The Artificial Intelligence and Machine Learning profession is expected to grow by “23% between 2022 and 2032, which is much higher than average” according to the U.S. Bureau of Labor Statistics, which in comparison, “The average growth rate for all occupations is 3 percent.” If a career in AI/ML interests you, now is the time to strengthen those technical skills, practice technical interviewing, and fine tune your technical portfolio. This can be done through certificate or degree programs. Take a look at our programs to see two different options available to become an AI/machine learning engineer. Each has different structures based on experience and lengths of time to consider before deciding what’s best for you. If you have questions about either program or want to discuss our programs, feel free to contact us!
Develop Required Technical Skills
Arguably the most important part of becoming a Machine Learning engineer is mastering the technical skills needed to do the job. The following skills are crucial to know and understand:
- Python, PyTorch, C/C++, Jupyter
- CUDA (NVIDIA), SageMaker (AWS), TensorFlow, Azure Machine Learning, Keras, Panda
- Various AutoML tools
- Neural networks and their architectures
- Supervised, unsupervised, and reinforcement learning
- All the fundamentals: data structures, algorithms, data types, logic, problem solving and debugging, writing quality code, using Git, etc.
Given the number of open machine learning engineering jobs, companies aren't necessarily looking for itemized machine learning experience with a specific tech stack, but you absolutely must have the fundamentals, previous engineering experience, software development experience, and excellent knowledge of machine learning as a subject. You need proof on your resume that you know what you're doing!
This means you need to be able to:
- Understand an existing code base for a predictive model and its implementation
- Understand major or common cloud-based tools that are part of "ML Ops" to understand data pipelines and flows
- Produce a predictive model
- Work comfortably with large data sets
- Be comfortable with complexity, including complex data sets
- Be able to make decisions about predictive models
- Think critically about data while simultaneously thinking contextually
- Work on a model to help improve it or optimize, to improve from 94.6% to 94.8% accuracy
Companies will value any practical experience building machine learning models, producing quality code, learning quickly, and showing you're a capable engineer.
Build a Technical Portfolio (With Machine Learning Projects!)
Having a strong technical background and a portfolio that shows you have actually worked on machine learning projects and utilized machine learning techniques is a key component to becoming an ai/ml engineer. A strong technical portfolio that has depth and shows the extent of your technical skills and ability to handle databases, deployments and development. You need to ask yourself these questions:
- Have you actually built a model, with a large dataset, that required formatting, processing, training, testing, and deployment?
- Do you have at least 1 specific project in your area of interest: NLP, neural networks, deep learning, computer vision, game rendering, healthtech, etc.?
If you don’t yet have these necessary skills or experience, you need to obtain them before applying to positions that require them in industry.
Understand How Machine Learning is Implemented Using Cloud Tools
Machine learning includes basic knowledge on data pipelines, data storage, ETL pipelines. New cloud tools are always emerging and it’s difficult to stay on top of it all, but you need to know how mass amounts of data across a company are collected, stored, and processed, because your job will likely be to do something with all that data!
While you don't necessarily need to create an AWS account and start using Glue products (because that might get expensive), you do need to know how things work, which services serve what purpose, how they work together, and ultimately, where data you will use is stored, processed, and accessed.
Get Good at Technical Interviews
Practicing for and preparing for technical interviews are crucial to acing them in real-life settings. In Qwasar programs, learners complete 20-40 technical interview role plays throughout their program. They understand the interviewer perspective by living it in our unique, dual interviewer-interviewee perspective. In addition, completing Hackerrank challenges, especially algorithms, sets learners up for success in future career interviews. Another essential part is to practice answering behavioral interview questions as this part is especially important in the interview process.
Earn a Qualification Option 1: Complete a Certificate Program
If you already have significant industry experience in software, and/or you already have a Computer Science degree, it's likely that you don't need to do a Master's in AI/ML to become a machine learning engineer.
If you don't have any technical qualifications, you need to look seriously at certificate programs to ensure they will train you to the level industry requires and give you a strong foundation in computer science fundamentals. You will need these to pass technical interviews, let alone understand what computers do when they process all that data!
You should spend time comparing machine learning engineering jobs with the curriculum for different certificate programs. Make a checklist and see what lines up.
You also want hands-on experience with real-world, life-sized projects.
Qwasar’s AI/Machine Learning Engineer certificate program focuses heavily on algorithms and the application and improvement of different types of algorithms with large and complex datasets. The program covers machine learning, data engineering, fundamentals in data structures and algorithms, and the basics of neural networks and deep learning. Our applied, elite program is entirely project-based, meaning learners develop a strong technical portfolio as well as desired hard and soft skills. Overall, the program is designed to train learners to Silicon Valley standards in machine learning with an emphasis on applied algorithms, critical thinking, and extensive preparation for technical interviews and a technical portfolio.
