What is Rocheston AINA?
AINA is Rocheston’s in-house, Linux-based AI operating system—cloud-hosted, GPU-accelerated, and 100% browser-native. Log in, build, and deploy without installing a single thing. It’s stunningly beautiful, colorful, and fast—and it’s exclusive to RCAI students. AINA OS is a cloud-hosted learning environment built by Rocheston for RCAI students.
Full blown OS
Step into the future of artificial intelligence. AINA OS isn’t a tool. It isn’t a lab. It is a full-blown operating system—built from the Linux kernel up, engineered purely for AI, and delivered entirely from the cloud. Open your browser, and an entire AI supercomputer opens with it.
AINA OS is a cloud-hosted learning environment built by Rocheston for RCAI students.
AINA OS — Built for RCAI Training
AINA OS is Rocheston’s cloud AI workspace used inside the Rocheston Certified AI Engineer (RCAI) program. It opens in your browser—no installs—and it’s updated every week so you always train on the latest. It’s not a product you can buy; access is exclusive to RCAI students.
Why it’s cutting-edge: AINA turns learning into doing. Hundreds of ready labs and starter apps are waiting. You open one, follow the guide, tweak the code, recompile, and deploy. Inside AINA you write Python, run scripts, compile code when needed, work with massive datasets, train models, and publish real AI applications. Snapshots and resets let you experiment safely, and the built-in AINA assistant helps with code and explanations so you keep moving.
Why it’s cutting-edge: AINA turns learning into doing. Hundreds of ready labs and starter apps are waiting. You open one, follow the guide, tweak the code, recompile, and deploy. Inside AINA you write Python, run scripts, compile code when needed, work with massive datasets, train models, and publish real AI applications. Snapshots and resets let you experiment safely, and the built-in AINA assistant helps with code and explanations so you keep moving.
You Can’t Just Download This
AINA cannot be downloaded, installed, or pirated. It is exclusively available to RCAI students in the cloud. Updated weekly with the latest AI models and tools, AINA keeps you permanently ahead. Others are stuck installing packages, debugging drivers, and wasting weeks—while you log in and start building.
Pre-wired with the tools we teach; updated weekly so labs stay current.
The Playground of AI
Train massive models on cloud GPUs with zero setup. Fine-tune LLMs in the morning, deploy bots in the afternoon. Build apps, copilots, and agents that actually run, not just in slides. Generate images, music, video, and animation—then chain them into complete products.
Exclusive to RCAI cohorts; not sold as a standalone product.
This is alive. It’s fun. It’s real engineering
AINA is your classroom, your lab, and your portfolio engine. Every RCAI module (00–41) is already wired inside AINA. Every lab, every project, every capstone—done inside reproducible environments that just work. In six months you’ll leave not just with theory, but with running endpoints, working agents, and real products you can demo to anyone.
AINA OS isn’t a commercial cloud service. It’s the training workspace used inside the Rocheston Certified AI Engineer program.
AI-Powered Cyberattack Simulation
HexStrike AI inside AINA OS turns offensive security into a three-step workflow. Launch Claude. Say: “AINA, launch web application attacks on [IP].” Watch it work. AINA plans, pivots, chains exploits, and documents everything automatically—no manual setup, no scripts to babysit. Autonomous. Intelligent. Relentless.
AINA executes multi-stage cyberattacks and returns auditor-ready logs. Say the command, monitor the run, export the findings.
Confidence, Mastery, Proof
AINA gives you something no courseware ever can: proof. By the end of your six months, you will have shipped AI apps, copilots, agents, and services. You’ll have a portfolio, demos, and APIs live in the cloud—evidence employers and clients cannot ignore. You don’t just learn about AI—you live it.
Build here, deploy anywhere. Push to Git, export models (ONNX/TF/Torch/GGUF), and hand off to your employer’s cloud.
The Invitation
This is where RCAI students stop learning about AI and start building it. Beautiful. Powerful. Exclusive. Updated weekly. AINA is not just your lab—it’s your runway. Log in today, and step into the future of artificial intelligence.
Beautiful interface, brutal performance—AINA.
Zero-Install, Browser-Native Access
AINA OS runs entirely from the cloud. Students sign in via Chrome, Edge, or Safari on Windows, macOS, Linux, or Chromebook and start building immediately—no drivers, no environment setup, no dependency conflicts. Your IDEs, notebooks, terminals, and dashboards open in seconds with secure, authenticated access.
Click. Create. Conquer. AINA.
Cloud-Grade GPU, RAM, and CPU Power
AINA OS provisions powerful GPU instances with ample VRAM, fast CPUs, and large memory pools tuned for deep learning. Mixed-precision training, accelerated inference, and large-batch experiments run smoothly. You focus on modeling; AINA scales the compute.
Classroom Mode gives instructors snapshots, resets, and lab orchestration so every student can ‘break things’ and learn safely.
Built on a Cutting-Edge Linux Kernel
AINA OS uses a modern, performance-tuned Linux kernel curated by Rocheston. File systems, drivers, and accelerators are configured for AI workloads, so compilers, CUDA/ROCm stacks, and BLAS libraries run at peak efficiency with exceptional stability.
AINA OS is for education. For admissions and curriculum, see the RCAI program page.
Launch JupyterLab, VS Code, MLflow, and dashboards instantly.
AINA ships with prewired launchers for JupyterLab, VS Code Server, TensorBoard, Gradio/Streamlit, and terminal sessions. Environments are version-locked and reproducible. Open a notebook, code in VS Code, visualize runs in TensorBoard, and track experiments in MLflow—with zero setup.
AINA: open a browser, open a frontier. Learn fast. Build faster. Join RCAI.
Framework Super-Stack (Everything Included)
PyTorch, TensorFlow, JAX/Flax, Keras, scikit-learn, RAPIDS, ONNX Runtime, OpenVINO, TensorRT, timm, Detectron2, Ultralytics YOLO, MMDetection, spaCy, NLTK, Gensim, Transformers, Sentence-Transformers—plus time-series, RL, graph, and AutoML stacks. AINA OS is your complete toolbox.
AINA turns “what if” into “watch this.”
Ask for code, explanations, and fixes inside AINA.
The AINA chatbot generates Python snippets, explains frameworks, drafts pipelines, and suggests hyperparameters. Ask “build a FastAPI service for this model,” “convert this to ONNX,” or “explain the difference between vLLM and llama.cpp,” and it guides you step by step.
Prototype by lunch, deploy by dinner—AINA.
Full Cloud Bridges: Azure, AWS, Google, NVIDIA
AINA includes official connectors and SDKs for Azure ML and Azure OpenAI, Amazon SageMaker and Bedrock, Google Vertex AI, and NVIDIA NGC/NIM. Authenticate once, move data, trigger training jobs, deploy endpoints, and bring managed services into your AINA workflows.
Your campus in the cloud, your lab is AINA.
RAG Studio with Vector Databases
FAISS, Qdrant, Milvus, Weaviate, Pinecone, Chroma—plus PDF loaders, chunkers, embeddings, and evaluators—are preconfigured. Spin up a private knowledge-base chatbot, add citations, benchmark with eval sets, and ship a secure RAG app in hours.
