Results for “jml”

Write, read, and validate HUML documents, converting between YAML/JSON/TOML and HUML for human-readable configuration files.

Run state-of-the-art machine learning models directly in JavaScript/TypeScript across browsers and server-side runtimes using Transformers.js.

Benchmark Jetson LLM/VLM serving performance across vLLM, llama.cpp, and Ollama with structured JSON output.

Recommends an inference runtime and memory-related launch flags for LLM/VLM workloads on NVIDIA Jetson devices, based on a live memory audit snapshot.

Switch the active Jetson target-platform pointer to an existing profile YAML. Use before customize/build/flash to change target; not for authoring profiles — use jetson-init-target instead.

Reduce per-token latency on Jetson vLLM servers by appending speculative decoding configuration, with guidance on when to enable and how to benchmark the improvement.

Serve LLMs and VLMs on NVIDIA Jetson devices using vLLM or SGLang with optimized Docker containers and quantization presets.

Create and modify NeMo AutoModel training and evaluation recipes, including YAML structure, builders, and execution flow.

Measure Jetson DRAM and NvMap usage, capture before/after baselines, and verify memory reclamation with live audit data.

Creates a new Jetson target-platform profile YAML by selecting a reference devkit and optional custom carrier, then updates the active target pointer.

Guides machine learning development with experiment tracking, hyperparameter optimization, model registry, and MLOps pipeline integration.

YAML configuration for CI/CD, Docker Compose, and Kubernetes.

Write QML unit tests with Qt Quick Test using TestCase, SignalSpy, and data-driven functions.

Adds the MariaDB JDBC driver to a build, configures connection URLs and datasources, and sets up TLS with the correct sslMode and certificate options.

Use when implementing ML functionality with production-grade patterns and safeguards.

Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks, including model serving, feature engineering, A/B testing, and monitoring.

Trains machine learning models on genomic interval data from BED files, including region embeddings, single-cell ATAC-seq analysis, and consensus peak building.

Jenkins CLI for controllers. Use when users need to manage jobs, pipelines, config.xml, runs, logs, artifacts, credentials, nodes, or queues in Jenkins. Triggers include "jenkins", "jk", "pipeline", "build", "job create", "job config", "config.xml", "run logs", "jenkins credentials", "jenkins node".

Turn model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.

Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.

Use when planning or sequencing an ICML manuscript workflow end to end - from topic selection and drafting through OpenReview submission, double-blind review, author response, camera-ready, PMLR publication, public original-submission release, and rerouting decisions. Use when you need the next ICML skill, the official page to reopen, or the blocking gap for the current stage.

Sets up the BMad ML module in a project. Use when the user requests to 'install ML module', 'configure BMad ML', or 'setup BMad ML'.

Generates OMG-compliant BPMN 2.0 XML and SVG diagrams from natural language process descriptions, with validation, automatic layout, and optional process optimization advisories.

Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring.

Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.

Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring.

Write idiomatic QML for Qt Quick applications with sound property design, bindings, and C++ integration.

Jenkins CLI for controllers. Use when users need to manage jobs, pipelines, config.xml, runs, logs, artifacts, credentials, nodes, or queues in Jenkins. Triggers include "jenkins", "jk", "pipeline", "build", "job create", "job config", "config.xml", "run logs", "jenkins credentials", "jenkins node".

Avoid common Ansible mistakes covering YAML syntax traps, variable precedence, idempotence failures, and handler gotchas.

Explains MariaDB Connector/J JDBC driver behavior: auto-registration, URL schemes, prepared statement handling, batch inserts, transactions, fetch sizes, pooling, and failover. Use when writing or reviewing Java code that talks to MariaDB with the mariadb-java-client driver.

Expert-level skill for Jira Query Language (JQL). Use when the user asks about writing, debugging, optimizing, or understanding JQL queries; needs to filter Jira issues by complex criteria, date ranges, history, or cross-project conditions; wants to build saved filters, dashboard gadgets, or automation rules; or needs guidance on JQL performance, functions, operators, history operators (WAS/CHANGED), relative dates, role-based query patterns, or the JQL REST API.

Turns model work into a production ML system with data contracts, reproducible training, quality gates, deployable artifacts, and monitoring.

Orchestrates AI/ML workflows for building LLM applications, RAG systems, AI agents, and ML pipelines, covering design, integration, observability, and security.

Choose between DML statements and Database class methods for bulk Salesforce operations, handling partial success, DMLOptions, and error collection.

Configure and manage Atlassian Jira projects, workflows, JQL queries, automation, and reporting using MCP tools and bundled scripts.