I’ve been putting together a learning list to help me go deep on machine learning in 2023. After doing a few tutorials reading some docs, and watching a few videos, I jumped onto Pluralsight to see what they had. Given that I’ve been teaching at Pluralsight for more than a decade, I feel silly not checking there first! I bookmarked a few good-looking machine learning courses to watch later, but what really caught my eye was that Google Cloud folks were busy adding and updating tons of courses in the catalog over the past couple of months. Here are a handful of high-quality, educational courses that jumped out at me.
Data and AI/ML
- Innovating with Data and Google Cloud. Ninety-minute course that talks about the value of data, types of cloud data services, and what machine learning is about.
- Google Cloud Big Data and Machine Learning Fundamentals. This course is two-and-a-half hours long, and has many hands-on labs for streaming data processing, working with BigQuery ML, using pre-built and custom ML models, and working with Vertex AI.
- Applying Machine Learning to your Data with Google Cloud. Spend ninety minutes on this excellent overview into machine learning, pre-trained ML APIs, using BigQuery with datasets and ML models, and more. Lots of hands-on exercises here.
- Achieving Advanced Insights with BigQuery. This course is a bit under two hours, and you get to use some advanced SQL, learn about schema design, understand how to optimize BigQuery for performance, and go hands on with BigQuery and Vertex AI.
- Managing Machine Learning Projects with Google Cloud. This four-and-a-half hour course covers ML practices, building and evaluating ML models, identifying real-world use cases, and managing ML projects. It’s got some hands-on lab exercises as well.
- Building Batch Data Pipelines on Google Cloud. Important topics covered in this two-hour-and-twenty minute course. Watch this to learn about EL/ELT/ETL and cloud services like Dataproc, BigQuery, Dataflow, Data Fusion, and more.
- Building Resilient Streaming Analytics Systems on Google Cloud. Related topic to the previous item, but this one goes into stream processing with coverage of services like Pub/Sub, Dataflow, and more. Good material here.
- Essential Google Cloud Infrastructure: Core Services. This course is about three-and-a-half hours and covers topics like IAM, storage, databases, and operations. Looks like a very solid overview of the base infrastructure.
- Reliable Google Cloud Infrastructure: Design and Process. This is a four+ hour course that helps you architect and design a system that runs on Google Cloud. It’s not about building out the specific components, but rather, about going from requirements to architecture. it covers topics like microservices, automation, reliability, security, maintenance, and more.