AINA: less setup, more breakthroughs.
MLOps Built-In (Track, Version, Govern)
MLflow, Weights & Biases, ClearML, and Neptune integrate with DVC + Git LFS so your datasets, code, and models are versioned. Evidently, Deepchecks, and Great Expectations monitor data drift and quality. Prometheus and Grafana illuminate performance in production.
Make models. Make apps. Make noise—with AINA.
Data Engineering Engine
Pandas, Polars, DuckDB, Spark, and Dask accelerate ETL. Connect to S3, GCS, Azure Blob, Postgres, BigQuery, Redshift, Snowflake, and MinIO. Validate schemas, create feature stores, and feed models with efficient parquet pipelines.
AINA makes artificial intelligence feel inevitable.
Multi-Modal Creation Studio
AINA OS provides templates for diffusion models, TTS/ASR, video and image generation, and prompt workflows. Produce assets for apps, marketing, education, and prototyping. Chain outputs together—e.g., script → narration → images → video.
Build agents that work, with AINA behind them.
LLM Studio and Optimization
Use PEFT/LoRA, QLoRA, and parameter-efficient techniques. Quantize with bitsandbytes or GPU-optimized runtimes. Serve with vLLM, Text Generation Inference, Triton, or ONNX Runtime. Export to GGUF, TensorRT, or OpenVINO for edge and latency-sensitive deployments.
AINA: beauty in the UI, power in the kernel.
Enterprise-Grade Serving and APIs
Ship FastAPI or gRPC endpoints, wrap models with BentoML, KServe, Seldon, TorchServe, or Triton, and publish internal or public APIs. CI/CD templates get you from prototype to reliable service with health checks and autoscaling.
When it has to run today, run it on AINA.
Security and Compliance by Design
Signed base images, SBOM generation, and vulnerability scans (e.g., Trivy/Grype) protect your supply chain. Secrets are stored safely. Role-based access and private workspaces keep experiments isolated while enabling controlled collaboration.
Dream in color, build in AINA.
Classroom Lab Mode for RCAI
AINA maps directly to the RCAI syllabus (Modules 00–41). Instructors can pin lab environments, publish datasets, and freeze versions so every student gets the same, working setup. Students complete labs, quizzes, and capstones without wrestling with local installs.
AINA: where code becomes capability.
Snapshots, Checkpoints, and Reset
Create restore points before big changes, checkpoint training jobs, and reset to a clean state with one click. AINA keeps experiments reproducible and recoverable, which encourages exploration and faster learning.
RCAI confidence, AINA acceleration.
Agent, Bot, and App Builder
Starter kits for chatbots, function-calling agents, and tool-use workflows help you connect LLMs to your own tools, vectors, and data. Add retrieval, chain steps, schedule runs, and deploy as web apps or APIs.
AINA keeps your artificial intelligence current—weekly upgrades, daily wins.
Performance-Tuned for Speed
Mixed precision, fused kernels, batched decoding, and optimized data pipelines let you iterate rapidly. Notebook hot-reload and compiled extensions reduce turnaround time. AINA’s tuned kernel and libraries make the hardware sing.
Big ideas deserve big GPUs—AINA delivers.
Collaboration and Sharing
Share notebooks, pin environments, and co-edit code in VS Code Server. Export MLflow runs for peer review. Publish demo links for stakeholders. Keep private data private while enabling safe team workflows.
One platform, every breakthrough—AINA.
6-Month RCAI Access with Real Results
RCAI students receive six months of AINA OS access—ample time to finish every lab, build multiple capstones, and develop a real portfolio. By the end, you will have shipped working apps, bots, agents, and services you can demo to employers or clients.
AINA: ship the thing. Then ship the next one.
Every module runs inside AINA OS.
The RCAI curriculum aligns directly with AINA—from Module 00 (Welcome) through Module 41 (Microsoft Machine Learning Tools). You’ll learn Python (05), Math (06), ML (07), Model Training & Deployment (08), Kaggle (09), Tech and Frameworks (10–11), CV and Detection (12), OpenAI/ChatGPT (13), Cybersecurity AI (14), LLMs (15), Labs (16), Generative Art (17), Face Recognition (18), Deep Learning (19), Data Science (20), Azure/Vertex/SageMaker (21–23), and the broad Applications tracks (24–37), plus Deep Technical Lectures (38), Big Data (39), Classroom Project (40), and Microsoft tooling (41)—all inside AINA.
Screenshots
List of Tools in Rocheston AINA
WORKBENCHES & IDES:
JupyterLab, VS Code Server, RStudio Server (opt), Terminal (bash/zsh/fish), TensorBoard, MLflow UI, Gradio, Streamlit, Dash, FastAPI playground, OpenAPI/Swagger UIs, Postman, Bruno (opt)
PROGRAMMING LANGUAGES & RUNTIMES:
Python 3.x, Node.js, TypeScript, R (opt), Java (JDK), Go (opt), Julia (opt), C/C++, CUDA toolkits, ROCm (where available), GCC/Clang, CMake, Ninja
PACKAGE & BUILD TOOLING:
conda, mamba, pip, uv, Poetry, virtualenv, pip-tools, build, twine, pre-commit, black, isort, ruff, flake8, mypy, pytest, tox, coverage, git, git-lfs
FILES, STORAGE, WAREHOUSES, QUEUES:
Amazon S3 (API), Google Cloud Storage (API), Azure Blob (API), MinIO, HDFS (opt), WebDAV
PostgreSQL, MySQL/MariaDB, SQLite, MongoDB, Cassandra, Redis, Elasticsearch, OpenSearch, ClickHouse, Snowflake (API), BigQuery (API), Redshift (API), Databricks (API), Trino/Presto (API)
Kafka, Pulsar, RabbitMQ, NATS
Parquet, Arrow, ORC, Avro, JSON/NDJSON, CSV, Feather, Delta Lake (connector), Iceberg (connector), Hudi (connector)
