Overcoming 5 common roadblocks in the ML learning journey
According to forecast predictions from Forbes, the global machine learning (ML) market in 2017 was valued at $1.58B and is expected to reach $20.83B in 2024, growing at a compound annual growth rate (CAGR) of 44.06% between 2017 and 2024. The same report indicates there are 44,864 jobs in the US today (98,371 worldwide) that list ML as a required skill.
If you’ve thought about adding artificial intelligence (AI)/ML expertise to your cloud toolkit, there’s no better time! Whether you’ve dabbled in ML, you’re completely new, or you’re looking to solidify your expertise, there are a multitude of trainings, hands-on labs, and customer-use-case examples that you can take advantage of now to skill up. This blog outlines how to overcome five common roadblocks you may come across in your ML journey, along with resources from AWS Training and Certification.
1. I’m aware of the AI/ML basics, but how do I use them to solve business problems?
Often, developers know the basics of ML but aren’t sure how to put the right pieces together to tackle a specific business problem. For this, I turn to architecture examples from AWS Partners and customers from the show, This is My Architecture. These two-to-five-minute videos are designed to highlight architecture solutions developed by customers and Partners showing how cloud building blocks can be put together to solve a business problem. Whether it’s about retraining the models, monitoring the workflow, or developing a serverless ML architecture, you’ll find real-time and practical solutions across different industry verticals. I also highly recommend reading customer stories to discover how ML is creating better business results across nearly every industry in various customer segments—from large-scale enterprises to tiny start-ups and everything in between.
2. I want to advance my AI/ML skills but I’m not sure where to start. How do I find the right resources customized to my ML requirements?
AWS Training and Certification has AWS Ramp-Up Guides to help data scientists and developers build their knowledge of ML in the AWS Cloud. The AWS Ramp-Up Guide for Machine Learning features free digital training, classroom courses, videos, whitepapers, AWS Certification materials, and other information you can use. In Amazon Machine Learning University on YouTube, you can dive deep into the same curriculum we use to train our own developers and data scientists, for free.
3. I get lost in the data whenever I start implementing ML solutions. Is there an easier way?
Yes. A key step in the ML process is getting the data ready for modelling. Data scientists spend around 60-80% of their time integrating, cleansing, analyzing, and maintaining data sets from various sources. AWS offers the following free courses to help you including Machine Learning Terminology and Process and Tabular Data.
4. I don’t know where to practice my ML skills and put my knowledge to the test with use cases.
If you’re purely looking for an ML sandbox to get hands on with ML quickly and easily, look no further than Amazon SageMaker JumpStart. Amazon SageMaker JumpStart provides a set of solutions for the most common use cases that can be readily deployed with just a few clicks. You can accelerate your ML journey by using fully customizable solutions and referencing architectures and AWS Cloud templates. Amazon SageMaker JumpStart also supports one-click deployments and allows you to fine-tune more than 150 popular open-source models, such as natural language processing, object detection, and image-classification models. Explore use cases that include predictive maintenance, computer vision, autonomous driving, and more.
5. Do I need a PhD to get started on my AI/ML journey?
The short answer is “no.” At AWS, our mission is to take our rich experience and expertise with ML and put it in the hands of all organizations—every developer, data scientist, researcher, and engineer. We do this by offering the broadest and deepest set of AI/ML services. Along with innovations, we want to make you ML-ready by enabling your existing talent to become productive with these services. We offer both free, self-paced digital courses and hands-on classroom training from AWS Authorized Instructors, ranging from foundational to advanced.
- Demystifying AI/ML/DL (45 minutes)
For intermediate-level learners:
- Math for Machine Learning (8 hours)
- The Elements of Data Science (8 hours)
- The Machine Learning Pipeline on AWS (four-day course)
For experienced data scientists:
We also offer an industry-recognized credential, AWS Certified Machine Learning – Specialty, which validates your ability to design, implement, deploy, and maintain ML solutions for given business problems. You can access an exam guide, sample questions, and more to help you prepare for the AWS Certification exam.
I hope these resources and tips help you see how you can take the steps today to advance your ML skills and expertise. It’s never been easier—or more important—to gain ML experience. A world of exploration and innovation in the cloud is waiting.