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So the next edition of the study roadmap is MLOps - a combination of machine learning, DevOps, and Data Engineering. The aim is to deploy and maintain machine learning systems in a reliable and efficient way. So how does one become an MLOps engineer? 

 

1. The Foundations

 

If MLOps is a combination of machine learning, DevOps, and Data Engineering - you can imagine that the foundations of MLOps are the foundations of these sub-sectors too. 

So what are the foundations?

 

Python

 

If you chose Python as your programming language, here are some recommended courses:

• 100 Days of Code: The Complete Python Pro Bootcamp for 2022 - Udemy
• Programming for Everybody (Getting Started with Python) - Coursera (University of Michigan)

A scripting language is highly advised as an MLOps Engineer as you will need to automate processes at a high level. Python, Go, and Ruby are examples of popular scripting languages that you can choose. 

 

SQL:

 

• The Ultimate MySQL Bootcamp: Go from SQL Beginner to Expert - Udemy
• Complete SQL Mastery - CodeWithMosh

 

Mathematics:

 

• Mathematics for Machine Learning - Book
• Linear Algebra by Khan Academy - YouTube
• ??Statistics and Probability by Khan Academy - YouTube
• Mathematical Foundations of Machine Learning - Udemy

 

2. Machine Learning Algorithms and Libraries

 

As an MLOps engineer, your day-to-day tasks will revolve around Machine Learning algorithms, therefore it is important for you to understand the models you are working with in-depth. You will also need to know the libraries and frameworks to succeed in your role. 

 

Machine Learning Algorithm resources:

 

• Understanding Machine Learning: From Theory to Algorithms By Shai Shalev-Shwartz, ?Shai Ben-David - Book
• Machine Learning Algorithms Explained in Less Than 1 Minute Each - Blog
• Popular Machine Learning Algorithms - Blog
• Machine Learning Algorithms by Simplilearn - YouTube

 

Machine Learning libraries resources:

 

• Pandas - Managing tabular data
• NumPy - Scientific computation
• Matplotlib - Data visualization
• Scikit-Learn - Data preprocessing and modeling
• SciPy - Scientific computation
• NLTK - Text processing
• TensorFlow - Deep Learning
• Keras - Deep Learning
• PyTorch - Deep Learning

There are more libraries out there, but these are the most popular ones which you will typically be working with. 

 

3. Databases

 

Taking the aspect of a Data Engineers role, Databases and their management systems are an important element to an MLOps Engineers roles and responsibilities. In order for you to maintain the machine learning systems in a reliable and efficient way, you will need databases to help you with that.

Here are some resources:

• Principles of Database Management - YouTube
• Free SQL and Database Course - Blog
• The Ultimate MySQL Bootcamp: Go from SQL Beginner to Expert - Udemy
• Complete SQL and Databases Bootcamp: Zero to Mastery - Udemy

 

4. Model Deployment

 

As an MLOps Engineer, you will need to learn how to deploy your models. Large companies typically use cloud platforms to host their applications, such as AWS, GCP, and Microsoft Azure. So it is highly likely that you will also be doing the same, therefore I would highly recommend that you have a good understanding of each of these, as you will most certainly be using it as an MLOps Engineer. 

Here are some resources to help you:

• Cloud Computing Tutorials and Resources
• Ultimate AWS Certified Cloud Practitioner 2022 - Udemy
• GCP Associate Cloud Engineer Google Cloud Certification - Udemy
• Microsoft Azure: From Zero to Hero - Udemy

 

5. Experiment Tracking

 

For some professionals who work with data, their end goal is to achieve model deployment. However, as an MLOps Engineer, experiment tracking is vital. Experiment tracking allows us to manage all the experiments along with their components, such as parameters, metrics, and more. This makes it easier for us to organize the component of each experiment, reproduce past results and log everything. 

As an MLOps engineer, you should know about the different tools you can use to track your experiments. I will list the most popular ones:

• MLFlow
• Comet ML 
• Neptune
• Weights and Biases
• TensorBoard

 

6. Metadata Management

 

Metadata is data about data, and the management of this type of data can help you gain a better understanding, group, and sort the data for other uses. Producing metadata from a model can be used to train parameters, evaluate metrics, test pipeline outputs, and more. 

Poor metadata management during the workflow lifecycle can lead to conflicting information, a lack of trust in the data, and an increase in cost.

Here are some resources to help better understand:

• Data Management - Metadata Management - YouTube
• Data Management Masterclass - Udemy
• Prepare Data for Exploration - Coursera

 

7. Data and Pipeline Versioning

 

Data versioning is the storage of different versions of data that have been created over time. There are different reasons why the data changes over time, such as data scientists testing to see if they can increase the efficiency of an ML model or the flow of information. The advantage and need for data versioning help from a business perspective by enabling consumers to be aware if a newer version of the dataset is available.

Below is a list of popular tools used for data versioning:

• DAGsHub
• DVC
• Pachyderm
• lakeFS

 

8. Model monitoring

 

The model monitoring stage comes after model deployment and is the process of exactly what it says - monitoring the model. You want to be looking out for model degradation, data drift, and others to ensure your model is at a good performance level. 

Here are some resources to help you:

• Continuous monitoring - MLOps guide
• Testing and Monitoring Machine Learning Model Deployments - Udemy
• A Machine Learning Model Monitoring Checklist: 7 Things to Track - Blog

• IBM Watson OpenScale - Tool

 

9. Projects

 

You should have a good understanding and in-depth knowledge of the skills required to be part of the MLOps profession. Once you have those skills under your belt, the next stage is to put them to the test through projects - which can then be later used as part of your portfolio.

Here are some project ideas:

• Made With ML - All aspects of MLOps
• Automating the Archetypal Machine Learning Workflow and Model Deployment
• Social Power NBA
• MLOPS End To End Implementation
• Experiment Tracking Using MLflow

Practicing your skills and perfecting them is the main aim here!

 

10. Interview

 

Now we’re ready to smash an interview. When preparing for an interview, the aim is to prepare, prepare, and then relax! When it comes to tech roles, there can be a lot to remember, and sometimes nerves cause you to forget everything. So I always recommend people to keep calm and enjoy this stage - enjoy all the work you’ve put in and prove how solving these challenges is lightwork!

Here are some resources to help you:

• Top 30 MLOps Interview Questions - Blog
• Nail Your Next Machine Learning Interview - Webinar (FREE)
• MLOps Community Session Playlist - YouTube
• 20 MLOps Interview Questions and Answers
• Machine Learning Interview Preparation - Udacity (FREE)
• Grokking the Machine Learning Interview - Educative

 

Wrapping it up

 

As MLOps consists of machine learning, DevOps, and IT - there are so many resources out there to help you become the most successful MLOps Engineer you can be. Check out the other editions of this article, to help you out:

1. The Complete Data Science Study Roadmap
2. The Complete Machine Learning Study Roadmap
3. The Complete Data Engineering Study Roadmap

 
 
Nisha Arya is a Data Scientist and Freelance Technical Writer. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.