DATA ENGINEERING / ETL / ORCHESTRATION:
Pandas, Polars, PyArrow, DuckDB, Dask, Apache Spark, Ray Data, dbt (core), Airflow, Prefect, Dagster, Flyte, Great Expectations, Soda Core, Airbyte (connectors), Singer taps, Apache NiFi (opt), Pandera
CORE ML/DL FRAMEWORKS:
PyTorch, TensorFlow, Keras, JAX/Flax, scikit-learn, RAPIDS (cuDF, cuML), XGBoost, LightGBM, CatBoost, ONNX, ONNX Runtime, OpenVINO, TensorRT, TVM, TorchMetrics, timm
HPO / AUTOTUNE / AUTOML:
Optuna, Ray Tune, Hyperopt, Ax, SMAC3/BOHB, KerasTuner, Nevergrad, AutoGluon, H2O AutoML, auto-sklearn, TPOT
COMPUTER VISION:
OpenCV, torchvision, Albumentations, imgaug, Ultralytics YOLO (v5/v8), YOLOX, Detectron2, MMDetection, MMSegmentation, DETR, Segment Anything (SAM), GroundingDINO, OpenMMLab stack, MiDaS (depth), ESRGAN (super-res), EasyOCR, PaddleOCR, Tesseract
NLP / LLM TOOLING:
Hugging Face Transformers, tokenizers, datasets, accelerate, PEFT/LoRA/QLoRA, SentenceTransformers, spaCy, NLTK, Gensim, Stanza, Flair (opt), fastText, OpenNMT, SentencePiece
LLM ORCHESTRATION & GUARDRAILS:
LangChain, LlamaIndex, Haystack, DSPy, Guidance, Instructor, semantic-kernel (opt), NeMo Guardrails, Guardrails-AI, Rebuff, Llama Guard, LangFuse (API), TruLens (API), Arize Phoenix (API), LangSmith (API)
LLM SERVING & OPTIMIZED BACKENDS:
vLLM, Text Generation Inference (TGI), llama.cpp, ggml/gguf, Ollama, FasterTransformer (opt)
RAG & VECTOR DATABASES:
FAISS, Annoy, HNSWlib, Chroma, Qdrant, Milvus, Weaviate, Pinecone (API), Redis (vector), Elasticsearch/OpenSearch (kNN), Vespa, pgvector, LanceDB
DOC INGEST, OCR & EVAL (RAG PIPELINE):
unstructured, pypdf, pdfplumber, Apache Tika, textract, Tesseract, PaddleOCR, Ragas, DeepEval (opt)
EMBEDDINGS (TEMPLATES/APIs):
all-MiniLM-L6-v2, bge-large/bge-small family, E5-Large, GTE-Large, MPNet variants, OpenAI text-embedding-3 (API), Cohere embed (API), Voyage (API)
GENERATIVE AI — IMAGE/TEXT/AUDIO/VIDEO:
Diffusers, Stable Diffusion 1.5/2/XL, ControlNet, IP-Adapter, LoRA/PEFT training, DreamBooth, Textual Inversion, ComfyUI (opt), Automatic1111 (connector)
Multimodal: CLIP, BLIP/BLIP-2, LLaVA (templates)
Audio/Music: torchaudio, librosa, Demucs, Bark, MusicGen, Riffusion
Speech: OpenAI Whisper & faster-whisper, wav2vec2, Vosk, SpeechBrain, Coqui TTS
TIME SERIES & FORECASTING:
Prophet, statsmodels, darts, GluonTS, Kats, Orbit, Nixtla (NeuralForecast, StatsForecast)
REINFORCEMENT LEARNING & SIMULATION:
Stable-Baselines3, RLlib, CleanRL, Gymnasium, PettingZoo, Brax (JAX), Isaac Gym (where licensed)
GRAPH ML:
NetworkX, PyTorch Geometric (PyG), Deep Graph Library (DGL), StellarGraph (opt), Neo4j driver (connector)
SERVING / API / DEPLOYMENT:
FastAPI, Flask, gRPC, Uvicorn/Gunicorn, TorchServe, Triton Inference Server, KServe, Seldon Core, BentoML, ONNX Runtime Server, Ray Serve, NVIDIA TensorRT-LLM, Celery, RQ
MLOPS / TRACKING / OBSERVABILITY:
MLflow, Weights & Biases, ClearML, Neptune, Aim, DVC + Git LFS, MLflow Model Registry, W\&B Artifacts Data/Model monitoring: Evidently AI, Great Expectations, Deepchecks, WhyLabs (API)
Ops/metrics/logs: Prometheus, Grafana, OpenTelemetry (opt), Loki/Elastic (opt)
CLOUD SERVICES & SDKs (CONNECTORS):
AWS CLI, boto3, Amazon SageMaker SDK, Amazon Bedrock SDK, S3/STS, Redshift (API), EMR connectors. Azure CLI, Azure ML/AI SDK, Azure OpenAI, Cognitive Services, Azure Storage
gcloud CLI, google-cloud-python, Vertex AI SDK, BigQuery, GCS. NVIDIA NGC CLI, NIM microservices (where licensed). Hugging Face Hub, OpenAI, Anthropic, Cohere, Mistral, Together, Replicate, Fireworks (APIs), Databricks (API), Snowflake (API), IBM watsonx (API)
WAREHOUSES / DATABASES
Snowflake, BigQuery, Redshift, Databricks, PostgreSQL, MySQL/MariaDB, SQLite, MongoDB, Cassandra, ClickHouse, Elastic, OpenSearch, Redis
SECURITY, SUPPLY CHAIN & SECRETS:
Trivy, Grype, Syft (SBOM), Cosign (opt), Bandit, Safety, pip-audit, HashiCorp Vault, AWS Secrets Manager (API), Azure Key Vault (API), GCP Secret Manager (API)
CONTAINERS / DEVOPS / KUBERNETES:
Docker, Podman, Kubernetes, kubectl, Helm, Kustomize, Docker Compose, Kind (local K8s demos), Argo Workflows (opt), Kubeflow (opt), Make, GNU Parallel, GitHub Actions/GitLab CI templates
LABELING & DATA CURATION:
Label Studio, CVAT, doccano, Prodigy (API/licensed), Roboflow (API), FiftyOne
VISUALIZATION & BI:
Matplotlib, Seaborn, Plotly, Altair, Bokeh, HoloViews, Apache Superset, Metabase, Grafana, Kibana (opt)
MEDIA & GEOSPATIAL UTILITIES:
FFmpeg, ImageMagick, exiftool, GeoPandas, Shapely, Fiona, Rasterio, Folium, kepler.gl (via notebooks)
DATASETS — LOCAL SAMPLES + OFFICIAL CONNECTORS:
Vision: MNIST, Fashion-MNIST, CIFAR-10/100, STL-10, Caltech-101/256 (scripts), COCO, Pascal VOC, OpenImages, ADE20K, Cityscapes, KITTI, WIDER FACE, LFW, VGGFace2 (license-aware), ImageNet (connector) NLP: WikiText-103, SQuAD, GLUE/SuperGLUE, IMDB, AG News, Yelp, Quora Duplicate, SNLI/MultiNLI, C4, The Pile (subsets), Wikipedia snapshots, Common Crawl pipelines (scripts) Audio/Speech: LibriSpeech, Common Voice, VoxCeleb, ESC-50, UrbanSound8K
Video/Multimodal: Kinetics, UCF101, HMDB51, MSRVTT, WebVid-2M (connector) Embeddings/Eval: MTEB tasks (connector), LAMBADA, BIG-bench (connector) Kaggle: kaggle CLI wired for datasets/competitions (TOS), Hugging Face Datasets (connectors)
EDGE / MOBILE / ON-DEVICE:
ONNX Runtime, TensorRT, OpenVINO, TFLite, Core ML Tools, Jetson toolchains, GGUF quantized LLMs, MediaPipe (opt)
COPILOTS & ASSISTANTS:
