All About Machine Learning Engineer Course, Job, and Salary -Recently, you might have heard of the term “machine learning” now and then and perhaps you are curious about whatever that is.
Simply put, machine learning is part of the computer science field specifically concerned with artificial intelligence that works on algorithms to interpret data in a way that replicates how humans learn.
Those who are working in this field are called machine learning engineers. Learn more about what course they take to become one, what kind of job it is as well as its salary in this article.
What is machine learning?
Machine learning, or ML, is a subfield of artificial intelligence (AI) that focuses on developing algorithms and statistical models that enable computer systems to learn from data, identify patterns, and make predictions or decisions without being explicitly programmed or in a nutshell, a machine that is trained to be able to “make a decision” without relying on human explicit instructions.
The process of making this decision is named machine learning modelling, where it creates mathematical models that can learn and make predictions or decisions based on data inputs. It involves training the model on a large dataset to identify patterns, correlations, and relationships between data points, and then using this knowledge to make predictions or decisions on new data.
In the real world, ML has a wide range of applications, such as image recognition, speech recognition, natural language processing, predictive analytics, and plenty of others.
Now, what machine learning engineer jobs do
The position of machine learning engineer is acting as a critical member of the data science team. Its task revolves around designing, developing, and deploying machine learning models and systems as well as improving them.
They also act as a communicator among other data science teams and work directly with related teams who build and construct AI systems.
While its responsibilities vary between each project, generally most ML covers:
1. Data preparation: Collecting, cleaning, and preprocessing data to ensure it is suitable for machine learning algorithms.
2. Model selection and development: Choosing the appropriate machine learning algorithms, tuning hyperparameters, and developing models that can make accurate predictions.
3. Performance evaluation: Evaluating the performance of machine learning models and identifying areas for improvement.
4. Deployment: Integrating machine learning models into production systems and ensuring that they work reliably and efficiently.
5. Monitoring and maintenance: Monitoring the performance of machine learning models in production, identifying and resolving issues, and updating models as necessary.
What study and course do machine learning engineer takes
Typically, ML engineers are from computer science, mathematics, and statistics experienced in programming languages such as Python, R, or Java.
An understanding of machine learning frameworks is also required in becoming ML engineers and while there are a handful of framework applications out there, TensorFlow, scikit-learnー a Python machine learning library, as well as AWS SageMaker, are ones that are commonly used.
Google Cloud also offers a Professional ML Engineer Certification as well as a handful of machine learning engineer courses for you to be prepared for the certification.
Machine learning engineer salary
According to machine learning engineer indeed, the average salary for a machine learning engineer in Australia is $121,630 per year and $137,441 in Melbourne specifically.
The salaries above are meant to be a rough estimate due to the limited amount of data however CSIRO, an Australian Government agency responsible for scientific research, reported that the average base salary for ML engineers is around $103,160 per year.
With the rise of AI, the profession of machine learning engineer may become the current appealing job offer, especially for those who have already interested in the field of computer science, mathematics, or statistics.
It’s also best to get familiar with software and frameworks related to ML engineering and perhaps installing TensorFlow Python machine learning with scikit learn and seeing how it works might be a good first step into this field.