Machine learning (ML) skills are in high demand, as organizations look to take advantage of potential benefits and use cases such as product enhancement, speech and image recognition, targeted marketing, fraud detection, and natural language processing—to name a few.
With the rise in demand for developers familiar with machine learning, machine learning courses and certifications are taking on greater significance and providing more value for organizations and individuals. The Computing Technology Industry Association (CompTIA), an advocate organization for global IT professionals, says machine learning is among the top 10 most in-demand skills. “Not every software developer will be directly coding foundational AI algorithms, but more and more software applications will feature machine learning to some degree,” CompTIA says. “Developers should be aware of different ML techniques and libraries.”
In a March 2024 report, the employment marketplace Upwork placed machine learning, which is an essential aspect of artificial intelligence (AI), as the second most needed data science and analytics skill for 2024, as well as one of the fastest-growing skills. The AI and ML subcategory saw 70 percent year-over-year growth in the fourth quarter of 2023, Upwork says.
All of this should lead technology and other professionals to at least consider earning one or more machine learning certifications. I talked to technology experts and hiring managers to find out what to look for in a machine learning course and which certifications deliver for developers seeking career advancement.
Machine learning certifications matter more than ever
“Machine learning certifications are worth considering, as they provide structured learning and a deep understanding of complex algorithms, technologies, and methodologies involved in ML,” says John Thompson IT manager at Relyir Artificial Grass, a leading manufacturer of artificial grass products.
“Moreover, they serve as proof of an individual’s skills, often leading to better job prospects or promotions,” Thompson says. “The rapid digital transformation that today’s businesses are undergoing is driving the need for these certifications. Companies are finding immense value in leveraging ML for predictive analysis, automation, and decision-making functions, thereby increasingly seeking certified ML professionals to drive these initiatives.”
ML certifications “are certainly worth getting, as they equip individuals with specific knowledge and skills, making them more employable in high-demand fields like artificial intelligence, data analysis, and tech-driven operations,” says Dev Chandra, an automation consultant and fractional COO at The Process Hacker.
“The driver for these certifications is the escalated demand for specialized skills,” Chandra says. “As businesses increasingly leverage big data, the ability to understand, interpret and apply data through machine learning is invaluable. This growing demand transforms into a competitive advantage for certified professionals.”
Machine learning certifications are valuable for those looking to enhance their competencies or specialization, says Javier Muniz CTO at LLC Attorney, a provider of business services.
“As business processes and practices increasingly incorporate AI and machine learning capabilities, having a detailed understanding of these technologies can make a candidate more competitive, and potentially help them drive benchmark-beating results once hired,” Muniz says.
Machine learning skills employers look for
The demand for machine learning certifications is driven by the growing reliance on and investment in AI by companies across various sectors, Muniz says. “Understanding the mechanisms behind these complex systems is vital to leverage their full potential, and certified individuals are often better [aware] of the latest cutting-edge developments.”
For organizations, having staff with machine learning certifications can be a valuable asset, helping them to drive innovation and guiding intelligent decision-making processes, Muniz says. Companies in sectors such as financial technology and healthcare are seeing benefits from AI and machine learning, and having people certified in machine learning skills is important.
“I have seen the value of machine learning certification firsthand in this field,” says Kevin Shahnazari, founder and CEO of FinlyWealth, a credit card recommendation platform provider. “Machine learning certifications are worth their weight in gold.” Certifications offer a structured path for professionals to gain and validate their expertise in what is a fast-advancing field, Shahnazari says. “Companies want to make sure they have a means of identifying skilled ML practitioners, and certification provides a benchmark.”
Machine learning certifications “are definitely worth getting,” says Tharindu Fernando, a full-stack developer at Net Speed Canada, a site that provides data to help clients choose the best Internet plan, and who specializes in developing websites, applications, and platforms in the healthcare sector. “In my experience working on various tech projects, including developing websites and apps for the healthcare sector, I’ve seen firsthand how these certifications can boost a professional’s credibility and skills.”
In healthcare, there’s a growing need for professionals who understand both the technical and practical aspects of machine learning, Fernando says.
Real-world experience and certifications go hand-in-hand
Of course, certifications alone will not necessarily lead to a job offer. Experience in using machine learning tools is also valuable for technology professionals.