AINA Chatbot (built-in), OpenAI Assistants (API), Claude (API), Cohere (API), Mistral (API), LangGraph templates, Function-calling/Tools templates
CLASSROOM & COLLABORATION:
Shared notebooks/projects, environment pinning, read-only lab baselines, cohort workspaces, artifact storage, exportable MLflow runs, demo links
UTILITIES & SYSTEM TOOLS:
tmux, htop/nvtop, wget, curl, httpie, ripgrep, fd, jq, yq, rsync, rclone, cron, ssh, ssm (connectors), make, unzip/7z, tree
EXPLAINABILITY & RESPONSIBLE AI (XAI):
SHAP, LIME, Captum, ELI5, InterpretML, Alibi Detect, Fairlearn, AIF360, What-If Tool, DiCE, Responsible AI Toolbox
PRIVACY, FEDERATED & SECURE ML:
Opacus (PyTorch DP), TensorFlow Privacy, PySyft, Flower (FL), TensorFlow Federated, NVIDIA FLARE (API/connector), Crypten, TenSEAL (FHE), Pyfhel (FHE), OpenDP SmartNoise, Presidio (PII), AnonymizeDF, ARX (connector)
FEATURE STORES:
Feast, Vertex Feature Store (API), SageMaker Feature Store (API), Databricks Feature Store (API), Hopsworks (API), Tecton (API)
AGENT FRAMEWORKS & AUTOMATION:
LangGraph, CrewAI, AutoGen (Microsoft), Semantic Kernel, Haystack Agents, SuperAGI (opt)
PROMPT EVAL, RED-TEAMING & GUARDRAILS:
promptfoo, garak, Microsoft PyRIT, NeMo Guardrails, Llama Guard, Rebuff, OpenAI Evals (API), lm-eval-harness (EleutherAI), Langfuse (API), TruLens (API), Phoenix (API), Lakera Guard (API)
MODEL COMPRESSION, PRUNING & DISTILLATION:
Intel Neural Compressor, OpenVINO NNCF, torch-pruning, nn-pruning, knowledge-distillation templates (DPO/ORPO/LoRA/QLoRA), DeepSpeed-Chat, TRL (HF)
DISTRIBUTED & HPC EXTRAS:
Horovod, FSDP (PyTorch), Megatron-LM (templates), Ray Train, Lightning Fabric/Strategy, NCCL tools, Nsight Systems/Compute (env dependent)
SCIENTIFIC & MATH STACK (CORE DS):
NumPy, SciPy, SymPy, Numba, CuPy, Modin (pandas at scale), Joblib, Multiprocessing, Polars (already listed)
SCRAPING, CRAWLING & DOC INGEST EXTRAS:
Scrapy, BeautifulSoup4, lxml, trafilatura, newspaper3k, goose3, Readability, Playwright, Selenium, pyppeteer/puppeteer
NOTEBOOK ECOSYSTEM & AUTOMATION:
IPyWidgets, Jupytext, nbconvert, papermill, Voilà, Panel, Markdown/Quarto (opt)
REPORTING & DOCS GENERATION:
Pandoc, ReportLab, WeasyPrint, wkhtmltopdf (connector), mkdocs, Sphinx, Mermaid (diagrams)
EDA & DATA PROFILING:
ydata-profiling (pandas-profiling), Sweetviz, Lux (opt), Dataprep EDA (opt)
DATA QUALITY, LINEAGE & CATALOG:
Great Expectations (already), Soda Core (already), Deequ (Spark), OpenLineage (connector), Marquez (connector), DataHub (connector), Amundsen (connector)
GEOSPATIAL & MAPS (EXTRA):
GDAL/OGR, PROJ, PostGIS (connector), OSMnx, Folium (already), kepler.gl (notebooks), pydeck
RECOMMENDER SYSTEMS (EXTRA):
LightFM, Spotlight, TensorRec, NVIDIA Merlin (API/connector), Cornac, implicit (already)
SEARCH & LIGHTWEIGHT INDEXES:
Meilisearch, Typesense, Vespa (already), Elasticsearch/OpenSearch (already)
SCHEDULING & EVENTING (APP LEVEL):
APScheduler, Celery + Flower, RQ, Temporal (connector), CRON templates
LOGGING, TRACING & ERROR MONITORING:
Sentry SDK (API), OpenTelemetry (already), structlog, loguru, ML debugger: debugpy, cProfile/py-spy/line-profiler/memory-profiler, torch.profiler
BROWSER/UI TEST & LOAD:
Cypress (connector), k6 (connector), Locust (opt), Lighthouse CI (connector)
DATA ANON/SYNTHETIC DATA:
Faker, SDV (Synthetic Data Vault), ydata-synthetic, Gretel (API/connector), synthcity (opt)
MEDIA & FILE TOOLING:
pdfminer.six, pikepdf, PyMuPDF, ExifRead, Pillow/PIL, Wand (ImageMagick bindings), ffmpeg-python
BIG DATA & STREAMING:
Apache Flink (connector), Spark Structured Streaming, Kafka Streams clients, Delta Lake/Iceberg/Hudi connectors (already listed)
UI FRAMEWORKS (PYTHON APP FRONTS):
Shiny for Python (opt), Flet (opt), NiceGUI (opt), Textual/Rich (TUI)
GRAPH DATABASES (CONNECTORS):
Neo4j (py2neo/official driver), TigerGraph (connector), ArangoDB (connector)
DATA VALIDATION (SCHEMA & TYPES):
Pandera, Pydantic, Cerberus, Voluptuous
MOBILE & EDGE (EXTRA):
NCNN (opt), MNN (opt), Core ML Tools (already), MediaPipe (already), TFLite Micro (opt)
SEARCH/APIS FOR LLM CONTENT SAFETY:
OpenAI Moderation (API), AWS Comprehend (API), Google Perspective (API), Azure Content Safety (API)
BENCHMARKS & METRICS (LLM & CLASSICAL):
HF Evaluate, scikit-learn metrics suite, torchmetrics (already), MTEB (connector), GLUE/SuperGLUE (already)
KAGGLE & HF OPS (EXTRA):
kaggle CLI (already), HF Hub CLI (already), Datasets lfs cache & snapshot scripts, Spaces deployment templates (connector)
INFRA AS CODE (OPTIONAL FOR DEVOPS LABS):
Terraform (opt), Pulumi (opt), Ansible (opt)
TEAM/PRODUCTIVITY UTILITIES:
make, just (task runner) (opt), tmux (already), ripgrep/fd (already), jq/yq (already), direnv (opt)
RCAI MODULE ALIGNMENT (RUNS INSIDE AINA):
Modules 00–41: Python Programming (05), Math Foundations (06), Machine Learning (07), Train/Deploy (08), Kaggle Datasets (09), AI Frameworks (11), Object Detection (12), OpenAI & ChatGPT (13), AI in Cybersecurity (14), LLMs (15), Labs (16), Generative Art (17), Facial Recognition (18), Deep Learning Essentials (19), Data Science (20), Azure AI (21), Google Vertex AI (22), Amazon SageMaker (23), Applications (24–37), Deep Learning Tech Lectures (38), Big Data (39), Classroom Project (40), Microsoft ML Tools (41) Modules 13 (OpenAI/ChatGPT), 15 (LLMs), 16 (AI Labs), 24–37 (Applications: personal, sales, coding, images, audio, video), 30 (Personal Chatbots), plus cloud modules 21–23 (Azure, Vertex, SageMaker) for hosted agents.
