MLOps Tutorial- A Complete Guide For Beginners

MLOps or Machine Learning Operations is a multidisciplinary field that amalgamates software development lifecycle (SDLC) with machine learning (ML). This overlap between IT operations and ML facilitates the performance of ML models in the production environment for making necessary changes and detecting issues. This MLOps tutorial will give you a complete guide on the path to learn MLOps, what is MLOps? and how to implement it? Additionally, we have also covered how to become an MLOps engineer and what they do.

Let's begin!

What is MLOps (Machine Learning Operations)?

MLOps refers to a set of practices to efficiently and reliably deploy and maintain ML models in the production environment. Once an algorithm is all set to launch, DevOps engineers, ML engineers, and Data Scientists practice MLOps for transitioning this algorithm to production systems.

MLOps began as only a set of best practices. Today, however, it has evolved into a completely independent approach to the process of machine learning lifecycle management.

What Is The Need For MLOps?

MLOps has become an integral part of many organizations. This is because of the kind of results it sprouts for those who implement it. There are quite a few reasons as to what is the need for MLOps. Some of the top reasons are-

• MLOps provides a solid framework to effectively manage the ML lifecycle, leading to a more iterative and structured workflow.
• Bottlenecks are common when the algorithm is non-intuitive and complicated. With MLOps, collaboration between data and operations teams is fluent, leading to reduced severity and frequency of these issues.
• MLOps ensures that it stays in compliance and updated with the ever-changing regulations.

What Does An MLOps Engineer Do?

An MLOps engineer is a highly skilled professional tasked with the responsibility of streamlining the workflow of the ML platform. This goes from design to development and production, and even further. An MLOps engineer works closely with operations staff, business leaders, data scientists, and developers associated with the ML product.

Here is a list of some of the responsibilities you will have to overtake for an MLOps career-

• Deploy & operationalize MLOps, especially implementation
• Model evaluation & explainability
• Model workflows from onboarding - operations to decommissioning
• Version management & data archival
• Provide best practices
• Model hyperparameter optimization
• Model governance & version tracking
• Execute POC for efficient & automated model operations at scale
• Model training & automated retraining
• Model & drift monitoring
• Design & develop scalable MLOps frameworks
• Create & use metrics, monitoring & benchmarks
• Provide technical design solutions

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MLOps Tutorial- Your Path To Learn MLOps

MLOps learning path is not an easy one. Or rather, it is not easy to decode it. As an MLOps engineer, you would need to have strong knowledge in the leading skill suite, along with extensive working experience in a few the below-mentioned fields:

• Knowledge of programming languages, especially Python. Working experience of foundational languages like Java and C++, along with other widely chosen data science languages like R.
• Practice of collaborating with plenty of multidisciplinary teams as well as project stakeholders. 
• Familiarity with software development methodologies and approaches like CI/CD, DevOps, and Agile.
• Software troubleshooting and testing using Agile toolchains.

Now, it is important to understand these skills are highly akin to that essential for a DevOps job. However, to ensure an MLOps career, you must gain a strong understanding of Machine Learning models and algorithms.

An MLOps engineer must possess a solid skill set that includes but is not limited to statistical modeling, IT operation and infrastructure, database administration and construction, scripting and programming, and data science.

How To Become An MLOps Engineer?

Anyone who wishes to become an MLOps Engineer must be prepared to gain a hefty amount of skills and experience. Even if you have a good amount of experience, an MLOps course is sure to help you better understand and streamline your career ahead.

This MLOps tutorial covers the key skills and tools to know about, and learn to explore the ability to work as an MLOps engineer. However, it is to be noted that every organization and industry is different. Hence, what skills you have might or might not be fit for a certain organization or industry.

Here are some commonly accepted norms for an MLOps engineer-

• A bachelor's/ master's/ PhD in an analytical discipline such as Computer Science, Operations Research, Economics, Mathematics, Statistics, etc.
• Deep programming/ quantitative educational background.
• 3-6 years of working experience in end-to-end management of ML projects. 1-2 years of working experience centered on the MLOps field.
• Working experience with MLOps tools like Data Version Control (DVC), ModelDB, Pachyderm, and Kubeflow.
• Knowledge of ML frameworks like PyTorch, Scikit-Learn, TensorFlow, Keras, etc.
• Familiar with monitoring build & production systems by employing automated alarm and monitoring tools.
• Familiarity in supporting model deployment and model builds for autoML tools and IDE-oriented models, model training & version tracking (Kuberflow, Dataiku, MLflow), model evaluation, experiment tracking, explainability (TensorBoard, SHAP), model hyperparameter optimization, and model management.

Conclusion

The future of MLOps engineering seems pretty bright as all the technological advancements are in favor of these professionals. MLOps engineers are predicted to work more closely with data engineers in the coming years to address all bias and data quality issues. We hope this MLOps tutorial has helped you enough to understand the basics of MLOps. For deeper information, you will need guidance from experts.

MLOps Tutorial FAQs (Frequently Asked Questions)

Que 1. Does MLOps require coding?

Yes, MLOps generally does require coding knowledge to developer codes that can run in production environments. Hence, as an MLOps engineer, you must learn to code and gain basic working experience.

Que 2. What is the best language for MLOps?

You need high proficiency in programming languages and the best language for MLOps is undoubtedly Python.

Que 3. What is the salary of DevOps engineer vs MLOps?

The average salary of an MLOps engineer in the US is $87,220 per annum. On the other hand, the average salary of a DevOps engineer in the US is 133,115 per annum.

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