Machine learning problems new hires hit in the first 90 days
ML systems fail in more places than ordinary software: the data, the training code, the hardware, the model artefact and the serving layer. New joiners need to learn where each piece lives before they can debug it.
- Training: "CUDA out of memory", batch size and gradient accumulation, mixed precision, and loss that turns to NaN.
- Reproducibility: different results across runs, unpinned CUDA and library versions, and data loaders that shuffle differently.
- Pipelines: feature computation that differs between training and serving, and feature store point-in-time joins.
- Serving: model latency over budget, batching, ONNX or TorchScript export issues, and container images that do not match training environments.
- Monitoring: data and prediction drift alerts, and deciding whether to retrain or investigate upstream data.
- LLM work: retrieval quality in RAG systems, prompt and evaluation sets, token costs and latency.
Platforms and MLOps
Teams run on platforms such as SageMaker, Vertex AI, Azure ML, Databricks or Kubeflow, with MLflow or Weights and Biases for tracking and a model registry for promotion. Job Support helps you understand your team's path from experiment to production and debug failures at each step.
Design reviews ask why you chose an approach. Sessions help you prepare clear trade-offs on accuracy, latency, cost and maintainability.
What an ML support session looks like
Rahul joins an ML platform team at a Gurugram startup serving US customers. A fine-tuning job fails with CUDA out of memory on a larger dataset. In a Job Support session he reviews the redacted config and logs, enables gradient accumulation with a smaller per-device batch and mixed precision, and confirms throughput is acceptable before opening a pull request.
In the Zoom design review, the live transcript helps him follow questions about the change and answer with his own measurements.
Using ML job support responsibly
Models and training data are often among a company's most protected assets.
- Do not share proprietary model weights, training data or customer data.
- Never share cloud, GPU cluster, model registry or API keys.
- Follow your employer's policy on sharing ML code and prompts externally.
Machine learning job support pricing and how to start
Job Support costs $5 per hour, taxes included. Each hour includes 200 AI credits plus support, and hours and credits never expire. Pay by Stripe or bank transfer. Refunds apply only if the tool does not work.
Start on the free plan with 5 live Copilot minutes and 100 AI credits, no card needed. Install the desktop app on Windows or macOS.