AGENTIC FRAMEWORKS (CORE)
LangGraph, CrewAI, AutoGen (Microsoft), Semantic Kernel, Haystack Agents, Transformers Agents, LlamaIndex Agents, LangChain Agents, SuperAGI (opt)
ORCHESTRATION & STATE MACHINES
Graph/ DAG agents (LangGraph), router & tool-calling agents (LangChain/LlamaIndex), conversational planners, multi-agent “crews”, role & tool routing, function-schema tooling (JSON Schema/OpenAPI tool specs)
TOOL-USE / FUNCTION BRIDGES
OpenAI Assistants (API), Anthropic Tools (API), Mistral Tools (API), Cohere Tools (API), function-calling wrappers for FastAPI/HTTP, Python REPL tool, Shell (sandboxed), file I/O tools, vector-search tools, SQL tools
PLANNING & REASONING PATTERNS
ReAct, Reflexion, Tree-of-Thought (ToT), Graph-of-Thought, MRKL, Toolformer-style patterns, routing & fallback strategies, self-consistency, debate/consensus agents
MEMORY & KNOWLEDGE
FAISS, Qdrant, Milvus, Weaviate, Chroma, Redis (vector), pgvector; document loaders (unstructured, pypdf, pdfplumber, Tika, OCR) and long-term memory stores via LlamaIndex/LangChain
WEB BROWSING / AUTOMATION
Playwright, Selenium, browser-use tools, requests/HTTP clients, search connectors (Tavily (API), SerpAPI (API), Wikipedia), scraping (Scrapy, BeautifulSoup, trafilatura, newspaper3k)
EXECUTION SANDBOXES
Python notebook kernel, Python REPL tool, code-execution cells, task runners, safe temp workspaces, FFmpeg/ImageMagick utilities for media tasks
EVAL, TELEMETRY & GUARDRAILS
promptfoo, garak, Microsoft PyRIT, NeMo Guardrails, Llama Guard, Rebuff, TruLens (API), Langfuse (API), Phoenix (API), OpenTelemetry, policy prompts, allow/deny lists, PII scrubbing (Presidio), jailbreak/TOFU checks
WORKFLOWS & SCHEDULING
Airflow, Prefect, Dagster, APScheduler; event triggers to run agents on schedules or data arrivals
PREBUILT AINA AGENT TEMPLATES
Research & Cite Agent (web browse + sources). PDF/Data Analyst Agent (RAG over docs). Dev/Ops Remediation Agent (log triage → fix suggestion). Support Triage Agent (classification → reply draft). ETL Orchestrator Agent (ingest → clean → vectorize → index). Creative Studio Agent (prompt → image/music/video pipelines)
DATASETS — LOCAL SAMPLES + OFFICIAL CONNECTORS:
Hugging Face Datasets (datasets library) with streaming, map/filter, parquet/arrow caching; MTEB & GLUE/SuperGLUE via HF; Wikipedia/Common Crawl pipelines (scripts); Kaggle CLI (TOS)
LLM SERVING & OPTIMIZED BACKENDS:
vLLM, TGI (Hugging Face Text Generation Inference), llama.cpp/gguf, Ollama; Optimum Runtime integrations (ONNX Runtime, OpenVINO, TensorRT)
PHISHING (emails / URLs / webpages)
Phishing Email Dataset (Kaggle) — cleaned corpus of phishing vs legitimate emails; great for NLP classification, intent/IOC extraction, and transformer-based models. Load via Kaggle CLI or Hugging Face mirrors.
RANSOMWARE / MALWARE (behavioral, static, telemetry)
Ransomware PE Feature Sets (Kaggle) — static PE-file feature vectors (headers, imports, entropy, strings) for safe ML without executing binaries. Good for rapid prototyping of static-analysis classifiers. Network & Host Telemetry Collections (CIC-IDS / custom corpora) — includes benign vs malicious network flows and host logs you can use to simulate ransomware lateral movement or exfil patterns.
JupyterLab, VS Code Server, RStudio Server (opt), Terminal (bash/zsh/fish), TensorBoard, MLflow UI, Gradio, Streamlit, Dash, FastAPI playground, OpenAPI/Swagger UIs, Postman, Bruno (opt)
PROGRAMMING LANGUAGES & RUNTIMES:
Python 3.x, Node.js, TypeScript, R (opt), Java (JDK), Go (opt), Julia (opt), C/C++, CUDA toolkits, ROCm (where available), GCC/Clang, CMake, Ninja
PACKAGE & BUILD TOOLING:
conda, mamba, pip, uv, Poetry, virtualenv, pip-tools, build, twine, pre-commit, black, isort, ruff, flake8, mypy, pytest, tox, coverage, git, git-lfs
FILES, STORAGE, WAREHOUSES, QUEUES:
Amazon S3 (API), Google Cloud Storage (API), Azure Blob (API), MinIO, HDFS (opt), WebDAV
PostgreSQL, MySQL/MariaDB, SQLite, MongoDB, Cassandra, Redis, Elasticsearch, OpenSearch, ClickHouse, Snowflake (API), BigQuery (API), Redshift (API), Databricks (API), Trino/Presto (API)
Kafka, Pulsar, RabbitMQ, NATS
Parquet, Arrow, ORC, Avro, JSON/NDJSON, CSV, Feather, Delta Lake (connector), Iceberg (connector), Hudi (connector)
DATA ENGINEERING / ETL / ORCHESTRATION:
Pandas, Polars, PyArrow, DuckDB, Dask, Apache Spark, Ray Data, dbt (core), Airflow, Prefect, Dagster, Flyte, Great Expectations, Soda Core, Airbyte (connectors), Singer taps, Apache NiFi (opt), Pandera
CORE ML/DL FRAMEWORKS:
PyTorch, TensorFlow, Keras, JAX/Flax, scikit-learn, RAPIDS (cuDF, cuML), XGBoost, LightGBM, CatBoost, ONNX, ONNX Runtime, OpenVINO, TensorRT, TVM, TorchMetrics, timm
HPO / AUTOTUNE / AUTOML:
Optuna, Ray Tune, Hyperopt, Ax, SMAC3/BOHB, KerasTuner, Nevergrad, AutoGluon, H2O AutoML, auto-sklearn, TPOT
COMPUTER VISION:
OpenCV, torchvision, Albumentations, imgaug, Ultralytics YOLO (v5/v8), YOLOX, Detectron2, MMDetection, MMSegmentation, DETR, Segment Anything (SAM), GroundingDINO, OpenMMLab stack, MiDaS (depth), ESRGAN (super-res), EasyOCR, PaddleOCR, Tesseract
