Top FAQs about our Machine Learning Engineering CT was originally published on Springboard.
According to hiring managers, knowing machine learning concepts is important but not enough to get you hired. For this reason, we launched in 2019 our Machine Learning Engineer Career Track. Below we compiled the most common questions being asked by prospects of our MLE CT. If you are wondering if this program is right for you, check them out here.
FAQ 1: Is this the right program for me?
Students with at least one year of experience working in the software engineering industry have the highest likelihood to be successful in our program. Most of the students who finish our program come from having previous experience as data engineers, QA engineers, back-end engineers, software engineers, or other roles in application development.
Outside software engineering disciplines, some of the students who are also likely to succeed in our program are Data Scientists, and Master or Ph.D. graduates in computer science, electrical engineering, applied math, or a related field where computer simulation is an ongoing part of the curriculum.
While some candidates who hold advanced degrees with significant data analysis or are self-taught (via online courses, etc.) may meet the minimum requirements, it is unlikely they will be eligible for our Job Guarantee. Since the program was built specifically for software engineering disciplines, coming from an adjacent or unrelated program can create extra challenges for students who don’t have a strong background with proficient knowledge of a modern programming language (i.e., C++, Java, Python). Instead, our Data Science Career Track with a specialization in Machine Learning might be a better choice.
FAQ 2: Who should NOT consider this program?
If Machine Learning is a side hobby for you or you’re just curious about the A.I. industry, this course might not be the best fit for you.
The students who succeed in our program come because they see becoming a Machine Learning Engineer as their next step in their career. They take our Career Track to get a new job as Machine Learning Engineers (MLEs) or because they want to switch to a more challenging role within their current company.
If you are hobbyist or enthusiast in the A.I. space, free-online tutorials and low-cost courses are a better option for you. You can get started with excellent free online resources like our Introduction to Machine Learning in Python or the dozens of video tutorials available online on YouTube.
This program is for someone who sees the limitations of learning how to build, deploy, and scale Machine Learning models from only watching free online videos or taking low-cost courses. Our program is project-based and requires our students to work on building and deploying their own ML models after they learn the theory presented in the curriculum.
If you are proficient in handling modern programming languages like C++, Java, or Python and you don’t want to go back to school for a 2 years MLE master’s degree, then you could be the right candidate for this program.
If you want to seek a career that relates to A.I., but you are not sure just yet if Machine Learning Engineering is the career you want to pursue long-term, a good alternative to get started is our Data Science Career Track program.
FAQ 3: How is the program different from cheaper online resources?
If you are a hobbyist or enthusiast in Artificial Intelligence, and you are not sure if you want to commit to a career in Machine Learning Engineering, then free-online tutorials and low-cost AI courses are definitely a better option for you! There are dozens of excellent video tutorials on Youtube and other websites to learn the fundamentals of Machine Learning. In fact, we use many of them to explain key concepts throughout our curriculum.
However, if you’re serious about becoming a Machine Learning Engineer, you probably see the limitations of free online tutorials and low-cost courses to learn the engineering side of Machine Learning.
Learning the fundamentals of Machine Learning is one hurdle, but applying those learnings to write your own ML algorithms or to deploy and scale a machine learning model in production is a whole different challenge. Springboard’s Career Track tackles both of these aspects.
The program follows a hands-on, real-life project-based learning methodology where you work on applying the knowledge gained from the theory by completing project milestones. As you complete these projects, you learn the skills required to write ML algorithms, deploy real AI models, and, most importantly, work towards building your own machine learning portfolio as the program progresses.
Comparing a Career Track vs. an online course is like comparing apples and oranges…
The way our students learn our Deep Learning module is a great example to showcase this. This module teaches the principles of Deep Neural Networks along with engineering frameworks like Keras, TensorFlow, and PyTorch. One of the videos we use in our platform to teach the fundamentals of Deep Neural Networks with PyTorch is the video below created by Stefan Otte and available for free on Youtube.
This video is one of the best – if not the best – video to learn the fundamentals of Deep Neural Networks with PyTorch. Does that mean that after watching a one hour and a half video, you can now build and deploy your own Neural Network model into production using PyTorch? Unlikely.
