The Top 5 Machine Learning Certifications in 2022
The world of machine learning certification is a confusing one. There are countless online options that promise a certificate in exchange for taking a course or passing an exam. There is a huge variety in the quality of these certifications and how much credibility they carry with employers. Let’s go through five of the best options today for certifications that will really prove your knowledge to your next boss.
Why get a machine learning certification?
If you’re looking for a job in machine learning, you’ll want to make your résumé or CV as attractive as possible, and a certification can be part of that. In truth, different employers and different cultures place different priorities on certifications. Many US-based companies will put a lot more weight on your professional experience and formal education. But in other countries, certification can be much more important. For example, many students who take my machine learning certification prep course live in India. How much your certification helps may also depend on whether your hiring manager understands the knowledge and value that comes with certification.
Last Updated September 2023
AWS machine learning certification preparation – learn SageMaker, generative AI, data engineering, modeling & more | By Sundog Education by Frank Kane, Stephane Maarek | AWS Certified Cloud Practitioner,Solutions Architect,Developer, Frank Kane, Sundog Education Team
Explore CourseBut the best certifications aren’t easy. Even if you are already experienced in machine learning, expect to spend about a month preparing for the exam and several hours taking the exam itself. You should also prepare to spend a few hundred dollars on exam fees and study materials.
But in short, getting certified in machine learning can’t hurt. It might help, and you will likely become a better machine learning engineer just from all the learning and preparation you need to do. These exams test a broad range of real-world scenarios and problems. While studying, you’ll probably learn about new ones you have never encountered before.
Machine learning certification vs. machine learning certificates
There are countless online courses out there that promise you a “certificate of completion” for completing the course. Employers may be skeptical of these certificates because they often only mean that you played back some course videos. Completion certifications don’t prove that you learned the material and can apply it.
But some course completion certificates are better than others. There are a few comprehensive and well-known machine learning online courses that include a lot of hands-on activities and exercises. Many hiring managers in the machine learning field may have taken them personally and therefore place more value on them. Kirill Eremenko, Jose Portilla, and I (Frank Kane) have taught millions of people around the world about machine learning, and that’s just on Udemy. Well-known and respected institutions such as Harvard and MIT also offer online machine learning courses. A certificate of completion from them will also carry some weight with employers.
But this article focuses on certifications, not certificates. We’ll consider a machine learning certification to indicate that you passed a comprehensive exam in machine learning in a supervised environment. These standardized tests can be challenging. Passing one tells an employer that you have mastered the concepts of machine learning and know how to apply them in real-world scenarios. These exams do not just test your recall of facts, but they also force you to apply those facts to solve problems you’ve never seen before. And that’s exactly what an employer is hiring you to do.
1. AWS certified machine learning specialty
Big tech companies like Amazon, Microsoft, and Google offer many of the most-respected machine learning certifications. While these certifications come with undeniable name recognition and authority, they do tend to focus on technologies offered by the company hosting the exam. Therefore, you need to do your homework and identify which cloud platform your potential employers are using. Most likely, it’s Amazon Web Services (AWS), which is why the AWS Certified Machine Learning Specialty exam is on the top of this list.
Taking this exam myself has changed my mind about how valuable certifications can be. As a former Amazon hiring manager, I didn’t put much weight on somebody’s ability to pass an exam. But passing this AWS exam really does require the ability to apply a wide range of tools and experience to real-world problems. Seeing an AWS Certified Machine Learning Specialty certification on a résumé today tells a hiring manager you have what it takes to succeed.
To pass the AWS Certified Machine Learning Specialty exam, you do need broad and deep knowledge of AWS. But the exam also tests your general knowledge of machine learning and artificial intelligence (AI), the nuances of training these systems, and feature engineering. It’s not just an AWS exam, and that makes it even more valuable.
This 3-hour exam costs 300 USD and covers the following domains:
- Data engineering. This includes storing data using S3, ingesting data using Kinesis and EMR, and transforming data using Glue, EMR, and AWS Batch. The exam also covers non-AWS tools, including Hadoop, Spark, and Hive.
- Exploratory data analysis. This is less specific to AWS and tests your general knowledge on cleaning data, labeling data, feature engineering, analyzing data, and visualizing data.
- Modelling. This is the largest part of the exam and is also not specific to AWS. You’ll need to understand how to choose an algorithm for a given business problem, the nuances of training the algorithm and the choices of computer hardware to do so, how to optimize your algorithm’s hyperparameters, and how to evaluate the algorithm’s results.
- Machine learning implementation and operations. This gets back into AWS-specific territory. You’ll test your knowledge of how to scale machine learning (ML) systems across the cloud in a secure fashion, as well as your knowledge of Amazon’s variety of higher-level ML services.
2. TensorFlow developer certificate
Although not a general certification in machine learning, this exam offered by Google will prove you can build deep learning systems with TensorFlow. TensorFlow is a very popular framework for building AI models.
Deep learning has really taken over machine learning in recent years, and this highly technical certification will certainly carry some weight for any company using TensorFlow. And with Google’s name behind it, it is a trusted certification.
The TensorFlow Developer Certificate is also unusual in that it is relatively inexpensive (100 USD) while covering a lot of material. You are allowed five full hours to complete it (so be sure to eat and use the bathroom first!). It’s not just multiple-choice questions. In fact, the expectation is for you to build actual TensorFlow models to solve exam problems. It’s also open-book — you can use whatever resources you would in the real world to build your solutions. As a hiring manager, I would place a lot of faith in what this certification tells me about a prospective candidate’s real-world performance.
