Your 6 Step Guide to Become an AI ML Engineer

You’ve made the decision to become an AI/ML engineer - now it’s time to put it into action. We’ve developed this 6-step guide to follow in order to approach the process of becoming a well-rounded engineer, ready to tackle an industry career.

Step 1: Understand What an AI/ML Engineer Is and Does

The first step in the process is to truly understand the job description and functions of an AI/ML engineer. This is crucial to knowing it's really the right path for you and understanding the gravity of the workload and fundamental concepts. This can be done by researching the programming languages needed to be successful in AI/ML careers, understanding what tools are used, and identifying terminology you're not familiar with. 

For individuals aspiring to pursue a career in Artificial Intelligence (AI) and Machine Learning (ML), proficiency in certain programming languages is fundamental. These languages serve as the backbone for implementing algorithms, manipulating data, building models, and deploying solutions. Here are the essential programming languages for AI/ML careers:

• Python: Python stands out as the most popular and widely used language in the AI/ML domain due to several reasons:
  1. Ease of Use - it's simple and readable syntax makes it beginner-friendly and conducive to rapid development.
  2. Rich Libraries - it offers powerful libraries like NumPy, Pandas, Matplotlib, and Scikit-learn, which are extensively used in data manipulation, visualization, and machine learning tasks.
  3. Machine Learning Frameworks - Python serves as the primary language for renowned ML frameworks like TensorFlow, PyTorch, Keras, and Scikit-learn, allowing seamless model implementation and experimentation.
• R: R is another language popular among data scientists and statisticians, offering good statistical computing and visualization, making it suitable for statistical modeling and data exploration.
  It also has a comprehensive collection of packages like dplyr, ggplot2, and caret, aiding in data manipulation, visualization, and machine learning tasks.
• Java or C++: Occasionally used for performance-critical tasks or deploying models in resource-constrained environments, Java and C++ can be preferred over Python due to their efficiency or speed.
  
  Julia is also emerging as a potential language for scientific computing and machine learning due to its speed and ease of use.

For newcomers, starting with Python and gradually exploring other languages based on project needs or specific career paths is a pragmatic approach. Mastering these languages alongside fundamental AI/ML concepts and frameworks is crucial for a successful career in this field. It’s also important to focus on the algorithms and data structures that lay the foundation for most concepts in AI/Machine learning. 

Next is understanding the fundamentals concepts of AI/ML engineering. You'll need to build a solid foundation in statistics, probability, and machine learning as a mathematical discipline, not just how to program/code for machine learning. By taking a comprehensive look at these ML concepts such as supervised, unsupervised, and reinforcement learning, you can prepare yourself for what’s to come in future educational settings to get ready for AI/ML careers. Also, the ML algorithms such as linear regression, decision trees, neural networks are important to dive into to get a full look at machine learning. Lastly, hands-on practice with ML libraries/frameworks like TensorFlow, PyTorch, and scikit-learn will come in handy for the rest of your learning journey. 

Another crucial part of understanding an AI/ML engineer's job is taking a look at the work itself. A day-in-the-life for an AI/ML engineer involves:

• Lots of optimization, going from 94.5 to 94.7% accuracy
• Lots of working with enormous data sets
• Lots of data cleaning, formatting, checking, interpreting, analyzing, training, testing
• Lots of critical thinking!

Step 2: Research and Choose the Right Training Program for You

Once you’ve given yourself a great understanding of what an AI/ML Engineer is and does, it’s time to choose the right training program to equip yourself with the skills to become employed. While there are many options out there, the first thing we’d recommend is to take a look at job descriptions that interest you specifically and ensure that your program covers those skills you;ll need to obtain those jobs. It would be beneficial for you to ensure you complete a program that sets you up for long-term career success. 

There are typically three main routes you can take with training options for AI/ML engineering: certificate programs, degree programs, and bootcamps. Each program has pros/cons, costs, varying lengths of time and different styles of instructions. It’s imperative that you choose the one that will work best for you in all of those aspects, so you are most likely to succeed. 

First, we’ll talk about certificate programs. We offer a certificate program in AI/ML Engineering at Qwasar that is 12-18 months in length depending on full or part time commitment. The total program cost is $2,400 which can be paid monthly ($100/month for part-time or $200/month for full-time). This option is great for learning who are brand new to coding or career switching from a different industry. We utilize active, project-based learning with competency-based education to ensure learners master the fundamental concepts of AI/ML. 

Next, we’ll discuss degree programs. One option is to attend a four-year college/university to obtain your Bachelor’s degree in AI/ML. This option can range in price depending state and aid level. We offer a Master’s of Science in Computer Science with a specialization in AI/ML at Qwasar. This program is 1 year full time or 2 years part time. The total program cost is $33,000 before scholarships. Most students receive scholarships, which are available on our website, and reduce tuition to $13-15,000. Our degree program has a modern admissions process with no bachelor’s degree required, but technical experience is necessary. The curriculum includes a thesis, capstone, and academic article reviews in addition to credit courses. This program is great for those looking to upskill, advance their knowledge, and further their education in the field. 

Lastly, we’ll talk about bootcamps. Bootcamps have gained popularity in recent years for their quick nature. They are typically 6-18 weeks in length and range from $799 - $21,950 according to Career Karma and sometimes involve income share agreements. The instruction style is fast paced and lacks flexibility for most learners. At the end, many graduates feel left behind and without support or fully understanding of all the concepts. While this option works for some, it’s important to do your research and be sure this option works with your learning style, as they can be pretty pricey. If you have no background in computer science, programming, or mathematics, please don't do a bootcamp!