The most critical part of learning the concepts presented in the curriculum is to be able to apply them to real-life problems and challenges. You can only achieve this by working on real-life projects and getting guidance from a Machine Learning Engineering expert.
Let’s expand on that previous point.
Learning the basics of how to build a Neural Network with PyTorch could be an important part of Machine Learning. You can learn this from excellent resources at a much lower cost than our program or even for free (i.e., youtube).
But what’s critical to be a successful Machine Learning Engineer is to decide based on your resources available (or your company’s), how a problem is solved most effectively.
When you transition to a Machine Learning Engineering job, your main challenge will not be if you understand the fundamentals of deep learning. Your main challenge will be to decide the tradeoffs of using one model vs. another to find the most effective solution to a problem – both in terms of model performance and cost.
If building a Neural Network using Pytorch is the right approach to solve the problem an organization is facing, the next step for you as a Machine Learning Engineer is to build, deploy, or scale the specific ML model correctly.
These are the types of skills you will master from our program compared to the skills you gain from other traditional courses.
With Springboard’s Machine Learning Engineering Career Track:
- You learn the fundamentals of Machine Learning from the most reputable content available online; and
- You work on applying that knowledge to real-world problems by working on the different project milestones required to complete the course; and
- You get 1:1 mentorship from a Machine Learning Engineering expert who gives you weekly guidance so you can complete the projects of the program.
Finally, to be awarded our Springboard Machine Learning Engineering Career Track certificate, you will need to combine the skills gained from the curriculum to deliver an ML or DL capstone project. The project requires you to build, deploy, and scale a real model on a specific topic or area you are passionate about.
FAQ 4: How will this program help me achieve my career goals?
If your goal is to study Machine Learning Engineering to take your career to the next level, then this program will help you achieve that goal.
The top 3 reasons our students report for taking our program are:
- They want to switch to an A.I. or Machine Learning Engineering role in their current company.
- They want to build and deploy real Machine Learning Engineering models in their current career or venture.
- They want to advance in their career with a new job in the Machine Learning Engineering space.
The curriculum follows a hands-on, project-based learning methodology where the primary goal is to support professionals with building their Machine Learning portfolio through the completion of multiple projects. Every student gets paired with an industry expert who guides them throughout the curriculum with weekly video check-ins.
How are your goals aligned with Springboard’s goals?
Springboard’s programs are rapidly growing because they follow an outcomes-oriented education model where the final student goal is always aligned with the Career Track they take.
For instance, compared to a traditional Master’s degree, our Career Tracks offer a job guarantee.
Our job guarantee commitment ensures that eligible students get 100% of their tuition reimbursed if –after six months of completing the program – Springboard is not able to help them transition into a career in Machine Learning or A.I. That way, students can commit to our program with additional focus and confidence in Springboard. If our program doesn’t get our students a job, they simply don’t pay.
FAQ 5: What will I learn, and how will I learn it?
What will you learn in our program?
You’ll learn the foundations of machine learning and deep learning —
and how to implement them at scale. The first half of the course focuses on building and scaling a working prototype (either in ML or DL) while the second half focuses on deploying your prototype to production. Download our Syllabus to get a more detailed breakdown of the subjects covered.
How will you learn in our program?
One of the most critical aspects of the Machine Learning Engineering program is the way in which you’ll learn the concepts introduced in the curriculum. The curriculum is rigorous and intensely technical, teaching you the foundations of machine learning and deep learning. These resources, often organized and provided to you from different places across the internet, provide you in a logical and organized manner with the theory and fundamentals you need to learn to be a successful Machine Learning Engineer.
However, that’s just one part of the program.
The Machine Learning Engineering Career Track follows a hands-on, project-based learning methodology. Meaning you will work on applying the knowledge gained from the theory by completing project milestones.
As you work towards completing the projects of the program, you will gain the skills required to write your own algorithms, learn how to deploy and scale A.I. models, and, most importantly, you will work on building your own machine learning portfolio.
To ensure our students successfully gain these skills, they are paired with an expert in the industry who will guide them with 1:1 weekly video meetings on the progress of the curriculum.
