Developer Skills | ECOSYSTEM Research

AI, ML, Agentic Systems, Embedded AI & Systems Engineering — Developer Reference A developer-oriented reference to modern AI frameworks, model-training tools, agent harnesses, inference runtimes, MLOps systems, vector databases, embedded frameworks, GPU stacks, compilers, and adjacent systems technologies. How to use this guide: Each entry includes a short developer-focused summary and a primary documentation, project, or canonical reference URL. Technologies are grouped by practical engineering domain. --- 1. Core Programming Languages - Python — The dominant language for AI/ML research, data science, model training, orchestration, APIs, and scientific computing. URL: https://docs.python.org/3/ - C++ — High-performance systems language widely used for inference engines, runtimes, robotics, embedded systems, DSP, game engines, and AI kernels. URL: https://isocpp.org/ - C — Foundational systems language for firmware, operating systems, embedded runtimes, drivers, and low-level hardware interfaces. URL: https://www.iso.org/standard/82075.html - Rust — Memory-safe systems language increasingly used for AI infrastructure, inference runtimes, embedded systems, WebAssembly, networking, and high-performance services. URL: https://www.rust-lang.org/learn - TypeScript — Strongly typed JavaScript used for AI web applications, agent interfaces, frontend tooling, Node.js services, and SDK integrations. URL: https://www.typescriptlang.org/docs/ - JavaScript — Browser and server runtime language useful for AI interfaces, visualization, edge applications, and web-native inference. URL: https://developer.mozilla.org/en-US/docs/Web/JavaScript - Mojo — Systems/AI-oriented language from Modular designed to combine Python-like syntax with low-level performance and accelerator programming. URL: https://docs.modular.com/mojo/ - WebAssembly (Wasm) — Portable binary execution format for sandboxed high-performance code in browsers, servers, plugins, and edge environments. URL: https://webassembly.org/ --- 2. Core Numerical & Scientific Computing - NumPy — Fundamental Python array, linear algebra, vectorized math, random-number, and numerical-computing library. URL: https://numpy.org/doc/ - SciPy — Scientific-computing library covering optimization, signal processing, integration, statistics, sparse matrices, and scientific algorithms. URL: https://docs.scipy.org/doc/scipy/ - Pandas — DataFrame-based data analysis and preprocessing library used heavily in classical ML and dataset engineering. URL: https://pandas.pydata.org/docs/ - Polars — High-performance DataFrame library written in Rust with excellent support for large analytical workloads. URL: https://docs.pola.rs/ - Apache Arrow — Standardized in-memory columnar data format for fast interchange between analytics, ML, databases, and distributed systems. URL: https://arrow.apache.org/docs/ - Parquet — Columnar storage format optimized for analytical workloads and large ML datasets. URL: https://parquet.apache.org/ - DuckDB — Embedded analytical SQL database designed for fast local querying of structured data, Parquet, Arrow, and data-science workloads. URL: https://duckdb.org/docs/ - Matplotlib — Core Python plotting library for scientific, model-training, evaluation, and research visualization. URL: https://matplotlib.org/stable/ - Jupyter — Interactive notebook environment widely used for ML experimentation, education, research, and exploratory data analysis. URL: https://docs.jupyter.org/ --- 3. Deep Learning Frameworks - PyTorch — Flexible tensor and automatic-differentiation framework widely used for modern deep-learning research and production model development. URL: https://pytorch.org/docs/stable/ - TensorFlow — End-to-end ML platform for training, serving, deployment, distributed computation, and production workflows. URL: https://www.tensorflow.org/apidocs - Keras — High-level neural-network API supporting approachable model development across TensorFlow, JAX, and PyTorch backends. URL: https://keras.io/ - JAX — High-performance numerical computing and autodiff system with composable transformations such as jit, grad, vmap, and distributed execution. URL: https://docs.jax.dev/ - Flax — Neural-network library for JAX used for research-grade models and scalable accelerator training. URL: https://flax.readthedocs.io/ - Haiku — DeepMind neural-network library for JAX with a module abstraction inspired by object-oriented deep-learning frameworks. URL: https://dm-haiku.readthedocs.io/ - PyTorch Lightning — Structured training framework that abstracts distributed training, checkpoints, logging, and training loops around PyTorch. URL: https://lightning.ai/docs/pytorch/stable/ --- 4. Hugging Face Ecosystem - Transformers — Major library for pretrained language, vision, audio, and multimodal transformer models. URL: https://huggingface.co/docs/transformers/ - Datasets — Dataset loading, streaming, preprocessing, caching, and dataset-sharing framework for ML workflows. URL: http…