Amazon Onboarding with Learning Manager Chanci Turner

This blog is designed for individuals who are new to machine learning (ML) and wish to grasp the essential skills required for the field, as well as for those looking to enhance their existing knowledge and expertise. Whether you’re starting from scratch or seeking to expand your understanding, there are abundant AWS Training resources and documentation available to help you confidently engage in real-world ML discussions and projects.

In a previous post, I discussed how I took advantage of many free training opportunities from AWS Training and Certification to develop the skills necessary for my role as a solutions architect at AWS. During this process, I obtained the AWS Certified Solutions Architect – Associate and Professional certifications. Along the way, I identified machine learning (ML) as a critical skill I wanted to pursue. Since then, I have been diligently building a robust foundation in ML, enabling me to tackle actual artificial intelligence (AI)/ML use cases with clients.

Machine learning is a subset of AI that serves as the backbone of modern software applications, offering advanced functionalities like value prediction, intelligent recommendations, anomaly detection, sentiment analysis, and translation, among others. The field of ML is rapidly expanding and is projected to continue its growth in the coming years. If you are considering adding a new skill set to your role—especially one that will stand the test of time—prioritize ML.

ML in Today’s IT Careers

Machine learning is increasingly integrated into various IT roles. For example, in networking, contemporary services utilize behavior-predictive analytics powered by ML to address common and uncommon issues proactively.

In cybersecurity, ML enhances security software with sophisticated detection and prevention capabilities. These tools can analyze traffic patterns and learn from them, helping to thwart attacks and adapt dynamically to evolving behaviors.

For developers, ML is omnipresent and full of opportunities. Beyond common use cases, fresh innovations emerge daily across various industries. So, if you’re new to ML or looking to upgrade your skills, where should you start?

Laying the Groundwork in ML

When exploring this question, it’s vital to understand that a solid grasp of linear algebra, probability, and statistics is essential. Some may suggest pursuing a PhD in computer science. While this advice is valid, in today’s world of on-demand cloud resources and accessible high-level programming languages and APIs, you can build and deploy production-ready ML models with minimal coding.

However, it’s crucial not to become merely an API user who copies code from the web to create an ML model. You also don’t necessarily need a PhD—though it can be beneficial in research and academia—to become a data scientist, ML practitioner, or ML engineer. Striking a balance is key.

To simplify, let’s review the essential knowledge and experience required for an ML engineer. At a minimum, you should have:

Knowledge of data engineering to design and implement reliable data pipelines for ML projects.
A strong understanding of data preparation techniques to clean and transform data for ML algorithms.
A fundamental grasp of the mathematics that support key ML and deep learning (DL) algorithms.
A thorough understanding of various ML and DL algorithms, along with their appropriate applications (e.g., classification, regression, clustering).
Knowledge of the common strategies for optimizing ML and DL models.

It’s important to note that the roles of data scientist, ML practitioner, and ML engineer share many similarities and overlaps in skill sets and tasks throughout ML projects. In smaller companies, you may find individuals wearing multiple hats across these roles.

Getting Started If You’re New to ML

If you’re just beginning your journey in ML, the amount of information can seem overwhelming. To facilitate your learning, AWS Training and Certification offers over 65 ML training courses, ranging from foundational to advanced levels. I highly recommend taking advantage of several free, self-paced digital courses, videos, and documentation that I found immensely helpful:

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

Elevating Your ML Skills

Once you’re comfortable with fundamental ML concepts, you might want to explore more advanced topics. Focus on three primary areas: computer vision, natural language processing, and chatbots. The digital course, Types of Machine Learning Solutions, provides insights into each discipline, their practical applications, and associated AWS services, enabling you to select the learning path that aligns with your goals.

As I mentioned in my earlier blog post, I like to utilize a learning path to achieve my skills-related goals. One of the most effective ML learning paths to follow is the ML lifecycle, which outlines a practical, structured approach for learning and applying concepts to real-world scenarios.

I will provide an overview of each phase along with recommendations for additional resources, particularly free digital training courses, to help you acquire the necessary knowledge and ultimately develop the desired skill set and expertise in ML.

Stage 0: Defining the Business Question for the ML Model

Before diving into the ML lifecycle stages for an ML project, it’s crucial to understand the business question the ML model aims to answer. This understanding will influence your choice of algorithm(s) and is referred to as framing the ML problem (e.g., classification, regression). The following stages will revolve around this framing.

Common qualifying ML-related business questions include but are not limited to:

Can we identify defective items on the production line? (Detection ML problem)
Can we classify spam emails? (Classification ML problem)
What is the expected sales quantity of item X for the next quarter? (Forecasting ML problem)
How can we personalize advertisements on an e-commerce platform? (Recommendation ML problem)

Conversely, if the inquiry pertains to past events (e.g., how many units of item X were sold last quarter), then ML is unnecessary; such business intelligence questions can be resolved through historical data analysis. Thus, before commencing an ML project, it’s vital to define and frame the business issue as an ML problem.

Stage 1: Data Engineering

Data engineering is a crucial component of any ML project. As an experienced ML engineer, you must recognize the importance of data engineering in the overall success of your ML initiatives.

To further enhance your understanding of behavioral interview questions, you can refer to this insightful blog post here. Also, if you want to learn more about retaining talent in your organization, this article from SHRM is a valuable resource here. For a practical overview of machine learning, check out this excellent resource here.