Why It Matters:
Spot instances (AWS Spot, GCP Preemptible VMs, Azure Spot) can be 70–90% cheaper than on-demand. They’re perfect for training jobs or batch tasks that can handle sudden interruptions.

Action Steps:

• Implement frequent checkpointing so you can resume training if the instance is terminated.
• Combine spot and on-demand instances: keep a small base of on-demand machines and scale up cheaper spot instances for extra capacity.
• Use container orchestration (e.g., Kubernetes with spot-instance node pools) to automatically manage preemptions and re-deploy workloads when a spot instance is lost.

Why It Matters:
Running GPU-intensive tasks on an overkill instance is costly, but so is using underpowered resources that prolong training. Right-sizing matches hardware capabilities to your actual workload needs.

Action Steps:

• Monitor resource utilization (CPU, GPU, memory, I/O) during training and inference.
• Experiment with different instance types/sizes to find the best cost-performance ratio.
• If your pipeline is mostly CPU-bound, switch to CPU-optimized instances; if it’s memory-bound, prioritize high-memory machines.

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Why It Matters:
Running GPU-intensive tasks on an overkill instance is costly, but so is using underpowered resources that prolong training. Right-sizing matches hardware capabilities to your actual workload needs.

Action Steps:

• Monitor resource utilization (CPU, GPU, memory, I/O) during training and inference.
• Experiment with different instance types/sizes to find the best cost-performance ratio.
• If your pipeline is mostly CPU-bound, switch to CPU-optimized instances; if it’s memory-bound, prioritize high-memory machines.

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Why It Matters:
Bigger isn’t always better. Techniques like pruning, quantization, knowledge distillation, and LoRA (Low-Rank Adaptation) can drastically reduce model size, memory usage, and inference time.

Action Steps:

• Distillation: Train a “student” model to mimic the outputs of a large “teacher” model for similar accuracy with fewer parameters.
• Quantization: Convert weights to lower precision (e.g., INT8) for smaller model size and faster inference.
• Pruning: Remove redundant weights or neurons to create a sparser network with near-identical performance.
• LoRA: Fine-tune only low-rank parameter matrices for large language models, slashing compute costs on each new task.

‍Why It Matters:
It’s tempting to pick the latest, biggest model (like a massive language model) even when a simpler architecture might suffice. That decision can blow up your costs and complexity.

Action Steps:

• Evaluate smaller or more efficient backbone architectures (e.g., MobileNet, EfficientNet, DistilBERT).
• Use transfer learning or pretrained models to avoid training from scratch.
• Start with baseline experiments using modest architectures before “scaling up.” Validate the performance and only then consider more complex (and costly) models.

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Why It Matters:
Manual or ad-hoc training jobs can run when nobody’s around to watch them—or worse, they can conflict with production workloads. Scheduling ensures you’re using off-peak times (when cloud spot prices might be lower) and also prevents resource contention.

Action Steps:

• Automate training pipelines with tools like Airflow, Prefect, or Dagster.
• Schedule jobs during off-peak hours to possibly get cheaper spot capacity.
• Avoid running large trainings during critical business hours if they share resources with production or you need immediate debugging support.

Why It Matters:
Poorly designed data pipelines can become bottlenecks, causing GPUs to sit idle waiting for data or forcing you to over-provision. Streamlining ETL (extract, transform, load) ensures maximum utilization with minimum cost.

Action Steps:

• Use parallel data loading and caching (e.g., TFRecord, RecordIO, or Parquet).
• Preprocess data once and cache results (e.g., in cloud storage or a data warehouse).
• Profile the end-to-end pipeline to ensure you aren’t limited by slow I/O or disorganized transformations.

Why It Matters:
Modern ML needs DevOps best practices—often called MLOps—to streamline deployments, reduce manual errors, and foster reproducibility. Containerizing your environment and using continuous integration/continuous deployment (CI/CD) can drastically cut costs from wasted runs, environment inconsistencies, and debugging time.

Action Steps:

• Containerize your ML applications (using Docker or similar) so developers and production environments run the same code/libraries.
• Set up CI/CD for ML to automate testing, linting, and partial training runs before merging code.
• Infrastructure as Code (IaC) with Terraform or CloudFormation ensures consistent, version-controlled infrastructure for each environment (dev, test, prod).

Why It Matters:
If you aren’t documenting your experiments, you risk repeating the same trial-and-error, blowing through compute budget. Also, ephemeral “merge request” environments help you test code and pipelines in isolation before merging to main, preventing expensive mistakes.

Action Steps:

• Use an experiment tracking tool (Weights & Biases, MLflow) to log hyperparameters, model versions, and metrics.
• Create “PR environments” that spin up automatically whenever a developer opens a pull/merge request. In these temporary testbeds, you can ensure new code integrates cleanly with your data pipelines and training scripts.

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