Become an ML Engineer with these courses from Amazon and Google

With ML Engineer job roles in all the vogue and a lot of people preparing for them, I get asked a lot of times by my readers to recommend courses for the ML engineer roles particularly and not for the Data Science roles.

Now, while both ML and Data Science pretty much have a high degree of overlap and I could very well argue that ML engineers do need to know many of the Data Science skills , there is a special place in hell reserved for ML engineers and that is the production and deployment part of the Data Science modeling process.

So, it doesn’t hurt to look at what two of the world’s biggest companies are looking at when it comes to ML engineering. These courses taught by Google and Amazon are rather popular and typically beginner level. So these are the best bet for people who are looking to start their journey to become an ML Engineer.

My main criteria for selecting these particular courses is the practical utility they provide as well as the pedigree they bring. Also, note that you don’t need to take these courses in any order or even take all of them. Just focus on one or two of them based on your particular requirements and you should be fine.

Google

1. Google IT Automation with Python Professional Certificate

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Have you ever been asked to implement an API for your machine learning model, write some bash scripts to run a CRON job or some auto-scheduler and felt lost? This beginner-level professional certificate looks at Python from an engineering and production point of view rather than just Data Science. Something that we Data Scientists learn only after a few years of experience in the field and what is essentially a day to day thing for an MLE role.

The professional certificate from Google essentially talks about Python, Regex, Bash Scripting, automation testing, Github, debugging, Scaling up, and Programming Interfaces(APIs).

Most of these might not look so beginner-friendly right now, but they are some super cool skills to have in your portfolio and actually not that hard once you start understanding about the whole coding ecosystem.

2. Cloud Engineering with Google Cloud Professional Certificate

A lot of companies have started using Google Cloud Platform nowadays. If yours is such a company, this particular professional certificate might provide a lot of value. This specialization particularly focusses on the GCP platform and its various services like Google App Engine, Google Compute Engine, Google Kubernetes Engine, Google Cloud Storage, and BigQuery. You will also learn about other engineering concepts like load balancing, autoscaling, infrastructure automation, and managed services.

All of these services are pretty much becoming a standard for cloud computing at a lot of companies and it helps to learn about those if you are using the GCP infrastructure for building and deploying your models.

Amazon:

1. AWS Fundamentals

For people who aim to work at Amazon or the companies that use Amazon Web Services (AWS), this specialization teaches the AWS fundamentals and provides an overview of the features, benefits, and capabilities of AWS.

The main services you learn in this specialization are AWS Lambda(serverless compute), Amazon API Gateway(create, publish, maintain, monitor, and secure APIs at any scale), Amazon DynamoDB(Fast, flexible and scalable NoSQL database service), and Amazon Lex(Conversational AI for Chatbots), along with taking your application to the cloud.

This particular Amazon specialization is engineering heavy and by the end, you would understand how to build and deploy serverless applications with AWS.

2. Getting Started with AWS Machine Learning

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There are a lot of things to consider while building a great machine learning system. We, as data scientists, only worry about the data and modeling part of the project but do we ever think about how we will deploy our models once we have them?

I have seen a lot of ML projects, and a lot of them are doomed to fail as they don’t have a set plan for production from the onset. Having a good platform and understanding how that platform deploys machine Learning apps will make all the difference in the real world. This course on AWS for implementing Machine Learning applications promises just that.

This course will teach you:

1. How to build, train and deploy a model using Amazon SageMaker with built-in algorithms and Jupyter Notebook instance.
   
2. How to build intelligent applications using Amazon AI services like Amazon Comprehend, Amazon Rekognition, Amazon Translate and others.
   
   Source

3. AWS Computer Vision: Getting Started with GluonCV

This course also provides an overview of Machine Learning with Amazon Web Services but with a specific emphasis on Computer Vision applications.

In this course, you will learn how to build and train a CV model using the Apache MXNet and GluonCV toolkit. The instructors start by discussing artificial neural networks and other deep learning concepts and then walk through how to combine neural network building blocks into complete computer vision models and train them efficiently.

This course covers AWS services and frameworks including Amazon Rekognition, Amazon SageMaker, Amazon SageMaker GroundTruth, Amazon SageMaker Neo, AWS Deep Learning AMIs via Amazon EC2, AWS Deep Learning Containers, and Apache MXNet on AWS.

This course will provide a lot of value to learners who want to train a deep learning vision model from scratch and deploy it efficiently using the AWS computing platform.

Conclusion

In this post, I talked about the best courses I would recommend for people who are trying to get into ML engineering in this not so much return to school session.

Also, here are my course recommendations to become a Data Scientist in 2020.

I am going to be writing more beginner-friendly posts in the future too. Follow me up at Medium or Subscribe to my blog .

Also, a small disclaimer — There might be some affiliate links in this post to relevant resources, as sharing knowledge is never a bad idea.