While the TensorFlow Developer Certificate does not cover the broader aspects of machine learning, it does cover a very important piece of it. Specifically, the test covers:
- TensorFlow developer skills. Demonstrate your ability to code against Tensorflow’s API’s in the Python programming language.
- Building and training neural networks using TensorFlow. Prepare data, and build and train models to solve a variety of problems.
- Image classification. Build, train, and tune image recognition and object detection models.
- Natural language processing (NLP). Prepare text and build models to categorize text using a variety of techniques.
- Time series, sequences, and predictions. Prepare, train, and tune models based on time series data for forecasting.
It takes a lot of time and work to prepare for the TensorFlow Developer Certificate exam, but the certification really means something to employers — even if it’s a very specific skill set.
3. Professional machine learning engineer
This certification is similar to the AWS certification, but it focuses instead on Google’s cloud platform. Like the AWS certification, the Professional Machine Learning Engineer certification consists of multiple-choice or multiple selection questions. Some of the exam questions are specific to Google’s platform, but many are not. Like Amazon, Google is a trusted name, and employers usually recognize this certification as reputable.
The Professional Machine Learning Engineer certification exam is shorter and cheaper (200 USD) than the AWS exam. The topics covered on the two-hour exam include:
- Framing ML problems. Choosing the right solutions for given business problems, what to output, and what data sources to use. Also, recognizing when ML is likely to fail and may not be the right solution.
- Architecting ML solutions. This covers building scalable and reliable ML solutions, including your choice of hardware, feature engineering, automation, monitoring, and security.
- Designing data preparation and processing systems. This gets more specifically into data engineering — exploring your data, building data pipelines, and more depth into feature engineering.
- Developing ML models. This explores the details of specific machine learning and deep-learning models, along with the nuances of tuning, training, and scaling them.
- Automating and orchestrating ML pipelines. This tests your ability to combine all the pieces of an ML system.
- Monitoring, optimizing, and maintaining ML solutions. This delves more into the operations side of things and into ongoing tuning, training, and simplification of your models over time.
This exam focuses on building real-world systems at a massive scale. It also covers the challenges of maintaining and tuning those systems. Simply preparing for this exam will teach you a lot about how Google addresses these challenges and also make you a better engineer.
4. Microsoft Certified: Azure AI fundamentals
If your current or prospective employer is a Microsoft shop, then they may be using Azure for the cloud computing resources ML requires. If so, the Azure AI Fundamentals certification exam may be the right one for you. Like the AWS exam, it is specific to its own cloud platform but also tests your general knowledge of machine learning, artificial intelligence, and data engineering.
While the AWS Certified Machine Learning Specialty exam is for experienced technologists and covers advanced material, the Azure AI Fundamentals is for beginners. The exam is 60 minutes and contains only multiple-choice questions. In other words, the Azure AI Fundamentals certification will tell an employer that you know how to apply Azure’s services to machine-learning problems. It doesn’t show that you are a machine learning expert.
The cost of the exam depends on the country you’re in. It tests whether you can:
- Describe artificial intelligence workloads and considerations. In addition to testing your ability to move data and build larger systems to automate machine learning, this also touches on the ethics of AI, security, and privacy.
- Describe fundamental principles of machine learning on Azure. This touches on some more general topics, such as choosing the right machine learning algorithms, evaluating your algorithms, and feature engineering.
- Describe features of computer vision workloads on Azure. This tests the basics of when to use image classification, object detection, character recognition, facial recognition, computer vision, and more.
- Describe features of natural language processing (NLP) workloads on Azure. This covers Azure’s text analytics, language understanding, speed, and translator text services to solve problems in NLP.
- Describe features of conversational AI workloads on Azure. This domain tests your ability to build a chatbot using Azure.
5. Artificial intelligence engineer
The Artificial Intelligence Engineer certification offered by the Artificial Intelligence Board of America (ARTiBA) is last on this list because it’s the least known. Fewer companies recognize it compared to exams offered by Amazon, Microsoft, and Google. It is also expensive (550 USD) for a 90-minute exam. However, it is more than just an exam — you need to apply for this certification, and your exam results are just one part of that. You can achieve one of three different levels of certification based on your experience and education.
The domains of this exam are:
- Essentials of artificial intelligence and machine learning. This covers the basics of AI, supervised learning, unsupervised learning, and ensemble learning.
- Essentials of AI and ML programming. This includes recommender systems, genetic algorithms, search, and building games with AI.
- Essentials of natural language processing (NLP). This covers NLP and speech recognition. Note the exam overview lists object detection and tracking under this domain. However, this is computer vision, not NLP.
- Essentials of neural networks and deep learning. This includes the basics of neural networks, reinforcement learning, and convolutional neural networks.
Machine learning (ML) and artificial intelligence (AI) are closely related, but they are not the same thing. Still, AI is dominating machine learning these days, and a general AI certification that is not tied to a specific technology stack can be useful. However, many employers aren’t familiar with Artificial Intelligence Engineer certification yet. Until they are, this certification may be less trusted than the others. In addition, the exam material may be outdated and less challenging than what’s on the other certification exams.
Choosing your certification
These standardized exams can provide a reliable signal to employers that you know what you’re talking about when it comes to machine learning and that you can apply that knowledge to real-world problems. You should choose an exam based on what specific technologies your future employer uses.
Also note that there may be more than one technology. For example, it’s possible to use TensorFlow within Amazon Web Services. Therefore, achieving both of those certifications would be impressive to employers. Whatever your path, online learning platforms such as Udemy offer focused training to help you prepare for these exams and maximize your chance of passing the first time.
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