Step 3: Navigate the Course and Curriculum

Once you’ve chosen your training program it’s time to dive into the learning and curriculum. This is the fun part! You’ll be engaged in deep learning principles like neural network architecture. You will have the opportunity to gain practical experience with deep learning frameworks such as Keras, TensorFlow, and PyTorch. You will also work on real-world projects and Kaggle competitions. Another part of the learning process is working on open-source contributions and even completing your first internship. This is a great way to gain experience, network and work on industry projects with mentors. Throughout this whole process you will be simultaneously  building a strong portfolio showcasing AI/ML projects and their impact. This is the most important part in this whole process.

Step 4: Decide What Area of ML or What Industry to Work In

You’ll need to consider what specific area of machine learning or what industry you’d like to work in. This ties directly to where you’d like to apply to jobs and eventually work in the future.

Machine learning is a vast field, and companies hiring ML Engineers generally expect to see at least some specialization or past experience in a given area of industry.

A good training program should push you in this direction, and can serve as a great time to brainstorm different paths such as:

• Computer vision
• Natural language processing
• Large language models
• Autopilot
• Fintech, banking, or fraud detection
• Game development
• Robotics
• Advanced manufacturing
• Medical technology
• …and many more

You should take a look at the industry differences and compare the career opportunities such as ML career advancements within each one and the pathways to next levels. Some things to consider are collaboration level, continuous learning, business impact, ethical implications, data quality and volume, and domain knowledge.

Also, for most industries, you must have at least one project in your portfolio that matches with the area of ML industry you want to work in so now is a great time to pursue that while still in your training program.

Step 5: Develop a Technical Portfolio

A huge component of all the work you complete in your AI/machine learning training will be to display it concisely and effectively in a technical portfolio. This is where the following tips will come in handy to show future employers that you are skilled and prepared to do the job:

• Select Projects Wisely
  Choose projects that showcase your skills and interests in AI/ML. Aim for diversity in problem domains (e.g., healthcare, finance, natural language processing, computer vision) and techniques (classification, regression, clustering, deep learning, reinforcement learning).
• Data Collection and Preparation
  Demonstrate your ability to handle real-world data. Showcase your skills in data preprocessing, cleaning, transformation, and feature engineering. Explain the significance of each step in preparing the data.
• Model Development
  Showcase your proficiency in building AI/ML models. Include different algorithms and architectures you've implemented and explain why you chose a particular model for a given problem. Highlight the hyperparameters tuning process and your model evaluation techniques (e.g., cross-validation, evaluation metrics).
• Visualization and Interpretation
  Present results effectively through visualizations (e.g., matplotlib, seaborn, Tableau) and interpret the model's output. Explain complex concepts in a simple, understandable manner, demonstrating your ability to communicate technical results to non-technical stakeholders.
• Code Repositories
  Host your code on platforms like GitHub or GitLab. Keep your repositories clean, well-documented, and organized. Include README files explaining the project overview, installation guide, usage instructions, and results achieved.
• Collaborative Projects or Contributions
  Participate in open-source projects or collaborate with others on AI/ML-related tasks. Contributions to well-known projects or repositories can be valuable additions to your portfolio and demonstrate teamwork and adaptability.
• Blog Posts or Publications
  Write a technical blog post or article explaining your projects, methodologies, or insights gained. Share these on platforms like Medium, Towards Data Science, or your personal blog. Publishing in academic or industry conferences/journals is also a great addition.
• Online Portfolios and Personal Website
  Create an online portfolio or personal website showcasing your projects, blog posts, achievements, and skills. Use this platform to tell your story and highlight your expertise in AI/ML.

Doing some or all of these things will help you in the long run.

Step 6: Network and Build Soft Skills

The final step in the process to becoming an AI/ML engineer is to work on your professional development and networking skills. These need to be honed as you enter the working world and will constantly come into contact with others in industry. In addition to technical proficiency, soft skills play a pivotal role in the success of a career in Machine Learning (ML). These skills complement technical expertise, enabling professionals to effectively communicate, collaborate, and innovate in the dynamic field of ML. Here are some essential soft skills for machine learning careers:

Communication Skills

• Can you explain complex concepts or technical ideas in a simple and understandable manner for non-technical stakeholders?
• Can you write and produce clear and concise documentation of algorithms, models, and project workflows for team members and future reference? (Not just using GenAI)
• Can you communicate findings and results effectively through presentations or reports?

• Can you think critically, evaluate problems, breaking them down into manageable parts, and devising creative solutions?
• Can you troubleshoot effectively, identifying and addressing issues in models, data, or algorithms to enhance performance?

• Are you flexible, adaptable, and open to learning new tools, libraries, and techniques as the field evolves?
• Are you continuously learning?

• Can you work collaboratively in a team? Can you work effectively in cross-functional teams comprising engineers, data scientists, domain experts, and business stakeholders?
• Are you receptive to feedback and constructive criticism to improve models and methodologies?

• Can you effectively prioritize tasks and manage time effectively to meet project deadlines?
• Do you understand effective resource allocation? Seeing yourself too as a resource for completing a project?

• What are your responsibilities to developing ethical AI? Do you understand the implications of decisions you make regarding an algorithm?
• Do you understand the importance of data privacy and complying with relevant regulations?

Good Luck!

We hope this 6-step guide on how to become an AI/ML engineer is useful. Good luck in your learning journey - these are not easy subject areas, but undoubtedly ones in high demand with a strong career potential.