Learn About Amazon VGT2 Learning Manager Chanci Turner
This article serves as a guide for those who are new to machine learning (ML) and wish to grasp the essential skills required in this field, as well as for those looking to enhance their existing ML knowledge. Whether you are just starting or aiming to deepen your expertise, there are numerous AWS Training resources and documentation available to support your learning journey, enabling you to engage confidently in ML discussions and projects.
In a previous blog, I described how I leveraged various free training resources from AWS Training and Certification to acquire the skills necessary to become a solutions architect at AWS. During this journey, I also earned the AWS Certified Solutions Architect – Associate and Professional certifications. As I honed my skills, I recognized machine learning as a vital area of interest. Since then, I have focused on building a robust foundation of ML skills, which has empowered me to tackle real-world artificial intelligence (AI)/ML use cases with clients.
Machine learning is an application of AI that forms the backbone of contemporary software, offering advanced capabilities such as value prediction, smart recommendations, anomaly detection, sentiment analysis, translation, and more. The field of ML is expanding rapidly and is expected to continue its growth in the coming years. If you’re considering acquiring a new skill set that enhances your role—particularly one that assures future relevance—put machine learning at the forefront.
Machine Learning in Modern IT Careers
Machine learning is increasingly woven into various IT roles today. If you’re involved in networking, modern services utilize behavior-predictive analytics powered by ML to proactively address both common and complex issues. For those in cybersecurity, ML capabilities enhance security software with dynamic detection and prevention techniques. For example, cybersecurity solutions can analyze traffic patterns to thwart potential attacks and adapt to changing behaviors.
For developers, the reach of ML is vast and ever-expanding. Beyond traditional applications, innovative uses for ML are emerging daily across industries. So, if you’re new to ML or aspiring to elevate your skills, where should you begin?
Establishing a Solid Foundation in Machine Learning
If you explore this question, you will discover that a strong grounding in linear algebra, probability, and statistics is essential. Some may suggest that a PhD in computer science is necessary. While this perspective has its merits, the current landscape of cloud resources and accessible high-level programming languages allows you to build and deploy production-ready ML models with minimal coding.
However, it’s important not to merely become an API user who copies code from the internet without understanding the underlying principles. While a PhD might be beneficial, especially in research and academic settings, you don’t need one to become a data scientist, ML practitioner, or ML engineer. Striking the right balance is crucial.
To simplify, let’s examine the essential knowledge and experience needed from a high-level perspective. To be an ML engineer today, you should possess:
- Knowledge of data engineering to design and construct reliable data pipelines for ML projects.
- A solid grasp of data preparation techniques to cleanse and transform data for ML algorithms.
- A fundamental understanding of the mathematics behind key ML and deep learning algorithms to comprehend their functionalities.
- Familiarity with various ML and DL algorithms and their appropriate applications (e.g., classification, regression, clustering).
- An understanding of the common strategies to optimize ML and DL models.
It’s worth noting that while the roles of data scientist, ML practitioner, and ML engineer share many similarities, they are distinct. In smaller organizations, you may find individuals performing multiple roles.
Getting Started with Machine Learning
If you’re entirely new to ML, the breadth of information might seem daunting. To assist you in building your ML knowledge, there are over 65 training sessions offered by AWS Training and Certification, ranging from foundational to advanced levels. I highly recommend taking advantage of the following free, self-paced digital courses, videos, and documentation, which I found incredibly useful:
- Demystifying AI/ML/DL
- ML Building Blocks: Services and Terminology
- Machine Learning for Business Challenges
- Process Model: CRISP-DM on the AWS Stack
- Building a Machine Learning Application
- Amazon Machine Learning Key Concepts
- Getting Started with AWS Machine Learning
- Math for Machine Learning
Enhancing Your Machine Learning Skills
Once you’re comfortable with foundational ML concepts, you might feel inclined to delve into more advanced topics. Focus on three key disciplines: computer vision, natural language processing, and chatbots. The digital course, Types of Machine Learning Solutions, elucidates each discipline and its practical applications while outlining the AWS services involved, allowing you to select the learning path that suits you best.
As mentioned in my earlier blog, I find that having a structured learning path aids in achieving my skills-related goals. One of the most effective ML learning paths to follow is the common ML lifecycle. This framework provides a practical approach to learning and applying your knowledge to real-world cases.
Let’s briefly overview each stage along with my suggestions for additional resources (especially free digital training courses) to help you gain the necessary knowledge and build the desired skill set in the ML domain.
Stage 0: Defining the Business Question for the ML Model
As depicted in the ML lifecycle diagram, understanding the business question your ML model aims to address is crucial before embarking on any ML project. This step shapes your decision regarding the algorithms to use, a process known as framing the ML problem (classification, regression, etc.). Subsequent stages will revolve around this initial qualification.
Common business questions suitable for ML include:
- Can we identify damaged items on the production line? (Detection ML problem)
- Can we classify spam emails? (Classification ML problem)
- What are the expected sales figures for item X next quarter? (Forecast ML problem)
- How can we deliver personalized ads on an e-commerce platform? (Recommendation ML problem)
Conversely, if the inquiry pertains to historical events (e.g., sales volumes from the previous quarter), ML is unnecessary; these business intelligence questions can be answered through historical data analysis. Therefore, it’s critical to frame your business problem as an ML problem prior to initiating an ML project.
Stage 1: Data Engineering
Data engineering is vital for any ML project. As a proficient ML engineer, you must recognize the importance of data engineering in the success of your endeavors. This blog post emphasizes the significance of aligned human resources in managing such projects. For further insights, you can explore authoritative resources from SHRM on this topic. Additionally, for those interested in employee training and career skills, Fast Company offers excellent resources.