A machine learning engineer is a professional responsible for researching, building, and designing self-running software. Engineers specialising in machine learning create artificial intelligence systems that eventually learn and can reasonably anticipate the future.
A machine learning engineer’s duties comprise responsibilities like organising, evaluating, and analysing data, running tests, and optimising the learning process in order to create the most effective machine learning model.
Let’s dive in and explore the job description of a machine learning engineer and how you should prepare for such a position. Continue reading to know more!
What Does Machine Learning Engineer do?
Machine learning engineers are tasked with choosing the right algorithms as per compatibility and other requirements. Furthermore, they are also expected to gather proficiency in programming languages like Java, Python, and C/C++. The steps involved in a machine learning engineer’s overall job description are:
- Collecting data
- Processing and re-processing data
- Choosing a machine learning algorithm
- Training the machine learning model
- Testing the machine learning model
- Fine-tuning the machine learning model
Roles and Responsibilities of a Machine Learning Engineer
While a machine learning engineer’s precise duties and roles may differ from one business to the next, we have compiled a list of the most general responsibilities expected of machine learning engineers.
- Researching, designing, and developing machine learning schemes, models, and systems.
- Using statistical methods to improve existing models.
- Studying and converting data science prototypes.
- Analyzing real-life case studies of machine learning algorithms and categorising their success probability.
- Selecting the right type of data set before proceeding with data collection and modelling.
- Training and re-training machine learning models as and when required.
- Verifying the quality of collected data.
- Understanding when your findings can be implied to make business-related decisions.
- Keeping up with the latest advancements in machine learning and artificial intelligence domain.
- Contributing to the existing machine learning framework within your company.
Skills Required To Be a Machine Learning Engineer
To flourish as a machine learning engineer, one must combine and imbibe the skills of a data scientist and a software engineer. Let us take you through some of the skills required to be a skilled machine learning engineer:
Software Engineering Skills: Some of the main computer science concepts essential for machine learning engineers to know about are the basics of computer architecture, algorithms, and data structure. Furthermore, since machine learning engineers essentially work with software, it is also important for them to learn about basic software engineering concepts and best practices related to version control, system design, testing, and requirement analysis.
Data Skills: Machine learning engineers should have skills such as being able to evaluate predictive algorithms, programming languages, data modelling, and so on. These skills are usually associated with data scientists. Furthermore, it is also crucial to know about statistics and probability.
Soft Skills: In addition to the above-mentioned technical skills, machine learning engineers are also expected to possess soft skills such as leadership qualities, time management, teamwork, and polished communication skills. Furthermore, since the field of artificial intelligence and machine learning is continuously evolving, machine learning engineers should be open to continuing their education and always learning.
Steps to Become Machine Learning Engineer
To become a successful machine learning engineer, you need to follow the steps given below:
Step 1: Learn Programming
To develop a machine learning model, you must have proficiency in programming languages. If you are new, start with Python and then proceed to learn other languages. Overall, without learning programming languages, you cannot implement your machine learning models in reality.
Step 2: Improve Your Math Skills
Being familiar with mathematical ideas like matrices, probability, statistics, calculus, and linear algebra is a prerequisite to becoming a machine learning engineer. You can create an accurate and workable machine learning model with the aid of all these ideas.
Step 3: Read Up On Machine Learning Concepts
After strengthening your arithmetic and programming abilities, start studying the fundamentals of machine learning, including the different kinds of machine learning algorithms and their characteristics. You should also familiarise yourself with all the other ideas that machine learning is built upon.
Step 4: Learn How To Use Data Science Tools
As an aspiring ML engineer, you must learn how to use data science tools like Anaconda and Jupyter if you want to finally put your programming and machine learning skills to use in actual applications. Leverage these data science tools to experiment and understand their usage better.
Step 5: Learn To Handle Data
The next obvious step is to learn how to handle data since your machine learning model will rely on the data you supply it with. This is why you should also learn how to manipulate, analyse, and visualise data for the most optimum machine learning model.
Step 6: Practice With Real-World Projects
Once you have accomplished the steps above, you should use your knowledge and practice with real-world projects. You can work on projects available on various websites or even look up problems on the internet that you can solve with your machine learning model.
Step 7: Learn About Deep Learning
Once you’ve mastered small datasets, you should move on to handling larger datasets which can be done through deep learning. In deep learning, systems automatically extract all relevant data and do not require manual input of information. Hence, this gives deep learning a notable advantage over traditional machine learning.
Step 8: Create a Strong Resume
The last step is to make a strong resume so that recruiters can understand the knowledge and experience you possess in this field. Make sure to create a presentable resume that is grammatically accurate and highlights your educational background, skills, and real-life experience with handling machine learning models.
Conclusion
In conclusion, machine learning engineering is a course that is only becoming more valuable with time. Therefore, such a subject matter should be learned with all seriousness from a reputed source. In this regard, we recommend signing up for Eduonix’s Machine Learning for Absolute Beginners course, which covers all the basics of machine learning along with real-life projects. With the help of this course, individuals can fully expect to ace the field of machine learning.
