in MLOps
• The MLOps course has a range of maturity levels
• Comparisons of the MLOps platforms and tool stack

This course by Udemy is for data scientists, ML engineers, DevOps engineers, and other professionals wanting to learn the concept.

Deployment of Machine Learning Models: Udemy

This machine learning deployment course by Udemy teaches you how to transition your machine learning models from the research stage to a fully integrated solution. Additionally, the course will explain model deployment and how it is helpful for your career.

You will learn each stage of building a model in the research environment, how to turn Jupyter notebooks into production code, code packages, publish it to an API, and add continuous delivery using entertaining video lessons.

The trainer will talk about the idea of reproducibility, its importance, and how to utilize versioning, code repositories, and Docker to maximize reproducibility during deployment. In addition, you will learn the process of deploying a machine learning model from a Jupyter notebook to cloud platforms and infrastructure while taking CI/CD into account.

This course is for:

• Data scientists who want to use their first machine learning model
• Data scientists who desire to become more proficient at model deployment.
• People working in software development but wanting to transition into machine learning

MLOps Fundamentals: Coursera

This Coursera course will learn about MLOps tools used for installing, assessing, operating, and monitoring production ML systems on Google Cloud in this course.

Moreover, it involves ML systems’ deployment, testing, monitoring, and automation. ML professionals can employ tools for model evaluation and continual development. They collaborate with data scientists who can create models to provide velocity in deploying the best-performing models.

What will you learn?

• Implementing the essential technologies for effective MLOps
• Adopt the most effective CI/CD procedures when dealing with ML systems
• Configure and set up Google Cloud environments for dependable and efficient MLOps
• Implement workflows for training and inference that are dependable and reproducible

MLOps Specialization: Coursera

The MLOps Coursera course covers fundamental machine learning ideas and the practical knowledge of contemporary software development and engineering roles to create machine learning engineering for production.

The course covers different areas, including conceptualizing, creating, and maintaining integrated systems constantly running in production. The course teaches how production systems must manage continually changing data, starkly to typical machine learning modeling.

What will you learn?

• Creating a complete ML production system, including project planning, data requirements, modeling techniques, and deployment
• Creating a baseline model, resolving idea drift, and constructing a prototype for a productionized machine learning application
• Learning to utilize TensorFlow Extended to carry out engineering and transformation
• Learning to use tools for data provenance and lineage metadata, creating a data lifecycle, and using business data schemas to track data evolution

MLOps Certification Training: CloudxLab

CloudxLab provides this MLOps Certification Training course. Generally, it refers to the methods, skills, and real-world expertise needed to develop your machine learning models, implement them, and keep track.

After training our machine learning model, the next major challenge is deploying and scaling it in production so that more users may use it. You will learn how to use the tools and approaches efficiently through the experimental learning process.

Understanding machine learning and profound learning ideas are crucial, but you must also have solid expertise with production engineering skills. In addition to developing experience in DevOps technologies, this course delves deeply into machine learning and deep learning methods. Upon completion of the course, students will learn to:

• Create a machine learning system from beginning to end.
• Learn about the project’s scope, data requirements, modeling, and deployment
• Create pipelines to enhance the model training procedure
• Learn to tackle your business difficulties with multiple machine learning and deep learning techniques
• Using Spark MLlib to fit for distributed model training
• Utilize CI/CD pipelines to deploy your machine learning models to the real world

Certified MLOps Course Training: 360 DigiTMG

This MLOps course covers deploying machine learning models at scale. The training results from our dissatisfaction while working on consulting projects and attempting to implement Machine Learning projects in real-world settings.

ML engineers face different challenges while working on a project, like “operationalizing” and “productionalizing” the code. For many engineers, it is pretty challenging to deploy ML models rapidly and effectively because there is no platform or set of recommendations like there are in other software engineering projects.

This course will teach you:

• The demand for MLOps in the data science field
• Learn about Docker and the benefits of containerization
• Learn about TensorFlow Extended (TFX) and its many parts

MLOps Engineering on AWS

This MLOps course by AWS will help you learn and apply DevOps-style procedures while training and deploying ML models with the aid of MLOps Engineering on AWS.

Additionally, you will learn how to operate in a team and leverage tools, automation, procedures, and teamwork to support the developers and operations staff, DevOps engineers, and ML data platform engineers responsible for operationalizing ML models.

You will discover how to create an MLOps action plan for your business once the course is over so that you can go from studying to doing.

Professional Certificate in MLOps with AWS: EdX

This EdX Cource is another good option for ML enthusiasts. You will learn to integrate data engineering and data science abilities to deploy machine learning models through the Machine Learning Operations with Amazon Web Services course.

Most of the labor in implementing AI models does not involve model development. The curriculum will introduce you to collaborating with data scientists and implementing machine learning models that can learn from data and produce predictions. This procedure is generally automated; thus, MLOps and AI engineering expertise are required.

To add more, the course will help you learn how to construct the pipeline to watch over several operation points, such as the incoming data and the decision outputs.

MLOps Course Certification: igmGuru

This MLOps Cource by igmGuru aims to instruct participants in creating machine learning models for mass production.

Individuals receive improved training in using various tools and production techniques for machine learning. The course provides the required exposure to MLOps fundamentals you need to develop subject competence, which is vital for guaranteeing a successful career in AI.

MLOps Fundamentals: PluralSight

This Cource by PluralSight focuses on providing in-depth knowledge of standard machine learning practices for deploying, evaluating, monitoring, and operating ML-integrated systems.

Various machine learning operation tools will be introduced to the students during the course. Besides understanding the uses of these tools, trainees will further learn standard approaches to using these tools. The course will help professionals integrate an ML system with a business process.

Nevertheless, the trainees will further learn to test and monitor the ML system to improve its performance and eliminate technical glitches. PluralSight offers a free trial version of this online course. If you like the free version and want to continue with the course, purchase the premium version. The free trial is for ten days and includes only the introductory section of the course.

Conclusion

Machine Learning (ML) is a cutting-edge technology that makes machines smarter and self-sustaining decision-making. In the past, machines used to perform according to human inputs. But an ML-integrated system can perform independently with no human intervention and keeps improving its performance with time by analyzing data and learning.

So, if you want to get into the field of data science, learning ML Operations (MLOps) is essential. Through an MLOps course, you can learn the fundamentals of ML operations and clear your doubts and misconceptions. It will also help you land your dream job and earn decently.

Thus, choose a good MLOps course from the above list, enroll in it, and start learning.