NLP / LLM TOOLING:
Hugging Face Transformers, tokenizers, datasets, accelerate, PEFT/LoRA/QLoRA, SentenceTransformers, spaCy, NLTK, Gensim, Stanza, Flair (opt), fastText, OpenNMT, SentencePiece
LLM ORCHESTRATION & GUARDRAILS:
LangChain, LlamaIndex, Haystack, DSPy, Guidance, Instructor, semantic-kernel (opt), NeMo Guardrails, Guardrails-AI, Rebuff, Llama Guard, LangFuse (API), TruLens (API), Arize Phoenix (API), LangSmith (API)
LLM SERVING & OPTIMIZED BACKENDS:
vLLM, Text Generation Inference (TGI), llama.cpp, ggml/gguf, Ollama, FasterTransformer (opt)
RAG & VECTOR DATABASES:
FAISS, Annoy, HNSWlib, Chroma, Qdrant, Milvus, Weaviate, Pinecone (API), Redis (vector), Elasticsearch/OpenSearch (kNN), Vespa, pgvector, LanceDB
DOC INGEST, OCR & EVAL (RAG PIPELINE):
unstructured, pypdf, pdfplumber, Apache Tika, textract, Tesseract, PaddleOCR, Ragas, DeepEval (opt)
EMBEDDINGS (TEMPLATES/APIs):
all-MiniLM-L6-v2, bge-large/bge-small family, E5-Large, GTE-Large, MPNet variants, OpenAI text-embedding-3 (API), Cohere embed (API), Voyage (API)
GENERATIVE AI — IMAGE/TEXT/AUDIO/VIDEO:
Diffusers, Stable Diffusion 1.5/2/XL, ControlNet, IP-Adapter, LoRA/PEFT training, DreamBooth, Textual Inversion, ComfyUI (opt), Automatic1111 (connector)
Multimodal: CLIP, BLIP/BLIP-2, LLaVA (templates)
Audio/Music: torchaudio, librosa, Demucs, Bark, MusicGen, Riffusion
Speech: OpenAI Whisper & faster-whisper, wav2vec2, Vosk, SpeechBrain, Coqui TTS
TIME SERIES & FORECASTING:
Prophet, statsmodels, darts, GluonTS, Kats, Orbit, Nixtla (NeuralForecast, StatsForecast)
REINFORCEMENT LEARNING & SIMULATION:
Stable-Baselines3, RLlib, CleanRL, Gymnasium, PettingZoo, Brax (JAX), Isaac Gym (where licensed)
GRAPH ML:
NetworkX, PyTorch Geometric (PyG), Deep Graph Library (DGL), StellarGraph (opt), Neo4j driver (connector)
SERVING / API / DEPLOYMENT:
FastAPI, Flask, gRPC, Uvicorn/Gunicorn, TorchServe, Triton Inference Server, KServe, Seldon Core, BentoML, ONNX Runtime Server, Ray Serve, NVIDIA TensorRT-LLM, Celery, RQ
MLOPS / TRACKING / OBSERVABILITY:
MLflow, Weights & Biases, ClearML, Neptune, Aim, DVC + Git LFS, MLflow Model Registry, W\&B Artifacts Data/Model monitoring: Evidently AI, Great Expectations, Deepchecks, WhyLabs (API)
Ops/metrics/logs: Prometheus, Grafana, OpenTelemetry (opt), Loki/Elastic (opt)
CLOUD SERVICES & SDKs (CONNECTORS):
AWS CLI, boto3, Amazon SageMaker SDK, Amazon Bedrock SDK, S3/STS, Redshift (API), EMR connectors. Azure CLI, Azure ML/AI SDK, Azure OpenAI, Cognitive Services, Azure Storage
gcloud CLI, google-cloud-python, Vertex AI SDK, BigQuery, GCS. NVIDIA NGC CLI, NIM microservices (where licensed). Hugging Face Hub, OpenAI, Anthropic, Cohere, Mistral, Together, Replicate, Fireworks (APIs), Databricks (API), Snowflake (API), IBM watsonx (API)
WAREHOUSES / DATABASES
Snowflake, BigQuery, Redshift, Databricks, PostgreSQL, MySQL/MariaDB, SQLite, MongoDB, Cassandra, ClickHouse, Elastic, OpenSearch, Redis
SECURITY, SUPPLY CHAIN & SECRETS:
Trivy, Grype, Syft (SBOM), Cosign (opt), Bandit, Safety, pip-audit, HashiCorp Vault, AWS Secrets Manager (API), Azure Key Vault (API), GCP Secret Manager (API)
CONTAINERS / DEVOPS / KUBERNETES:
Docker, Podman, Kubernetes, kubectl, Helm, Kustomize, Docker Compose, Kind (local K8s demos), Argo Workflows (opt), Kubeflow (opt), Make, GNU Parallel, GitHub Actions/GitLab CI templates
LABELING & DATA CURATION:
Label Studio, CVAT, doccano, Prodigy (API/licensed), Roboflow (API), FiftyOne
VISUALIZATION & BI:
Matplotlib, Seaborn, Plotly, Altair, Bokeh, HoloViews, Apache Superset, Metabase, Grafana, Kibana (opt)
MEDIA & GEOSPATIAL UTILITIES:
FFmpeg, ImageMagick, exiftool, GeoPandas, Shapely, Fiona, Rasterio, Folium, kepler.gl (via notebooks)
DATASETS — LOCAL SAMPLES + OFFICIAL CONNECTORS:
Vision: MNIST, Fashion-MNIST, CIFAR-10/100, STL-10, Caltech-101/256 (scripts), COCO, Pascal VOC, OpenImages, ADE20K, Cityscapes, KITTI, WIDER FACE, LFW, VGGFace2 (license-aware), ImageNet (connector) NLP: WikiText-103, SQuAD, GLUE/SuperGLUE, IMDB, AG News, Yelp, Quora Duplicate, SNLI/MultiNLI, C4, The Pile (subsets), Wikipedia snapshots, Common Crawl pipelines (scripts) Audio/Speech: LibriSpeech, Common Voice, VoxCeleb, ESC-50, UrbanSound8K
Video/Multimodal: Kinetics, UCF101, HMDB51, MSRVTT, WebVid-2M (connector) Embeddings/Eval: MTEB tasks (connector), LAMBADA, BIG-bench (connector) Kaggle: kaggle CLI wired for datasets/competitions (TOS), Hugging Face Datasets (connectors)
EDGE / MOBILE / ON-DEVICE:
ONNX Runtime, TensorRT, OpenVINO, TFLite, Core ML Tools, Jetson toolchains, GGUF quantized LLMs, MediaPipe (opt)
COPILOTS & ASSISTANTS:
AINA Chatbot (built-in), OpenAI Assistants (API), Claude (API), Cohere (API), Mistral (API), LangGraph templates, Function-calling/Tools templates
CLASSROOM & COLLABORATION:
Shared notebooks/projects, environment pinning, read-only lab baselines, cohort workspaces, artifact storage, exportable MLflow runs, demo links
UTILITIES & SYSTEM TOOLS:
tmux, htop/nvtop, wget, curl, httpie, ripgrep, fd, jq, yq, rsync, rclone, cron, ssh, ssm (connectors), make, unzip/7z, tree
