Frequently Asked Questions.

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Both the Machine Learning Engineer and MLOps and Systems programs have a similar structure. During the week, you’ll spend about 10-15 hours on your own time with videos, readings, and a coding assignment. Each instructor holds weekly office hours, and we also use Slack, so you can always get help when you need it. During the live sessions, you come together with your cohort and your instructors.

The live sessions for both the MLOps and Systems and Machine Learning Engineer programs are meant to review and clarify the concepts you learned during the week, add context to the material, and give you a chance to ask questions and digest the content more thoroughly. Each live session starts with a lecture and review by the lead instructor, with time for questions and clarifications. Some weeks we also have a guest speaker. Then you’ll break into small groups and work on a project, with assistance from the instructors. You’ll break into small groups and work on it together, but each student submits their own code at the end of the day.

The Machine Learning Engineer program is designed around the Machine Learning Engineer job role. Based on feedback from our employer partners, we built the MLE program to cover end-to-end ML engineering, which includes data processing, model development, and deployment.

The MLOps and Systems program was also developed with feedback from our partners, but isn’t focused on a specific job title. The curriculum covers how to create and maintain the systems and infrastructure to deploy, test and maintain models in production environments. It’s focused on what happens after the model is developed, and covers concepts like CI/CD, model and concept drift, and more.

There is a small amount of overlap between the programs, but they are distinct from each other. They are not designed to build on each other; we consider them parallel programs. We hope you take both!

Our instructors are industry professionals with extensive experience in Machine Learning and Teaching. Each cohort is led by a Lead Instructor, who generally has a strong academic and teaching background. Teaching Assistants support each cohort, and they are hired based on their professional skills. Most are currently working in the Machine Learning field. You can see some of our instructional staff here.

Capstone Projects

Each program includes a capstone project where you develop a project with a group of two or three students. The capstone project is your chance to demonstrate what you’ve learned and showcase your skills. Both programs culminate in a Project Demonstration Day, where each team presents their capstone project to our employer partners. You’ll form teams and select projects in the first half of the program, and finalize your project plans no later than halfway through the program.

For each program, you can design your own project, or pick from one of our industry-sponsored capstone projects. See what our first cohort of the Machine Learning Engineer program built here.

Some of our partners have designed capstone projects with us, focusing on key skills or methods they value in their companies. Students may select to work on these projects as their capstone project. For the MLE program our sponsors have included Samsung, Cisco, Spotify, GroupBy, Ibotta, and GLG Consulting. We may not have exactly the same company projects for each cohort, but there are always several to choose from.

Career Services

Career Services introduces candidates to employers in several ways. Candidates can engage with employers through guest speaking events and informational interviews. Employers are also invited to Project Presentation Events at the end of each cohort. If candidates successfully complete the program, graduate the program, FourthBrain will refer their resume to employers who are looking to source candidates.

Your target role will depend on many variables, including your past experience, your career interests, and where you’d like to work. To determine which roles are best for you make sure that you are working with Career Services to figure out what is the best plan of action for your career.

Machine Learning Engineer job growth has been growing significantly the past 10 years and is continuing to grow. There is no shortage of jobs in the field.

MLOps is still a new field, and is not always a separate job role at many companies. However, we hear from our partners that these skills are in high demand, and it’s beginning to be recognized as a stand-alone field or role. Leaders in the field like Andrew Ng are discussing the need for a focus on these skills, and we anticipate an increase in interest in these job candidates over the next year.

We work with employer partners in a few ways, including as guest speakers, attending our Project Demonstration Day, and sponsoring capstone projects. We don’t have official interviewing programs with any companies, and we can’t guarantee a job. Our goal is to set you up for success in your job search by ensuring you have a strong resume and LinkedIn profile, and your GitHub portfolio contains the ML projects you worked on during the course.

Scholarships & Applications

We occasionally offer scholarships with one of our partners. If we have a current scholarship, you’ll be able to find it here. We don’t offer traditional financial aid. We have two standard payment options. All programs require a $500 deposit to secure your seat. You can pay the balance upfront, during the first week of class, and receive a $250 discount. Or you can pay in three monthly installments, starting from the first day of class.

There aren’t strict deadlines, but each seat is first-come, first-served. The application process for each program includes a take home challenge; for the Machine Learning Engineer program, this takes about 2-3 hours. The MLOps and Systems program quiz takes about 15 minutes. The whole process for both programs takes anywhere from 1 - 10 days, depending on how long you take to return the challenge. You should plan to apply and return your challenge no less than 3-4 weeks prior to the start date. We have gone to a waitlist for prior cohorts.

About FourthBrain

Artificial Intelligence jobs, particularly those related to machine learning and deep learning, have increased by more than 74% over the last five years, and according to LinkedIn’s Principal Economist, “Artificial intelligence will require the entire workforce to learn new skills, whether it’s to keep up to date with an existing role, or pursuing a new career as a result of automation.” We are seeking to prepare more people to enter the AI field by training more Machine Learning Engineers in the most flexible, efficient way possible.

Unlike programming, AI and Machine learning is tough to learn on your own. For many students, learning from a structured curriculum with a cohort of your peers is the most effective way to learn. With FourthBrain, you’ll get access to popular, world-class curriculum including courses from deeplearning.ai, hands-on ML projects guided by a dedicated instructor, and real-time collaboration with peers and instructors.

FourthBrain is not an accredited academic institution, so we do not offer student visas. However, anyone who meets the prerequisites can take our course, provided they can speak and write English at a professional level, and can attend live sessions, which are primarily based in US time zones.

Our Career Services are geared towards the US job market. That means that our employer partners are primarily US-based, and our guidance and coaching is based on US interview practices. If you are not authorized to work in the US, you can participate in most our career services offerings including guest speaking events, coaching, participation in Project Demo Day, and coaching calls with our Career Services team. We cannot guarantee an informational interview / advising call, and we cannot guarantee that our partners are willing to hire non-US workers.

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