The stack we teach and deliver on
The stack we teach and deliver on. Aventure Cloud builds and teaches software on the same technologies — so the skills we train are the skills we deliver with.
Frontier Technologies
Frontier Technologies is our focus: Frontier AI and Frontier Technologies — from traditional software through machine learning, deep learning, generative AI, and Agentic AI, delivered on Azure, AWS, and Google Cloud.
Languages
Languages — Python, Java, TypeScript, SQL, and Go. Skills: Python for data, AI, and automation; Java for enterprise and Spring Boot services; TypeScript and JavaScript for web front ends; SQL for data and analytics; Go for fast, reliable services.
AI & LLM Engineering
AI & LLM Engineering — LangChain, LangGraph, LangSmith, RAG, MCP, A2A, evals, and guardrails. Skills: LLM APIs and function calling; prompt engineering and structured output; chains, prompts, tools, and memory with LangChain; agent workflows, state, and multi-agent orchestration with LangGraph; tracing, evaluation, and observability for agents with LangSmith; RAG systems — chunking, embeddings, and vector search; Model Context Protocol (MCP) and agent-to-agent (A2A) communication; LLM evaluation and guardrails.
Agentic AI Systems
Agentic AI Systems — LLM application patterns, agent design (planning, tool use, memory, and multi-agent workflows), RAG systems, and evaluation & safety. We take existing traditional software, add an intelligence layer, and take it end to deployment — the Forward Deployment Engineer path: traditional software → intelligence layer → end to deployment.
Cloud & DevOps
Cloud & DevOps — Azure, AWS, Google Cloud, Terraform, Bicep, Docker, Kubernetes/AKS, GitHub Actions, and Azure Pipelines. Skills: cloud administration and architecture on Azure, AWS, and GCP; infrastructure as code with Terraform, ARM templates, and Bicep; containers and orchestration with Docker and Kubernetes (AKS); CI/CD with Azure Pipelines and GitHub Actions; monitoring, observability, and site reliability engineering.
Terraform — Multicloud Infrastructure with Terraform: infrastructure as code across Azure, AWS, and GCP in one workflow. Terraform providers and modules, workspaces and state, variables and outputs, reusable modules, and environment promotion (dev / QA / stage / prod) — with the same governance (policy, RBAC, tags) applied everywhere. See the Multicloud Infrastructure with Terraform training track under Trainings.
Data
Data — Microsoft Fabric, Data Engineering, ML, DL, and MLOps. Skills: data pipelines, ETL/ELT, data warehousing, lakehouse, and streaming; Microsoft Fabric — OneLake, Lakehouse, Data Warehouse, Data Factory, Power BI, Real-Time Intelligence, and Data Activator, with medallion architecture (bronze/silver/gold) and governed data; machine learning and deep learning with Scikit-learn, TensorFlow, and PyTorch; MLOps — experiment tracking, model versioning, model registry, CI/CD for ML, deployment, monitoring, and drift detection.
Web & APIs
Web & APIs — React, Node.js, FastAPI, REST, GraphQL, and Next.js. Skills: front-end development with React and Angular; APIs with Node.js and FastAPI; REST and GraphQL services; full-stack delivery with Java Spring Boot.
Quality Engineering
Quality Engineering — Pytest, Playwright, Selenium, Jest, API Testing, and Load Testing. Skills: test automation with Playwright and Selenium; API testing; unit and integration testing with Pytest and Jest; load testing.
Tools we use on each cloud
Azure
Azure Machine Learning, Azure AI Services, Microsoft Copilot in Azure, Azure OpenAI Service, Azure AI Studio, Azure AI Vision, Azure AI Search, Azure AI Bot Service, Azure Databricks, Azure AI Language.
Google Cloud
BigQuery, Cloud Dataproc, Looker, Vertex AI, AutoML, TensorFlow, TPU, Cloud Vision API, Cloud Natural Language API.
AWS
Amazon EMR, AWS Glue, Amazon SageMaker, Amazon Kinesis, Amazon QuickSight, Amazon Bedrock, Amazon Rekognition, Amazon Forecast, Amazon Monitron, AWS Inferentia.
Want to build with this stack, or learn it? Email info@aventurecloud.net.