I make learning Data Science effortless for you | Data Science @ Bosch

Hi 👋🏻
I'm Banias, AI Engineer at Netze BW.

Applying new technologies and methods in Data Science & AI is my passion.

Through my experience and continual learning, I've gained valuable insights and tips that I love sharing with Data Scientists, Machine Learning Engineers and builders like you.

If you're looking for practical advice, helpful resources, and articles on the latest trends or best practices in the field, you're in the right place.

Let's connect and stay updated on the exciting world of Data Science together. Feel free to reach out, read my articles, and let's grow our knowledge and network. :)


Note: My views are my own and don‘t represent my employer.

Banias Baabe

Running a 70B LLM on a single 4GB GPU is possible with 𝗔𝗶𝗿𝗟𝗟𝗠.

𝗔𝗶𝗿𝗟𝗟𝗠 optimizes inference memory usage so you can run big models without quantization, distillation, or pruning.

It runs inference layer by layer, loading only the layer that is needed for the next compute step, then freeing that memory before moving on.​

To make that practical, it first preprocesses the original Hugging Face weights and re-shards them by transformer layer (instead of giant multi‑GB shards), so each load is focused and smaller.​

This matters because disk I/O becomes the bottleneck when you stream weights.

The main thing that still sits in GPU memory is the KV cache for attention, which is much smaller than full model weights for typical context lengths.​

𝗔𝗶𝗿𝗟𝗟𝗠 also adds prefetching so it can overlap loading the next chunk of weights with the current compute, which helps claw back some speed.

And if you want more throughput, it supports an optional “model compression” mode using block-wise quantization of weights to shrink what needs to be loaded from disk, with the project claiming up to ~3x speedup and minimal accuracy loss.

🔗 Link to repo: github(.)com/lyogavin/airllm


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I teach AI Engineers the concepts and tools they need to stay ahead | AI Engineer @ Netze BW

Stop destroying your documents with 512-token chunks.

Traditional RAG is breaking your tables, splitting your diagrams, and mixing up your model specifications.

I just read a great article from Alex Vesa about a working data modeling approach for complex technical documents.

The problem? Most RAG systems treat a 500-page service manual like a blog post. They chunk blindly. A table that spans three pages gets shredded into meaningless fragments.

Here's what actually works:

✅ Pages, not chunks
Use full pages as your retrieval unit. That's how humans read technical docs anyway.
✅ Three-page sliding windows
Process each page with its neighbors (N-1, N, N+1). Now your system knows when content continues across boundaries.
✅ Treat tables like they matter
Extract structure. Generate metadata. Flatten for search. Keep all three representations.
✅ Multi-vector indexing
Dense embeddings for semantic search. Sparse vectors for exact matches. ColBERT for precision. Combine them.
✅ Domain-specific metadata
Model applicability, component types, cross-page relationships. Not just title and date.

Link ➙ https://lnkd.in/dJ9aJKAB

Thinking about using 𝗽𝗴𝘃𝗲𝗰𝘁𝗼𝗿 for your next project?

You might want to think again.

Here's the reality of using it in production:
1️⃣ 𝗜𝗩𝗙𝗙𝗹𝗮𝘁 𝗶𝗻𝗱𝗲𝘅𝗲𝘀 𝗻𝗲𝗲𝗱 𝗰𝗼𝗻𝘀𝘁𝗮𝗻𝘁 𝗿𝗲𝗯𝘂𝗶𝗹𝗱𝘀. New vectors throw off your cluster distribution over time.
2️⃣ 𝗛𝗡𝗦𝗪 𝗶𝗻𝗱𝗲𝘅𝗲𝘀 𝗮𝗿𝗲 𝗺𝗲𝗺𝗼𝗿𝘆 𝗵𝗼𝗴𝘀. We're talking 10GB of memory for hours just to build an index for millions of vectors.
3️⃣ 𝗜𝗻𝘀𝗲𝗿𝘁𝗶𝗻𝗴 𝗻𝗲𝘄 𝘃𝗲𝗰𝘁𝗼𝗿𝘀 𝗶𝘀 𝗮 𝗽𝗮𝗶𝗻. With IVFFlat, you have to reindex preiodically. With HNSW, you're looking at graph traversals and memory allocation for every new vector.
4️⃣ 𝗠𝗲𝘁𝗮𝗱𝗮𝘁𝗮 𝗳𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴 𝗶𝘀𝗻'𝘁 𝗰𝗹𝗲𝗮𝗿-𝗰𝘂𝘁. Pre-filtering is great for selective filters, but post-filtering can give you incomplete results, and it only gets worse with more filters.
5️⃣ 𝗡𝗼 𝗯𝘂𝗶𝗹𝘁-𝗶𝗻 𝗵𝘆𝗯𝗿𝗶𝗱 𝘀𝗲𝗮𝗿𝗰𝗵. You'll have to figure out how to balance vector similarity with text relevance and normalize scores on your own.