Machine learning certifications “can act as catalysts to promotion, salary increase, and an enriched understanding of data-driven decision-making,” Chandra says. “That said, while certifications can be a positive differentiator, it’s crucial to remember they’re one piece of a larger puzzle.”
Real-world experience, problem-solving skills, and continuous learning are equally important in this ever-evolving field, Chandra says.
“You will need to gain foundational and real-world expertise in ML models, algorithms and data management,” says Ram Palaniappan, CTO of IT services company TEKsystems.
“ML development and deployment success is measured based on accuracy,” Palaniappan says. “ML accuracy improves through iteration, and this needs strong knowledge on data and models. When it comes to certification, it’s not something you will need if you already have a job [in] ML engineering. You will always be able to explain different scenarios and how you have helped ML algorithms perform. However, if you are looking for an entry in ML engineering, then certifications play a key role in getting you called for an interview.”
Popular machine learning certifications
The value of a machine learning certification stems from the range of skills it covers and the machine learning tools or platforms featured. The following five are among the most popular according to experts.
AWS Certified Machine Learning – Specialty
The AWS Certified Machine Learning – Specialty credential from cloud provider Amazon Web Services (AWS) helps organizations identify and develop talent with critical skills for implementing cloud initiatives, according to AWS. Earning the certification validates expertise in building, training, tuning, and deploying machine learning models on AWS.
The certification is intended for individuals who perform a development or data science role and have more than one year of experience developing, architecting, or running machine learning or deep learning workloads in the AWS Cloud. AWS recommends that candidates have at least two years of hands-on experience developing, architecting, and running machine learning or deep learning workloads in the AWS Cloud, the ability to express the intuition behind basic machine learning algorithms, experience performing basic hyperparameter optimization, and other capabilities.
Databricks Certified Machine Learning Professional
The Databricks Certified Machine Learning Professional certification exam assesses an individual’s ability to use Databricks Machine Learning and its capabilities to perform advanced machine learning in production tasks, according to Databricks. This includes the ability to track, version, and manage machine learning experiments and manage the machine learning model lifecycle.
In addition, the certification exam evaluates a candidate’s ability to implement strategies for deploying machine learning models. Finally, candidates are assessed on their ability to build monitoring solutions to detect data drift. Individuals who pass the certification exam can be expected to perform advanced machine learning engineering tasks using Databricks Machine Learning.
Google Cloud Professional Machine Learning Engineer
A professional machine learning engineer builds, evaluates, produces, and optimizes machine learning models using Google Cloud technologies and has knowledge of proven models and techniques, according to Google Cloud. The Google Cloud Professional Machine Learning Engineer certification ensures a machine learning engineer can handle large, complex datasets and create repeatable, reusable code; consider responsible AI and fairness throughout the machine learning model development process; and collaborate closely with other job roles to ensure long-term success of machine learning-based applications.
The Google Cloud Professional certified machine learning engineer also must have strong programming skills and experience with data platforms and distributed data processing tools, Google Cloud says. This professional is also expected to be proficient in the areas of model architecture, data and machine learning pipeline creation, and metrics interpretation. By training, retraining, deploying, scheduling, monitoring, and improving models, the machine learning engineer designs and creates scalable solutions.
Microsoft Certified: Azure Data Scientist Associate
The Azure Data Scientist Associate certification from Microsoft prepares candidates for managing data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Python, Azure Machine Learning, and MLflow. It’s designed for data scientists. Candidates for this certification should have subject matter expertise in applying data science and machine learning to implement and run machine learning workloads on Azure, according to Microsoft.
Responsibilities for this role include designing and creating a suitable working environment for data science workloads; exploring data; training machine learning models; implementing pipelines; running jobs to prepare for production; and managing, deploying, and monitoring scalable machine learning solutions. Candidates should have knowledge and experience in data science by using Azure Machine Learning and MLflow.
Stanford University’s Machine Learning Specialization
Presented by the online learning platform Coursera, the three-course Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. The beginner-friendly program teaches the fundamentals of machine learning and how to use it to build AI applications.
The program provides a broad introduction to modern machine learning, including supervised learning, unsupervised learning, and best practices used in Silicon Valley for AI and machine learning innovation. Specifically, the courses cover areas such as building machine learning models in Python; creating and training supervised models for prediction and binary classification tasks; and building and training a neural network with TensorFlow to perform multi-class classification.