ABOUT THIS MACHINE LEARNING ENGINEER NANODEGREE

The Machine Learning Engineer for Microsoft Azure course will prepare you to take on Machine Learning roles by strengthening your skills in ML and providing hands-on experience. You will learn how to validate and evaluate Machine Learning models with Microsoft Azure.

At the beginning of the course, you will focus on the basics of Machine Learning on Azure with a low-code experience. When you complete this part, you will move on to applying more advanced techniques like ensemble learning and deep learning. You will gain practical experience running sophisticated Machine Learning tasks using the built-in Azure labs. Your final project will be to use Azure’s Automated Machine Learning and HyperDrive to solve a task. Once you solve it, you will deploy your model as a web service and test the model endpoint.

This Machine Learning Engineer nanodegree takes around 3 months to complete. Some experience with ML, statistics, and Python is required to enroll.

Why Become an ML Engineer?

We need both AI and machine learning to power robots. By using ML, we may create programs that are flexible enough to be readily updated and adapted to meet the demands of ever-changing settings and tasks.

If you’re considering a job in machine learning, consider these benefits:

ML is a skill of tomorrow. 

You’ll have a stable career in a discipline that is on the rise if you can fulfill the needs of huge corporations by becoming an expert in machine learning.

In this digital era, businesses confront a number of obstacles that ML offers to address. Real-world problems and their answers await you as an ML engineer. Your work will have a significant influence on the way organizations and individuals live and work. It goes without saying that having a profession that enables you to work on real-world problems and find solutions is very fulfilling.

ML is in high demand.

Although machine learning has grown rapidly, there remains a scarcity of skilled workers.

Because machine learning (ML) is in such high demand, getting in early will allow you to see trends firsthand and enhance your marketability to your company.

There is still more work to be done before machine learning is considered a mature technology. With time and experience, you will be able to pursue an upward career path and approach the companies you want to work for.

Many people consider machine learning as an opportunity to make a lot of money, and the average compensation for an ML engineer ranks high on our list because of this. This number is likely to climb in the next years due to the industry’s growth.

Open new challenges in your professional life.

Make a foray into data science Machine learning abilities may help you open new doors in your professional life. You can wear two hats if you have ML skills: one is that of a data scientist, and the other is that of a modeler. Acquire skills in several professions concurrently and begin on an exciting path packed with challenges, chances, and information. 

Currently, machine learning is taking place. So, you want to get a leg up on the competition by experimenting with solutions and technology that enable it in the early stages. A profession that is constantly on the rise will be a lot easier to come by if your abilities are in great demand when the time comes.

What Does a Machine Learning Career Path Look Like?

An entry-level position in the field of machine learning is that of a Machine Learning engineer. Engineers specializing in machine learning create software and solutions that eliminate the need for people to do routine activities. Machines can effectively and reliably complete the majority of these repetitive jobs based on condition and response combinations.

As an ML engineer, you may advance to the position of ML Architect. As a prototype developer, you’ll be creating and designing new apps.

ML data scientist, ML software engineer, senior architect, and so on are various positions in this discipline.

An experienced Python programmer with a working understanding of machine learning’s fundamental libraries may make the jump to ML as a second profession.

An ML expert may also be familiar with the following other fields of technology, which might be helpful:

• A large number of machine learning methods are based on Bayes rule, Markov models, and other probability processes. In addition, there are statistics such as the mean, median, standard deviation, and Poisson distribution.
• It’s important to remember that ML solutions don’t always work on their own. There is a symbiotic relationship between these and other technologies. As a result, having a solid foundation in software design is beneficial for machine learning (ML) professionals.
• ML Libraries and Algorithms – Professionals in machine learning might benefit from familiarity with methods like linear regression, bagging, boosting, and genetic algorithms.
• To detect patterns, clusters, and correlations in a dataset, an ML practitioner must be able to model the data’s structure. In order to keep them up to date, data models must also be evaluated on a regular basis. If you’re evaluating data, you’ll need to know how to ensure that it’s accurate and comprehensive.
• If you want to pursue a profession in machine learning, you’ll need to know Python. Following Apache Spark and SAS is a third technology.
• Even if you accomplish all of these things once, you won’t be finished. In order to climb the professional ladder, aspirants must always be on the lookout for methods to improve their knowledge and abilities.

A career in machine learning (ML) may put you at the forefront of the digital transformation taking place in a wide range of industries, including healthcare, retail, logistics, and more. In every industry, having knowledge of machine learning (ML) makes you a sought-after employee. In this manner, you have complete control over your ML career. Interested in learning more about Machine Learning? Then check out our Machine Learning Engineer nanodegree!

According to Indeed, the average salary for an ML Engineer in the US is $146k!

More Machine Learning courses.