EXPLAINABILITY & RESPONSIBLE AI (XAI):
SHAP, LIME, Captum, ELI5, InterpretML, Alibi Detect, Fairlearn, AIF360, What-If Tool, DiCE, Responsible AI Toolbox
PRIVACY, FEDERATED & SECURE ML:
Opacus (PyTorch DP), TensorFlow Privacy, PySyft, Flower (FL), TensorFlow Federated, NVIDIA FLARE (API/connector), Crypten, TenSEAL (FHE), Pyfhel (FHE), OpenDP SmartNoise, Presidio (PII), AnonymizeDF, ARX (connector)
FEATURE STORES:
Feast, Vertex Feature Store (API), SageMaker Feature Store (API), Databricks Feature Store (API), Hopsworks (API), Tecton (API)
AGENT FRAMEWORKS & AUTOMATION:
LangGraph, CrewAI, AutoGen (Microsoft), Semantic Kernel, Haystack Agents, SuperAGI (opt)
PROMPT EVAL, RED-TEAMING & GUARDRAILS:
promptfoo, garak, Microsoft PyRIT, NeMo Guardrails, Llama Guard, Rebuff, OpenAI Evals (API), lm-eval-harness (EleutherAI), Langfuse (API), TruLens (API), Phoenix (API), Lakera Guard (API)
MODEL COMPRESSION, PRUNING & DISTILLATION:
Intel Neural Compressor, OpenVINO NNCF, torch-pruning, nn-pruning, knowledge-distillation templates (DPO/ORPO/LoRA/QLoRA), DeepSpeed-Chat, TRL (HF)
DISTRIBUTED & HPC EXTRAS:
Horovod, FSDP (PyTorch), Megatron-LM (templates), Ray Train, Lightning Fabric/Strategy, NCCL tools, Nsight Systems/Compute (env dependent)
SCIENTIFIC & MATH STACK (CORE DS):
NumPy, SciPy, SymPy, Numba, CuPy, Modin (pandas at scale), Joblib, Multiprocessing, Polars (already listed)
SCRAPING, CRAWLING & DOC INGEST EXTRAS:
Scrapy, BeautifulSoup4, lxml, trafilatura, newspaper3k, goose3, Readability, Playwright, Selenium, pyppeteer/puppeteer
NOTEBOOK ECOSYSTEM & AUTOMATION:
IPyWidgets, Jupytext, nbconvert, papermill, Voilà, Panel, Markdown/Quarto (opt)
REPORTING & DOCS GENERATION:
Pandoc, ReportLab, WeasyPrint, wkhtmltopdf (connector), mkdocs, Sphinx, Mermaid (diagrams)
EDA & DATA PROFILING:
ydata-profiling (pandas-profiling), Sweetviz, Lux (opt), Dataprep EDA (opt)
DATA QUALITY, LINEAGE & CATALOG:
Great Expectations (already), Soda Core (already), Deequ (Spark), OpenLineage (connector), Marquez (connector), DataHub (connector), Amundsen (connector)
GEOSPATIAL & MAPS (EXTRA):
GDAL/OGR, PROJ, PostGIS (connector), OSMnx, Folium (already), kepler.gl (notebooks), pydeck
RECOMMENDER SYSTEMS (EXTRA):
LightFM, Spotlight, TensorRec, NVIDIA Merlin (API/connector), Cornac, implicit (already)
SEARCH & LIGHTWEIGHT INDEXES:
Meilisearch, Typesense, Vespa (already), Elasticsearch/OpenSearch (already)
SCHEDULING & EVENTING (APP LEVEL):
APScheduler, Celery + Flower, RQ, Temporal (connector), CRON templates
LOGGING, TRACING & ERROR MONITORING:
Sentry SDK (API), OpenTelemetry (already), structlog, loguru, ML debugger: debugpy, cProfile/py-spy/line-profiler/memory-profiler, torch.profiler
BROWSER/UI TEST & LOAD:
Cypress (connector), k6 (connector), Locust (opt), Lighthouse CI (connector)
DATA ANON/SYNTHETIC DATA:
Faker, SDV (Synthetic Data Vault), ydata-synthetic, Gretel (API/connector), synthcity (opt)
MEDIA & FILE TOOLING:
pdfminer.six, pikepdf, PyMuPDF, ExifRead, Pillow/PIL, Wand (ImageMagick bindings), ffmpeg-python
BIG DATA & STREAMING:
Apache Flink (connector), Spark Structured Streaming, Kafka Streams clients, Delta Lake/Iceberg/Hudi connectors (already listed)
UI FRAMEWORKS (PYTHON APP FRONTS):
Shiny for Python (opt), Flet (opt), NiceGUI (opt), Textual/Rich (TUI)
GRAPH DATABASES (CONNECTORS):
Neo4j (py2neo/official driver), TigerGraph (connector), ArangoDB (connector)
DATA VALIDATION (SCHEMA & TYPES):
Pandera, Pydantic, Cerberus, Voluptuous
MOBILE & EDGE (EXTRA):
NCNN (opt), MNN (opt), Core ML Tools (already), MediaPipe (already), TFLite Micro (opt)
SEARCH/APIS FOR LLM CONTENT SAFETY:
OpenAI Moderation (API), AWS Comprehend (API), Google Perspective (API), Azure Content Safety (API)
BENCHMARKS & METRICS (LLM & CLASSICAL):
HF Evaluate, scikit-learn metrics suite, torchmetrics (already), MTEB (connector), GLUE/SuperGLUE (already)
KAGGLE & HF OPS (EXTRA):
kaggle CLI (already), HF Hub CLI (already), Datasets lfs cache & snapshot scripts, Spaces deployment templates (connector)
INFRA AS CODE (OPTIONAL FOR DEVOPS LABS):
Terraform (opt), Pulumi (opt), Ansible (opt)
TEAM/PRODUCTIVITY UTILITIES:
make, just (task runner) (opt), tmux (already), ripgrep/fd (already), jq/yq (already), direnv (opt)
RCAI MODULE ALIGNMENT (RUNS INSIDE AINA):
Modules 00–41: Python Programming (05), Math Foundations (06), Machine Learning (07), Train/Deploy (08), Kaggle Datasets (09), AI Frameworks (11), Object Detection (12), OpenAI & ChatGPT (13), AI in Cybersecurity (14), LLMs (15), Labs (16), Generative Art (17), Facial Recognition (18), Deep Learning Essentials (19), Data Science (20), Azure AI (21), Google Vertex AI (22), Amazon SageMaker (23), Applications (24–37), Deep Learning Tech Lectures (38), Big Data (39), Classroom Project (40), Microsoft ML Tools (41) Modules 13 (OpenAI/ChatGPT), 15 (LLMs), 16 (AI Labs), 24–37 (Applications: personal, sales, coding, images, audio, video), 30 (Personal Chatbots), plus cloud modules 21–23 (Azure, Vertex, SageMaker) for hosted agents.
