Build declarative AI Services with LangChain4j using interface-based patterns, annotations, memory management, tools integration, and advanced application patterns. Use when implementing type-safe AI-powered features with minimal boilerplate code in Java applications.
Configure ReinforceNow training runs with config.yml and train.jsonl. Use when setting up training configuration, choosing models, configuring RL algorithms, rollout settings, or training data format. Triggers on "config.yml", "train.jsonl", "training config", "batch_size", "group_size", "max_turns", "qlora".
Convert HuggingFace datasets to ReinforceNow format. Use when creating train.jsonl from HuggingFace datasets, formatting data for SFT or RL training, or writing reward functions for math datasets. Triggers on "HuggingFace", "dataset", "train.jsonl", "convert dataset", "math reward", "latex".
Use the ReinforceNow CLI for RLHF training. Use when running rnow commands, initializing projects, submitting training runs, testing rollouts, or downloading models. Triggers on "rnow", "rnow init", "rnow run", "rnow test", "rnow download", "rnow login", "training run".
Build and train neural networks with TensorFlow
Deploy and serve TensorFlow models
Use when designing, tuning, or auditing revenue forecast models.
Scoring system for opportunity hygiene, win likelihood, and inspection prioritization.
Use to design composite intent scoring models with decay, weighting, and governance.
Use to drive consistent forecast methodology, grading, and inspection cadences.
Deep expertise in ML/CV model selection, training pipelines, and inference architecture. Use when designing machine learning systems, computer vision pipelines, or AI-powered features.
Build collaborative and content-based recommendation engines for product recommendations, personalization, and improving user engagement
Reduce feature dimensionality using PCA, t-SNE, and feature selection for feature reduction, visualization, and computational efficiency
Build binary and multiclass classification models using logistic regression, decision trees, and ensemble methods for categorical prediction and classification
Analyze temporal data patterns including trends, seasonality, autocorrelation, and forecasting for time series decomposition, trend analysis, and forecasting models
Implement computer vision tasks including image classification, object detection, segmentation, and pose estimation using PyTorch and TensorFlow
Design and architect neural networks with various architectures including CNNs, RNNs, Transformers, and attention mechanisms using PyTorch and TensorFlow
Determine cause-and-effect relationships using propensity scoring, instrumental variables, and causal graphs for policy evaluation and treatment effects
