Roadmap To Become A Machine Learning Engineer

Machine learning helps all customer-facing businesses understand customer preferences and customize marketing and advertising. Machine learning uses technology to assess real-world data from people to determine their preferences. 

Only in the last 10–20 years have artificial intelligence (AI) and machine learning (ML) become prominent. The business benefits of machine learning are still being recognized. Machine learning jobs are expected to grow faster than any other, therefore prospects are strong.

Tech-savvy Machine Learning Engineers develop self-running predictive software models. This blog describes the machine learning engineer role and how to succeed. Success requirements include work duties, background, skills, and experience. Check it out!

What is A Machine Learning Engineer?

A machine learning engineer is an IT professional who works on studying, developing, and constructing self-contained AI systems to automate predictive models. They develop AI algorithms capable of learning and making predictions, which characterize machine learning.

Machine Learning Engineering (MLE) is the application of multiple skills and technologies—such as machine learning techniques, tools, principles, and software engineering—to design and create sophisticated computing systems. They span data science, from data collection to model training and production deployment, oversee the entire process, and may do multiple duties.

The machine learning engineer is generally a broader data science team member, collaborating with data scientists, deep learning engineers, administrators, data analysts, data engineers, and data architects.

What Does A Machine Learning Engineer Do?

A Machine Learning Engineer is an expert programmer who uses enormous data sets to investigate, design, and generate algorithms that can learn and predict.

This role designs machine learning systems by assessing and organizing data, running tests and experiments, and monitoring and optimizing machine learning processes to create high-performing systems. Therefore, programming languages such as Python, Java, and C/C++ are required in many jobs.

Job Description of Machine Learning Engineers

The exact duties will rely on the company's size and the size of the data science team as a whole, but the following tasks are usually included in a Machine Learning Engineer job description:

• Design, create, and research machine learning systems, models, and schemes.

• Analyzing, modifying, and converting data science prototypes.

• Searching and selecting acceptable data sets before data collection and modeling.

• Performing statistical analysis and applying the results to enhance models.

• Train and retrain ML systems and models as needed.

• Identifying changes in data distribution that may impact model performance in real-world circumstances.

• Visualizing data to gain deeper insights.

• Analyzing the application cases of ML algorithms and evaluating them according to their success likelihood.

• Understanding when your findings can be used to make business decisions.

• Enhancing existing ML frameworks and libraries.

• Verifying and/or assuring data quality through data cleansing.

Responsibilities of Machine Learning Engineers

Machine learning engineers have two primary responsibilities: integrating data into machine learning models and putting these models into production.

Data ingestion and preparation is a difficult task. The data might come from a variety of sources, and it is often streamed in real-time. It needs to be automatically processed, cleansed, and prepared to meet the model's data format and other requirements.

Deployment is taking a prototype model in a development environment and scaling it up to serve actual users. This may necessitate running the model on more powerful hardware, granting access to it through APIs, and allowing for model upgrades and re-training with fresh data.

Requirements To Become A Machine Learning Engineer

Technical Skills

• Data skills: The Machine Learning Engineer should have a lot of the same skills as a Data Scientist. This job requires using Linux or other versions of Unix, and they need to know a lot about the operating system. Java, C, and C++ are code languages that machine learning engineers often use to prepare data for machine learning algorithms. It would also be very helpful to know about chance and statistics.

• Skills in software engineering: Computer architecture, algorithms (including how to create sorting, optimizing, and searching algorithms), and data structures are crucial computer science concepts for ML engineers. ML Engineers should know software engineering best practices for system design, version control, testing, and analyzing needs since they work with software.

• GPUs and CUDA programming: Graphical processing units (GPUs) speed up work for big machine-learning models. The most common way for GPUs to talk to programs is through CUDA, which is well supported by both GPU hardware and deep learning platforms. A machine learning expert needs to know how to use CUDA.

• Skills for machine learning: A Machine Learning Engineer frequently combines data science and software engineering. Machine Learning Engineers are learning deep learning, neural network architectures, natural language processing, and dynamic programming. Spark and Hadoop are used to prepare huge data for machine learning.

• Applied Mathematics: People who work with machine learning need to be good at maths. In math, linear algebra, probability, statistics, multivariate processing, tensors and matrix multiplication, algorithms, and optimization are all important ideas.

• Data Modeling and Evaluation: Machine Learning engineers need to be able to look at a lot of data, figure out how to model it well, and test the end system to see how it works.

>> Read more: 

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Soft Skills

And even though the working title is "machine learning," soft skills are also very important. Soft skills distinguish successful engineers from those who fail. Machine learning engineering is a technical job, but soft skills like communication, problem-solving, time management, and collaboration help projects succeed.

• Ability to communicate: Machine learning engineers often collaborate with data scientists and analysts, software engineers, research scientists, marketing teams, and product teams, thus communicating project goals, timetables, and expectations to stakeholders is essential.

• Ability to solve problems: Machine learning engineers, data scientists, and software developers must address problems. Machine learning solves real-time problems, therefore critical and innovative thinking is essential.

• Domain expertise: Machine learning developers must understand business needs and the challenges they solve to design self-running software and optimize solutions for businesses and customers. Without domain knowledge, a machine learning engineer may make inaccurate suggestions, miss useful features, and struggle to evaluate a model.

• Manage time: Machine learning engineers must balance stakeholder requests, research, project planning, software design, and rigorous testing. Time management is essential to teamwork.