Earn a Qualification Option 2: Get a Master's of Science in Computer Science (1-2 years)
If you have no Computer Science degree or background, you should consider a Master's of Science in Computer Science with a specialization in AI/Machine Learning or a Master's in AI. While you earn a degree qualification, it takes longer and costs more money.
Not all Master's degrees though focus on applied machine learning, so be careful to find one that works for what you want to do and your learning style. Remember, if your goal is to get a job, you need to have a strong technical portfolio, evidence of significant machine learning project work, and good interview skills.
If you want to go into research as opposed to applied machine learning, then most universities offer traditional Master's degrees that will suffice.
The Qwasar Master’s of Science in Computer Science program is unlike traditional educational programs in our curriculum design and learning instruction. The learning science of project-based learning combined with competency-based education is incorporated to drive engagement and mastery of key concepts in AI/machine learning fundamentals. Learners participate in core topics such as Advanced Machine Learning, Introduction to Machine Learning, Introduction to Deep Learning, Deep Learning for Computer Vision, and more as well as complete a thesis, and capstone project.
No other MSCS degree program in North America uses modern learning approaches or learning science and techniques that truly develop on-the-job skills needed in today’s world.
Qwasar programs offer three specializations for master’s students in:
- Backend Software Engineering
- AI/Machine Learning
- Full Stack Development
AI/Machine Learning engineering focuses on predictions, optimizing and improving the predictions, and early deep learning. Students will complete thesis or projects in Natural Language Processing, computer vision, and have the ability to customize their focus area within the AI/ML field.
AI/Machine Learning Capstone Project
The capstone project counts towards 30 credits of your overall 90 credits for the program. This project can be a huge lift in your overall performance. In that respect, it will last for 8-12 weeks depending on the program in order to create a quality, solid piece of work. Similar to the thesis project, you will have some flexibility in choosing the topic of your capstone project, upon approval by Qwasar. The major requirement is that it is related to the industry that you want to go into. This project is a massive piece to put into your technical portfolio and will demonstrate why you are a perfect candidate for future jobs. You will have to build software and prove your abilities.
AI/Machine Learning Thesis
The thesis requirement at Qwasar is to write a professional paper including a slide deck and a recorded presentation on your topic. This topic can be anything that interests you, but it will be subject to Qwasar approval. There are some restrictions on how wide the subject area of your topic can be. This project is worth 5 credits out of the total 90 for the program. This project will be both peer-reviewed and instructor-reviewed for a final grade. This project will take some quality time and dedication to research and prepare. It cannot be put together overnight. For the full-time program, prepare to work on it for 2 weeks of the program. For the part-time program, prepare to work on it for 4 weeks of the program. Once the paper component is complete, you will need to create a slide deck and then record yourself presenting it for the group. An additional component of the thesis requirement is that you must review two academic articles. You will be required to do 2 per course and 2 as part of each academic project at Qwasar (Capstone, thesis, etc.) on topics such as algorithms, computer science, etc.
Hands-on Machine Learning Experience in Our Engineering Labs
Engineering Labs at Qwasar are themed labs focused on building and creating solutions to real world projects or problems. Similar to an on-site robotics lab that allows robotics students to play with and explore robotics, our engineering labs are focused on empowering students to explore and build software in different areas. Often, projects in these labs go on to become Minimum Viable Products (MVPs), and are the seeds from which companies are born. Think of our Engineering Labs as the Innovation Labs that many companies set up in Silicon Valley. They exist to push boundaries, explore what’s possible, innovate and find creative solutions, and ultimately build a working software or product.
Labs are for students who have completed at least 50% of our curricula, and are also great opportunities for students to show their passions. After the first few courses, students will stop Live Coding Sessions and instead join an Engineer Lab. These are virtual, subject-based labs focused on hands-on projects built in groups. Projects offer an opportunity to explore the subject area while learning to collaborate with other students. Both projects and the labs themselves are great items to put on the resume!
Qwasar’s Engineering Labs are virtual, subject-focused labs where students work in pairs or groups to build a software project. Projects will last anywhere from 2 to 6 weeks depending on the size and difficulty of the project. These are great opportunities to put projects and the Labs on your resume, and to explore some of today’s emerging technologies and subjects. Labs are also opportunities for students to pursue areas they’re passionate about, or areas in which they lack experience and want to gain more exposure.
