What Is a Machine Learning Engineer (ML Engineer)? | Definition from AWS and Other Popular Certifications
Machine learning engineering is a rapidly growing field that plays a crucial role in the development and implementation of artificial intelligence (AI) systems. But what exactly does a machine learning engineer do? In this article, we will explore the definition of a machine learning engineer and discuss some popular certifications that can help professionals in this field showcase their expertise.
A machine learning engineer is responsible for designing and building AI models that can learn and improve from data. These professionals possess strong programming skills and have a deep understanding of mathematics and statistics. They work with big data and use algorithms and techniques to develop models that can make accurate predictions and decisions.
One of the essential skills for a machine learning engineer is knowledge and fluency in cloud platforms such as Amazon Web Services (AWS). Cloud platforms provide the necessary infrastructure and tools for developing and deploying machine learning models. AWS, in particular, offers a range of services and solutions for machine learning, making it a valuable platform for ML engineers.
As the demand for machine learning engineers continues to grow, the field has become highly competitive. To stand out from the crowd, it is beneficial for professionals to pursue certifications and exams that validate their expertise and skills. These certifications not only demonstrate their abilities to potential employers but also deepen their understanding of the technical concepts and tools required to tackle real-world challenges.
There are several popular machine learning certifications that current and prospective ML engineers should consider pursuing. Let’s take a closer look at them:
1. IBM Machine Learning Professional Certificate:
IBM offers this online program on the Coursera platform. It equips students with skills in machine learning algorithms, data science, and Python programming. The program covers various topics, including data preprocessing, deep learning, and model evaluation and deployment. Upon completing the six-course program, students receive a certificate from Coursera and a digital badge from IBM. This certification is suitable for individuals interested in pursuing careers as ML engineers or professionals aiming to enhance their knowledge and skills in the field.
2. AWS Certified Machine Learning – Specialty:
This certification from Amazon focuses on improving an individual’s ability to design, develop, and create machine learning models using AWS Cloud. The program is available in English, Korean, Japanese, and Chinese to cater to a broader audience of data professionals. To pass the certification exam, candidates must score at least 750 out of 1000. The cost to take the exam is $300.
3. Google Cloud Certified – Professional Machine Learning Engineer:
This certification validates a candidate’s expertise in designing, building, and deploying ML models using Google Cloud and industry-proven techniques. The certification exam consists of 50 to 60 multiple-choice questions and covers various topics such as problem framing and solution architecture. The cost of this certification is $200 plus taxes, and it is valid for two years, after which recertification is required.
4. Stanford University’s Machine Learning Course:
Stanford University offers an 11-week course that covers essential topics in machine learning, including mathematics and statistics. This program is available in multiple languages and is highly regarded by machine learning experts. Throughout the course, professors delve into core ML techniques and their practical applications in various fields such as computer vision and data mining. The course costs $49 per month and also offers a seven-day trial.
5. Introduction to Data Science in Python:
This course focuses on teaching students various data science methodologies, including ML algorithms and cross-validation. Students also gain hands-on experience in creating real-world applications such as recommender systems and image classifiers. The course offers two options: free auditing or a verified certificate for $109.
By pursuing these certifications, current and aspiring machine learning engineers can enhance their skills and validate their expertise in the field. These certifications not only help professionals stand out in a competitive job market but also equip them with the necessary knowledge and tools to address real-world challenges.
In conclusion, machine learning engineering is an emerging field that requires a combination of programming skills, mathematics expertise, and knowledge of cloud platforms such as AWS. Pursuing certifications can showcase a candidate’s abilities, validate their expertise, and deepen their understanding of the technical concepts and tools needed in this field. By considering the certifications mentioned above, ML engineers can stay ahead in this rapidly evolving field and make significant contributions to the development of AI systems.