• Teamwork: Machine learning engineers work alongside data scientists, software engineers, marketers, product designers and managers, and testers because they lead AI initiatives. Managers search for machine learning engineers that can collaborate and foster a supportive workplace.

• Desire to study: Even graduate-degreed machine learning engineers use boot camps, workshops, and self-study to stay current in artificial intelligence (AI), deep learning, machine learning, and data science. The best machine learning engineers are always learning new programming languages, tools, programs, and breakthrough approaches and technologies.

How To Become A Machine Learning Engineer?

For each person, becoming a machine learning engineer looks different. Let's take a look at the 6 steps below to design your career path as a machine learning engineer! 

Step 1: Learn How to Code 

Learn to code at a coding school or in college to see if becoming a machine learning engineer is a good fit for your skills. People who want to become machine learning experts need to learn how to code because algorithms for machine learning are based on code. The job does require a deeper understanding of how artificial intelligence works, though. And tools for machine learning can make it easy to code.

A lot of the time, machine learning engineers need to be good at object-oriented languages like Python and Java. Some instructions on how to learn code in machine learning:

• First, begin with Python. 

• Next, become familiar with Google Colab. 

• Third, enroll in a Pandas tutorial.

• After that, a tutorial on Seaborn is essential.

• Decision Trees is a nice method to start with the algorithm.

>> Read more:  Why Should Python Be Used For Machine Learning?

Step 2: Learn About Machine Learning

Machine learning lessons will take weeks or months and are taught at coding boot camps. However, companies might like to hire people with a bachelor's or higher degree in computer science.

A machine learning school can help you get better at what you do if you already have a bachelor's degree.

There are jobs in machine learning at every level. What you need to learn depends on what you want to do for a living. For an entry-level job, work experience and knowing how to use Python may be enough. For a senior position, however, a college degree in computer science, statistics, math, or physics may be needed.

Moreover, learning about Kaggle and Huggingface is also essential. Hugging Face and Kaggle offer data science and machine learning resources online. Hugging Face focuses on pre-trained language models and NLP, while Kaggle hosts picture classification, NLP, and fraud detection challenges.

Step 3: Use Machine Learning in Real Life

Machine learning engineers and experts need to have work experience and a portfolio to get hired. To get real-world experience, you can take classes in machine learning and ask the teachers about research possibilities.

Learning popular algorithms like linear regression, Naive Bayes, Random Forest, and logistic regression is a good idea once you know how to code in machine learning. On that page, you can learn how to make models for machine learning.

Online groups like Kaggle and Reddit also help people who want to become machine learning engineers find teachers and get answers to their questions. 

 Step 4: Find Internships or Jobs in Machine Learning

You can look for jobs or internships in machine learning before, during, or after a code boot camp or college. Machine learning engineers who are just starting to use machine learning models to make useful products as part of engineering and study teams. Interns in machine learning work with machine learning engineers to make AI programs.

Step 5: Keep Making Your Resume

All fields are still being changed by machine learning. To improve your chances of getting a job as a machine learning engineer, get more education by getting certifications or better degrees.

You could work on machine learning projects or get related certifications to improve your skills. If you want to start, here are some suggestions:

• IBM Machine Learning Professional Certificate (Certificate program)

• IBM AI Engineering Professional Certificate (Certificate program)

• AWS Certified Machine Learning - Specialty (Certificate program)

• Google Professional Machine Learning Engineer Certification (Certificate program)

• Build a Machine Learning Web App with Streamlit and Python (Guided Project)

• Unsupervised Machine Learning for Customer Market Segmentation (Guided Project)

• Cervical Cancer Risk Prediction Using Machine Learning (Guided Project)

• Python for Data Visualization: Matplotlib & Seaborn (Guided Project)

• Google IT Automation with Python Professional Certificate (Certificate program)

• Complete Pandas Bootcamp 2024: Data Science with Python (Certificate program)

• Google Colab Python Course with Free Certificate (Certificate program)

Students learn the basics of machine learning in these online programs. They learn about decision trees, linear and multiple regression, clustering, and principal component analysis.

Step 6: Look for Jobs As A Machine Learning Engineer

You can start looking for work once you have an education, like a college degree or a certificate from a code boot camp. Your resume and portfolio will show how long it takes you to get a job as a machine learning engineer. The need for people in your area will likely also play a role.

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Machine Learning Engineer Salary 

According to the BLS, machine learning engineers earned a median salary of $131,490. The bottom 10% of machine learning engineers made $74,210, while the top 10% made over $208,000. Besides work experience, education, and geography also affect income.

By Experience

If a machine learning engineer has a lot of experience, they can make a lot of things. In general, a machine learning engineer will make more money if they have more experience in their job. The experience can be broken down into the following parts:

By Regions

Now, let's compare machine learning engineer salaries worldwide.

Compared With Others AI Engineer Position

There are other AI engineer positions besides machine learning engineer. Let's take a look at the gap between these AI-engineer positions!

>> Read more: 

• What is the DevOps Engineer Salary in Vietnam?

• Python Developer Salary Worldwide In 2023

Conclusion

The machine learning engineer is the new kid on the block in the data business who is changing things up. In the past few years, this job has grown so quickly that it has surpassed even data science as one of the fastest-growing in the US. 

This blog has given you useful information about the machine learning engineer position. Hope you enjoy this blog and gain practical knowledge!

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