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Transform Unstructured Text Into Instant, Accurate Answers

12 Weeks. Live Online Classes. Instructor-led.

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Our Partners

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A group of diverse people in a business meeting, with a woman standing and speaking to the seated attendees in a bright, modern office with large windows.

Ship multilingual NLP systems with monitoring, guard-rails, and governance baked in

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Craft vector & hybrid search pipelines using pgvector, Pinecone, and Elasticsearch

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Engineer Retrieval-Augmented Generation (RAG) services that ground LLMs in trusted data

What you will learn?

    • Generate sentence-level embeddings with OpenAI, Cohere & HF models

    • Design efficient HNSW/IVF indexes in pgvector, Pinecone, or Milvus

    • Tune distance metrics, index parameters, and recall/latency trade-offs

    • Combine dense (vector) and sparse (BM25) scores in Elasticsearch hybrid search

    • Train lightGBM / XGBoost learning-to-rank models on click-through logs

    • Evaluate NDCG, MRR, and success@k; instrument A/B experiments

    • Chain retriever → re-ranker → LLM answerer with LangChain or LlamaIndex

    • Apply chunking, metadata filters, and Max Marginal Relevance (MMR) to boost relevancy

    • Expose gRPC/REST API; benchmark answers for factual grounding & latency

    • Fine-tune models (e.g., MiniLM, Mistral-7B) on domain-specific corpora

    • Integrate language detection and neural MT for cross-lingual retrieval

    • Implement transfer-learning tricks to keep GPU bills low

    • Build automatic hallucination & toxicity tests with Promptfoo / Rebuff

    • Stream metrics to Prometheus; create Grafana dashboards for drift & latency

    • Produce governance docs for GDPR, privacy, and data provenance

    • Team POC: policy-search, knowledge-base Q&A, or legal-doc RAG system

    • Deliver architecture diagrams, cost model, and live demo to hiring-manager panel

Turn terabytes of documents into instant, trustworthy answers. You’ll master embeddings, hybrid BM25 + vector ranking, and build multilingual RAG pipelines with pgvector, Pinecone, LangChain and OpenAI. Automated hallucination tests, governance dashboards, and Prometheus monitoring ensure your search or Q&A service stays accurate, compliant, and fast. Ideal for engineers modernising legacy Lucene stacks or delivering AI-powered knowledge bases in finance, legal, or energy sectors.

Who Should Enrol?

  • Upgrade from TF-IDF and keyword scoring to dense-vector retrieval, hybrid rankers and RAG pipelines that beat commercial SaaS search.

  • Add text‐heavy use-cases (chatbots, doc-QA, semantic search) to your model portfolio and learn to serve them with latency and cost SLAs.

  • Move beyond analytics to production-grade NLP: embeddings, transformers, evaluation suites and online A/B relevance tests.

  • Embed vector databases, similarity APIs and streaming ingestion into existing micro-service architectures—without breaking auth or billing.

  • Understand how modern retrieval works so you can scope, prioritise and govern enterprise search projects with vendors or in-house teams.

Professionals who must turn mountains of text into search and Q&A experiences users trust.

Prerequisites
Solid Python & Git, basic ML knowledge. Free Deep Learning Core and Docker-K8s Mini-Camp bridge badges are included for all Intermediate-track graduates.

Career Pathways

  • Build and optimise dense-vector, hybrid and RAG search stacks that power chat, FAQ and knowledge-base experiences.

  • Design offline metrics and online A/B tests, fine-tune LLM rankers and iterate on relevance signals at scale.

  • Integrate embedding services, vector DBs, orchestration frameworks and observability into cloud-native platforms.

  • Scope end-to-end IR solutions for large document corpora, balancing latency, accuracy and cost.

  • Translate business questions into scalable NLP/search architectures and roadmaps that engineering teams can execute.

Graduates leave with a live enterprise search stack (vector DB + RAG API), a publish-ready design doc, and recruiter-ready stories that map to the majority of “Search/NLP Engineer” roles on today’s job boards.