To complete the program and master the curriculum, you will be required to submit a capstone project. Using the knowledge, tools, and techniques that you learned in the program, you will build a real Machine Learning or Deep Learning application. The capstone project follows a 10-step guided process throughout the curriculum with guidance from your mentor.
FAQ 6: What is an example of how I will master ML skills in the curriculum?
Our Module 6, A “Deep” Dive into Deep Learning, is a great example. This unit teaches you the principles of Deep Neural Networks, common Neural Network configurations like RNNs, CNNs, MLPs, LSTMs, and engineering frameworks like Keras, TensorFlow, and PyTorch.
While you can find many of the concepts you’ll learn in this module online, the most critical aspect of mastering them is to be able to apply them in real-life scenarios. All of our modules follow a hands-on, project-based learning methodology, and with the help of your mentor, you will complete the projects that come on each module.
Completing the projects ensures you gain the skills required to write, deploy, and scale your own AI models. This learning methodology also allows you to tailor the curriculum towards areas in Machine Learning where you are most interested in.
FAQ 7: How is this program different from a Master’s in ML or A.I.?
Some candidates erroneously compare our program to low-cost online courses and free video tutorials. In the previous FAQ, we break down why this is not a valid comparison for candidates who are serious about a career in Machine Learning Engineering.
However, a master’s degree may offer a similar education model as our Machine Learning Engineering (MLE) Career Track. Here is a break down of how we compare.
- Program Depth. A master’s in Machine Learning and our MLE Career Track are comprehensive academic programs that provide you the tools and knowledge to transition to a career in Machine Learning Engineering.
- Curriculum Strength. Our curriculum was built (and is continuously updated) by Springboard’s MLE subject matter expert with the support of Springboard’s MLE board of advisors (Read more about them in FAQ 9). This process is similar to how a college dean leads the launch of a new Master’s program with the support of professors who teach the subject.
- Project-based learning. The program is taught following a hands-on project-based methodology. For example, our students learn various uses of Spark ML by working on customizing ML pipelines to build their own algorithms and compete with state-of-art algorithms.
- A job guarantee. Probably our most significant difference from a university. Eligible Springboard students are guaranteed a job after they complete our program, or 100% of their tuition is reimbursed.
- Lower cost. The total tuition to earn a graduate degree in the U.S can range from $30,000 to $120,000 (both online and on-campus). The tuition of our program is $7,940.
- Mentorship from Experts. As a Master’s student, you get office hours support from TAs and professors. Our students, however, get unlimited 1:1 mentorship support from Machine Learning Engineering experts already working in the industry.
- Focus on the skills employers are looking for. Most master programs focus on the research side of Machine Learning and could require more in-depth classes on calculus and statistics. Our curriculum will cover these areas just enough, so you can focus on getting experience in projects where you write your own algorithms or learn how to deploy and scale AI models.
FAQ 8: Who built this course and how much knowledge do they have?
Our course was created by Springboard’s Machine Learning Engineering experts. Our Lead Subject Matter Expert (SME) is the head of the program curriculum and works in conjunction with Springboard’s Machine Learning Engineering board. Here is some information about their background:
Sébastien Arnaud – Lead SME
Sebastien has over 17 years of experience working in Data Science, Software Engineering, and Machine Learning Engineering. He reached the Master level on Kaggle.com in 2014 for his past competitive entries, in particular for his real-time competitive solution for the “Job Salary Prediction” using Lucene similarities and Genetic Programming, which ranked him in the top 150 machine learning professionals in March 2013.
Sebastian is responsible for the structure of the Machine Learning Engineering Career Track, the philosophy of the program, and the engineering units.
Check out his track record here.
Eddie (ChengYu) Lin – SME
Eddie is part of Springboard’s Machine Learning Engineering board. He has over five years of experience in Machine Learning Engineering and is responsible for the ML model units.
Check out his track record here.
Dipanjan (DJ) Sarkar – SME
DJ is part of Springboard’s Machine Learning Engineering board. He is also a Springboard mentor for Machine Learning Engineering students. DJ is a top-rated writer for Towards Data Science and is mostly responsible for creating our Machine Learning Engineering projects.
Check out his track record here.
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