How To Learn MLOps -The Ultimate Guide For Beginners

September 28th, 2023

You must have already heard about DevOps. MLOps was born around similar concepts but has a different execution than DevOps. And since many people are still confused about what exactly MLOps is, we are here to make things a bit simpler for you.

One of the biggest questions aspirants ask revolves around how to learn MLOps. This blog is the ultimate guide for beginners who want to know what the MLOps platform is, how to learn MLOps or how to become MLOps engineer? Additionally, you will also get to know more about the best MLOps course.

What is MLOps?

Machine Learning Operations, more popularly known as MLOps is a set of practices incorporated in an organization to facilitate communication and collaboration between operations professionals and data scientists. With the MLOps best practices, organizations can simplify the management process, automate the deployment of Deep Learning and ML models in large-scale environments, and increase the quality.

Learn MLOps

The integral phases of MLOps are -

  • Data gathering
  • Data analysis
  • Data preparation/ transformation
  • Model training & development
  • Model validation
  • Model serving
  • Model monitoring
  • Model re-training

Who Should Learn MLOps?

We see a lot of beginners wondering who is fit to learn MLOps. And in the quest to find an answer, many think that only those with a good hold on DevOps practices should go for it. However, that is not the whole truth.

Yes, DevOps engineers are sure to benefit more as they hold experience in similar working environments. However, anyone without that experience can start too. Here are some professionals who can begin their career in MLOps -

  • Machine Learning Engineer
  • Data Scientist
  • DevOps Engineer

Once you follow a dedicated learning path and go for a trusted MLOps course, you can become an MLOps Engineer too. And the first step is to know about its best practices.

MLOps Best Practices

There are dozens of MLOps challenges but any organization can overcome them by adopting best practices. Here are some of MLOps' best practices -

1. Managing Data Pipeline

Managing data pipeline is extremely crucial since ML models are dependent upon data availability and quality. Organizations must establish data quality checks, data versioning systems and data governance policies to ensure data being used is reliable and consistent.

2. Containerizing Model Deployment

By containerizing the model deployment, the MLOps expert aids companies in ensuring that the ML models can run reliably and consistently throughout various environments. Various containerization technologies like Kubernetes and Docker are used.

3. Standardizing the Development process

When MLOps professionals standardize the development process of ML models, they aid companies in reducing the variability of models. Hence, ensuring their maintainability and reproducibility. This is done via CI/ CD pipelines, version control systems, and automated testing.

4. Monitoring Model performance

MLOps' success is heavily dependent on monitoring the model performance as ML models tend to degrade over time as infrastructure or data changes. Proper monitoring and alerting systems must be established.

5. Automating the Model training Process

This best practice aids companies in speeding up the iteration and experimentation of ML models. Meanwhile, they also reduce the burden on data scientists. It is achieved with the help of distributed training frameworks, hyperparameter tuning algorithms, and AutoML tools.

How to Learn MLOps?

These are some of the best resources you can use to kickstart your journey in MLOps.

1. Advanced Mathematics

You will have to gain a little bit more in-depth knowledge about probability, differential equations, linear algebra, and statistics.

2. Machine Learning & Deep Learning

The two supporting pillars of MLOps are Machine Learning and Deep Learning. Understanding their algorithms and how they help in this field is a must.

3. Big Data

When it comes to handling humongous datasets, using Big Data tools becomes necessary. AWS, Microsoft Azure, and GCP are the three most popular tools to use. The best way to gain knowledge about them is by learning and practicing.

4. Programming Language

Python is the primary language for ML and if you wish to make a career as an MLOps Engineer, you must ace this language.

5. DevOps

If you want to quickly get started with MLOps, then you must know DevOps inside-out. There are various tutorials, classes, and videos that you can use to get started.

6. MLOps Certification

There is nothing you can do to achieve the pinnacle of success if you do not have the right certification by your side, especially in the tech world. Thus, to find success and become a leading name, you should go with a certification to help you get closer to your dream job.

There is a good choice of certifications out there that will help you start your career here.

One of the options is to get certified as a cloud practitioner. You can choose any from Microsoft Azure, AWS, or GCP.

Another option is to go for Certified Machine Learning Operations certification.

Will Enrolling in MLOps Training Help?

There is nothing that can compare to the benefits of MLOps training. Individuals who enroll themselves in a leading institute to gain knowledge and skills about this field have better chances of passing the right certification exam.

Learning from industry experts, working on live projects to gain hands-on skills, and getting job assistance are perks that you can get only with an MLOps course.

Is An Online MLOps Course As Good As An Offline Course?

Yes! We are living in a digitally driven world and have come to the understanding that face-to-face classroom setting is not essential to gain complete knowledge. Online courses are as good as offline courses or even better.

You get the flexibility to learn at your own pace and time. And what more could one want with such a hectic lifestyle?


Now that you have a clearer idea about what MLOps is and how to start learning it, we know you can navigate further. Additionally, you can also get in touch with guidance counselors to help you better understand this field.

When it comes to enjoying a successful career as an MLOps engineer, it always comes down to getting a valuable MLOps certification. So, start your training course now and you will be all set to work in a top tech company soon.

Learn MLOps FAQs (Frequently asked questions)

Q1. Are there any MLOps communities I can join for better understanding?

Ans. Yes, you can join MLOps. Community on Slack and CDF Special Interest Group- MLOps for better understanding.

Q2. What educational background do I need to become an MLOps Engineer?

Ans. You must have a degree in any of these fields 

  • Data Science
  • Engineering
  • Mathematics
  • Computer Science
  • Computational Statistics

Q3. Do I need to learn scripting?

Ans. Yes, you will have to learn a scripting language to be able to automate processes as an MLOps professional. Python, Ruby, and Go are some of the most popular scripting languages you can choose from.

Q4. Is MLOps engineer a technical job?

Ans. Yes, being an MLOps engineer means you have a good grasp over technical aspects. You must have knowledge about application layers, managing servers, and scripting. You should also know how to work with ML models and work closely with data science teams.

Q5. How long will it take to learn MLOps?

Ans. You can easily learn MLOps in a few weeks if you have the right skills and knowledge about the essential topics. Otherwise, it might take you a couple of months.

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