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Amir Charkhi
Technology leader | Adjunct Professor | Founder

With 20 + years across energy, mining, finance, and government, Amir turns real-world data problems into production AI. He specialises in MLOps, cloud data engineering, and Python, and now shares that know-how as founder of AI Tech Institute and adjunct professor at UWA, where he designs hands-on courses in machine learning and LLMs.

Advanced: Enterprise NLP & Search

12 Weeks. Live Online Classes.

Advanced: Enterprise NLP & Search Advanced: Enterprise NLP & Search
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Advanced: Enterprise NLP & Search
$4,250.00

Turn terabytes of documents into instant, trustworthy answers. You’ll master embeddings, hybrid BM25 + vector ranking, and build multilingual RAG pipelines with pgvector, Pinecone, LangChain and OpenAI. Automated hallucination tests, governance dashboards, and Prometheus monitoring ensure your search or Q&A service stays accurate, compliant, and fast. Ideal for engineers modernising legacy Lucene stacks or delivering AI-powered knowledge bases in finance, legal, or energy sectors.

Student Outcomes

  • "I recently completed a course at AI Tech Institute and was thoroughly impressed. The curriculum was thoughtfully structured, the instructor was truly dedicated, and the overall learning experience delivered exceptional value."

    Maria

  • "Taking the Machine Learning course at AI Tech Institute was a game changer. It gave me the skills and confidence to break into AI and helped me land a job soon after. The content was practical and beginner friendly, with clear lessons and hands-on projects that made a real difference during interviews."

    Pani

  • "Very valuable course. Amir is a great instructor, explained how machine learning used in the real world in a easy way. Highly recommend for people who want to know more about AI or work in this area."

    Hoàng Phước

  • "The AI and Data science bootcamp was one of the most impactful learning experiences I’ve had. The curriculum was well structured. The instructor was knowledgeable and supportive, and the course provided a strong balance between theory and hands on labs. Highly recommended!"

    Ehsan

  • "I took a course at AI Tech Institute, and it genuinely exceeded my expectations. The content was not only clear and well-structured, but also incredibly relevant to the challenges I deal with day-to-day at work."

    Rosha

  • "Extremely happy with clear, well designed and applicable syllabus to be implemented in day-to-day need of nowadays coding ability. Coming from non-IT background, it is a valuable experience of seamlessly and effortless the teaching was, for me to understand the complex subjects. Would definitely recommend to colleagues and friends!"

    Kresna

  • "Absolutely fantastic experience with AI Tech Institute! I couldn’t be happier with the quality of education and support I received here. The instructors are true experts — knowledgeable, approachable, and always willing to help students succeed. The curriculum is up-to-date with the latest advancements in AI and tech, blending theory with plenty of hands-on projects that really build practical skills."

    Foad

  • "Amir is both technically proficient, and a very good teacher. He was attentive to where each person in the group was and paced the information flow accordingly. I also found him very approachable, encouraging and supportive."

    Francois

Frequently Asked Questions

  • Beginner courses: none— we start with Python basics.
    Intermediate & Advanced: ability to write simple Python scripts and use Git is expected.

  • Plan on 8–10 hours: 2× 3-hour live sessions and 2–4 hours of project work. Advanced tracks may require up to 10 hours for capstone milestones.

  • All sessions are recorded and posted within 12 hours. You’ll still have access to Slack/Discord to ask instructors questions.

  • New intakes launch roughly every 8 weeks. Each course page shows the exact start date and the “Apply-by” deadline.

  • Just a laptop with Chrome/Firefox and a stable internet connection. All coding happens in cloud JupyterLab or VS Code Dev Containers—no local installs.

  • Yes. 100 % refund until the end of Week 2—no questions asked. After that, pro-rata refunds apply if you need to withdraw for documented reasons.

  • Absolutely. We issue invoices to companies and offer interest-free 3- or 6-month payment plans.

  • Live Q&A in every session, 24-hour Slack response time from instructors, weekly office-hours, and code reviews on your GitHub pull requests.