Machine Learning in the Cloud with AWS Batch
AWS Batch is reshaping how teams run large-scale ML workloads without managing infrastructure. This micro-course cuts through the noise, showing you exactly how to orchestrate batch jobs, optimise costs, and scale ML experiments on AWS—skills that separate capable engineers from cloud-native architects.
AIU.ac Verdict: Ideal for ML engineers and data scientists already comfortable with AWS fundamentals who need to operationalise batch workflows at scale. The 20-minute format is a strength for quick upskilling, but won’t cover advanced multi-region failover or cost optimisation strategies in depth.
What This Course Covers
You’ll explore AWS Batch architecture, job definitions, compute environments, and how to containerise ML workloads for reliable execution. The course walks through practical scenarios: submitting batch jobs, monitoring execution, and integrating Batch with other AWS services like S3 and CloudWatch—the real-world plumbing that makes ML pipelines production-ready.
Expect hands-on labs in Pluralsight’s sandbox environment where you’ll configure job queues, manage dependencies, and troubleshoot common failures. By the end, you’ll understand when Batch outperforms alternatives like SageMaker Processing or EC2, and how to architect cost-efficient ML workflows that scale from dozens to thousands of concurrent jobs.
Who Is This Course For?
Ideal for:
- ML engineers operationalising models: You’re moving beyond notebooks into production. Batch is your answer for scheduled retraining, bulk inference, and parameter sweeps without DevOps overhead.
- Data scientists with AWS exposure: You know EC2 and S3 basics. This course bridges the gap to orchestrating complex ML jobs without learning Kubernetes or Airflow.
- Cloud architects designing ML platforms: You’re building internal ML infrastructure. Understanding Batch’s job model, scaling behaviour, and cost structure is essential for platform decisions.
May not suit:
- AWS beginners: You’ll need solid IAM, VPC, and container fundamentals first. This assumes you’re comfortable with AWS console and CLI basics.
- Real-time ML inference seekers: Batch is for asynchronous workloads. If you’re building low-latency APIs, look at SageMaker Endpoints or Lambda instead.
Frequently Asked Questions
How long does Machine Learning in the Cloud with AWS Batch take?
20 minutes of video content. Plan 30–45 minutes total if you follow along with the hands-on labs in Pluralsight’s sandbox environment.
Do I need AWS certification or prior experience?
Not required, but you should be comfortable with AWS fundamentals: EC2, S3, IAM roles, and basic Docker concepts. If you’re new to AWS, start with foundational courses first.
Will this teach me to build ML models?
No. This course assumes you have models or scripts ready. It focuses on *running* them at scale via Batch, not training or developing them.
Can I use what I learn in production immediately?
Yes. The labs are in live AWS sandboxes, and the patterns taught are production-grade. You’ll be able to deploy Batch jobs the same day, though enterprise deployments may need additional governance layers.
Course by AWS on Pluralsight. Duration: 0h 20m. Last verified by AIU.ac: March 2026.