AGENTIC FRAMEWORKS (CORE)
LangGraph, CrewAI, AutoGen (Microsoft), Semantic Kernel, Haystack Agents, Transformers Agents, LlamaIndex Agents, LangChain Agents, SuperAGI (opt)
ORCHESTRATION & STATE MACHINES
Graph/ DAG agents (LangGraph), router & tool-calling agents (LangChain/LlamaIndex), conversational planners, multi-agent “crews”, role & tool routing, function-schema tooling (JSON Schema/OpenAPI tool specs)
TOOL-USE / FUNCTION BRIDGES
OpenAI Assistants (API), Anthropic Tools (API), Mistral Tools (API), Cohere Tools (API), function-calling wrappers for FastAPI/HTTP, Python REPL tool, Shell (sandboxed), file I/O tools, vector-search tools, SQL tools
PLANNING & REASONING PATTERNS
ReAct, Reflexion, Tree-of-Thought (ToT), Graph-of-Thought, MRKL, Toolformer-style patterns, routing & fallback strategies, self-consistency, debate/consensus agents
MEMORY & KNOWLEDGE
FAISS, Qdrant, Milvus, Weaviate, Chroma, Redis (vector), pgvector; document loaders (unstructured, pypdf, pdfplumber, Tika, OCR) and long-term memory stores via LlamaIndex/LangChain
WEB BROWSING / AUTOMATION
Playwright, Selenium, browser-use tools, requests/HTTP clients, search connectors (Tavily (API), SerpAPI (API), Wikipedia), scraping (Scrapy, BeautifulSoup, trafilatura, newspaper3k)
EXECUTION SANDBOXES
Python notebook kernel, Python REPL tool, code-execution cells, task runners, safe temp workspaces, FFmpeg/ImageMagick utilities for media tasks
EVAL, TELEMETRY & GUARDRAILS
promptfoo, garak, Microsoft PyRIT, NeMo Guardrails, Llama Guard, Rebuff, TruLens (API), Langfuse (API), Phoenix (API), OpenTelemetry, policy prompts, allow/deny lists, PII scrubbing (Presidio), jailbreak/TOFU checks
WORKFLOWS & SCHEDULING
Airflow, Prefect, Dagster, APScheduler; event triggers to run agents on schedules or data arrivals
PREBUILT AINA AGENT TEMPLATES
Research & Cite Agent (web browse + sources). PDF/Data Analyst Agent (RAG over docs). Dev/Ops Remediation Agent (log triage → fix suggestion). Support Triage Agent (classification → reply draft). ETL Orchestrator Agent (ingest → clean → vectorize → index). Creative Studio Agent (prompt → image/music/video pipelines)
DATASETS — LOCAL SAMPLES + OFFICIAL CONNECTORS:
Hugging Face Datasets (datasets library) with streaming, map/filter, parquet/arrow caching; MTEB & GLUE/SuperGLUE via HF; Wikipedia/Common Crawl pipelines (scripts); Kaggle CLI (TOS)
LLM SERVING & OPTIMIZED BACKENDS:
vLLM, TGI (Hugging Face Text Generation Inference), llama.cpp/gguf, Ollama; Optimum Runtime integrations (ONNX Runtime, OpenVINO, TensorRT)
PHISHING (emails / URLs / webpages)
Phishing Email Dataset (Kaggle) — cleaned corpus of phishing vs legitimate emails; great for NLP classification, intent/IOC extraction, and transformer-based models. Load via Kaggle CLI or Hugging Face mirrors.
RANSOMWARE / MALWARE (behavioral, static, telemetry)
Ransomware PE Feature Sets (Kaggle) — static PE-file feature vectors (headers, imports, entropy, strings) for safe ML without executing binaries. Good for rapid prototyping of static-analysis classifiers. Network & Host Telemetry Collections (CIC-IDS / custom corpora) — includes benign vs malicious network flows and host logs you can use to simulate ransomware lateral movement or exfil patterns.
Join RCAI. Unlock Rocheston AINA OS. Master AI
Rocheston AINA OS is the cloud-native, Linux-based Artificial Intelligence Operating System used inside the Rocheston Certified AI Engineer (RCAI) program. It’s GPU-accelerated, zero-install, and purpose-built for learning by doing: you write Python, run notebooks and scripts, compile code, work with massive datasets, train models, and deploy AI applications—all in your browser. AINA OS updates weekly with the latest models, frameworks, and tools so RCAI labs stay current. It isn’t sold separately—access is exclusive to enrolled RCAI students.
Inside AINA, you don’t “learn about” AI—you operate it. From day one you open JupyterLab and VS Code, write Python notebooks and .py scripts, manage packages (pip/conda), and automate pipelines with shell scripts. Need performance or systems work? Drop to the terminal to compile C/C++ (or build Rust/Go) and link it into your Python workflows. Track experiments in MLflow/TensorBoard, benchmark vector search, stand up Triton/KServe endpoints, and deploy applications—always with snapshots/resets so you can break things safely and learn fast. The built-in AINA Chatbot acts like a mentor—generating code, explaining frameworks, and guiding you through pipelines so progress compounds daily.
Best of all, hundreds of labs and reference apps are prebuilt for you. Learn, modify, recompile, and deploy—it's that easy. Start from working baselines for RAG assistants, vision pipelines, speech and audio, multi-agent workflows, data engineering templates, and full MLOps projects. Every lab is reproducible and exportable (push code to Git, ship containers, and export models in standard formats like ONNX/TF/Torch/GGUF).
This is the RCAI advantage: every module runs inside AINA’s reproducible environments, so you complete labs and capstones without setup friction and leave with live demos and endpoints you can show to anyone. Your work is portable—continue on your employer’s stack whenever needed. AINA OS cannot be downloaded or purchased; it’s included with the RCAI program and kept permanently current through weekly upgrades.
If you want the fastest path from idea to impact—from first login to shipped AI—apply to RCAI, unlock AINA OS, and build your mastery of artificial intelligence.
Apply to RCAI, unlock AINA OS, and own your mastery of artificial intelligence.
Inside AINA, you don’t “learn about” AI—you operate it. From day one you open JupyterLab and VS Code, write Python notebooks and .py scripts, manage packages (pip/conda), and automate pipelines with shell scripts. Need performance or systems work? Drop to the terminal to compile C/C++ (or build Rust/Go) and link it into your Python workflows. Track experiments in MLflow/TensorBoard, benchmark vector search, stand up Triton/KServe endpoints, and deploy applications—always with snapshots/resets so you can break things safely and learn fast. The built-in AINA Chatbot acts like a mentor—generating code, explaining frameworks, and guiding you through pipelines so progress compounds daily.
Best of all, hundreds of labs and reference apps are prebuilt for you. Learn, modify, recompile, and deploy—it's that easy. Start from working baselines for RAG assistants, vision pipelines, speech and audio, multi-agent workflows, data engineering templates, and full MLOps projects. Every lab is reproducible and exportable (push code to Git, ship containers, and export models in standard formats like ONNX/TF/Torch/GGUF).
This is the RCAI advantage: every module runs inside AINA’s reproducible environments, so you complete labs and capstones without setup friction and leave with live demos and endpoints you can show to anyone. Your work is portable—continue on your employer’s stack whenever needed. AINA OS cannot be downloaded or purchased; it’s included with the RCAI program and kept permanently current through weekly upgrades.
If you want the fastest path from idea to impact—from first login to shipped AI—apply to RCAI, unlock AINA OS, and build your mastery of artificial intelligence.
Apply to RCAI, unlock AINA OS, and own your mastery of artificial intelligence.