Embrace Machine Learning Engineering Jobs

September 28, 2023 Stemta Corporation

Embrace Machine Learning Engineering Jobs: A Career Shift Worth Pursuing

In today’s dynamic job market, Data Science has long held the spotlight as one of the most coveted professions. But is it still the reigning champion, or has a new contender emerged? We believe it has—Machine Learning Engineering jobs is rising to prominence and eclipsing Data Science in the job market. Let’s discuss why Machine Learning Engineering jobs is surpassing Data Science and how you can embark on this exciting journey.

Machine Learning Engineer vs. Data Scientist: To understand this shift, let’s begin by distinguishing between these two roles. As a Snowflake article succinctly puts it:

• Data Scientists analyze data and extract insights.
• Machine Learning Engineers focus on coding and deploying machine learning products.

In essence, while a Data Scientist creates and evaluates models, a Machine Learning Engineer takes those models, deploys them into production, and ensures their ongoing maintenance. This transition is essential because it transforms a trained model into a revenue-generating product.

Both roles are undeniably valuable, but Data Scientists have been primarily hired for data analysis and modeling. Now, as companies seek to extract value from these models, the demand for Machine Learning Engineers has surged. Shockingly, according to Venture Beat, a staggering 87% of data science projects never make it into production due to a lack of skilled Machine Learning Engineers.

This discrepancy is evident when examining job postings on platforms like Glassdoor. In California, there are currently 1,809 job postings for Data Scientists compared to a staggering 3,345 postings for Machine Learning Engineering jobs. The demand for the latter is almost twice as high.

So why can’t Data Scientists simply acquire the skills to put models into production? The answer lies in focus. Data Scientists primarily deal with ML code, which is just a fraction of the entire ML infrastructure. Expecting them to manage both code and infrastructure for deployment, monitoring, and more would be overly complex. Hence, the synergy between Data Scientists and Machine Learning Engineers is essential for maximizing the value of data.

The Path to Becoming a Machine Learning Engineer: Now that we’ve established the demand for Machine Learning Engineers, let’s explore the skills and tools required for this exciting career path:

Most Valuable Skills:

1. Computer Science Fundamentals and Programming: Mastery of data structures, algorithms, computability, and computer architecture is crucial.
2. Probability and Statistics: Proficiency in probability, statistical measures, distributions, and analysis methods is essential.
3. Data Modeling and Evaluation: The ability to uncover meaningful patterns and predict outcomes is key.
4. Applying Machine Learning Algorithms and Libraries: Choosing the right models, understanding hyperparameters, and working with ML libraries like TensorFlow and Scikit-learn are vital.
5. Software Engineering and System Design: Comprehending system components and building interfaces is crucial.

Tools to Learn:

1. Python: The primary language for machine learning.
2. Linux: Essential for infrastructure-related tasks.
3. Cloud Platforms: Familiarity with popular providers like GCP, Azure, and AWS is valuable.
4. Docker and Kubernetes: These tools are essential for deploying models into production and creating robust architectures.

Other Useful Online Courses:

1. Deep Learning Specialization by Andrew Ng: A comprehensive course on deep learning and hands-on application.
2. Machine Learning Nanodegree by Udacity: Focuses on training ML models and deploying them in real-world scenarios.
3. IBM Machine Learning Professional Certificate: Covers a wide range of machine learning topics with ample hands-on practice.

The career landscape is shifting towards Machine Learning Engineering as the demand for skilled professionals in this field continues to grow. You’ve learned about the essential skills, tools, and courses to kickstart your journey. So, don’t hesitate—get hands-on, embark on projects, and aim to put your trained models into production. Becoming a Machine Learning Engineer is not just a career choice; it’s a journey into the future of technology.

Learn more about STEM Careers with the Stemta Career Guide