A new extension, pgvectorscale, does fix some of these problems with more memory-efficient indexing and better filter performance. But it isn't supported out-of-the-box on Azure and AWS, so you'd have to manage your own Postgres instance.

𝗽𝗴𝘃𝗲𝗰𝘁𝗼𝗿 is a great tool, but it's not a silver bullet. Make sure you're ready for the operational overhead before you commit.

🔗 Link to article: https://lnkd.in/d6jSRy2y


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Tired of blocking I/O messing up your async Python apps?

Try 𝗮𝗶𝗼𝗳𝗶𝗹𝗲𝘀 - the game-changer for handling files in asyncio without the headaches.

It's an Apache-licensed library that wraps standard file ops into async magic.

Why you should use it:
• Mirrors Python's built-in file API, so it's super familiar.
• Supports buffered text, binary files, and even tempfiles asynchronously.
• Delegates to a thread pool to keep your event loop flying.


🔗 Link to repo: github(dot)com/Tinche/aiofiles

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Vector databases are bloated.

What if you could get the same power with 97% less space?

Try 𝗟𝗘𝗔𝗡𝗡, the world's smallest vector index.

It transforms your laptop into a private and powerful RAG system.

You can finally RAG on everything.
-> Your entire file system
-> Millions of local emails
-> 💻 Your entire codebase
-> 💬 All your chat histories

Why is 𝗟𝗘𝗔𝗡𝗡 interesting?
✅ 𝗜𝘁'𝘀 𝗶𝗻𝗰𝗿𝗲𝗱𝗶𝗯𝗹𝘆 𝗹𝗶𝗴𝗵𝘁𝘄𝗲𝗶𝗴𝗵𝘁: Index 60 million documents in just 6GB instead of 201GB.
✅ 𝗜𝘁'𝘀 𝟭𝟬𝟬% 𝗽𝗿𝗶𝘃𝗮𝘁𝗲: Your data never leaves your laptop. No cloud costs, period.
✅ 𝗧𝗵𝗲𝗿𝗲 𝗶𝘀 𝗻𝗼 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆 𝗹𝗼𝘀𝘀: Get the same search quality as heavyweight solutions.

🔗 Link to repo: github(dot)com/yichuan-w/LEANN


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Do you want to identify which lines use the most amount of memory in your Python script?

Try 𝗺𝗲𝗺𝗼𝗿𝘆_𝗽𝗿𝗼𝗳𝗶𝗹𝗲𝗿.

𝗺𝗲𝗺𝗼𝗿𝘆_𝗽𝗿𝗼𝗳𝗶𝗹𝗲𝗿 is a Python module to make a line-by-line analysis of memory consumption for Python functions.

Below you can see how to use memory_profiler within your Python script:

• Decorate the function you want to profile with @𝗽𝗿𝗼𝗳𝗶𝗹𝗲.
• Run the script by passing the option -𝗺 𝗺𝗲𝗺𝗼𝗿𝘆_𝗽𝗿𝗼𝗳𝗶𝗹𝗲𝗿 to load the 𝗺𝗲𝗺𝗼𝗿𝘆_𝗽𝗿𝗼𝗳𝗶𝗹𝗲𝗿 module.

Link to project: pypi(dot)org/project/memory-profiler


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What, if an API call in your program fails?

Because of, let’s say, an instable internet connection?

This is not so uncommon.

You usually should have some retry mechanism in your program.

With 𝐭𝐞𝐧𝐚𝐜𝐢𝐭𝐲 in Python, this isn’t a problem anymore.

𝐭𝐞𝐧𝐚𝐜𝐢𝐭𝐲 offers a retrying behavior with a decorator with powerful features like:

- Define Stop Conditions
- Define Wait Conditions
- Customize retrying on Exception
- Retry on Coroutines

One of my favorite libraries!

Check it out: github(dot)com/jd/tenacity


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OpenAI's Responses API isn't what they're telling you it is.

They are talking about how it's "faster, more flexible, easier" than the old chat completions API.

But here's what's really going on.

OpenAI hides their reasoning traces.

Unlike Claude, DeepSeek, or Qwen, you can't see GPT-5's chain of thought.

This creates a massive problem for developers using the stateless chat completions API.

Without access to reasoning traces, you can't pass them back into the conversation.

Result? GPT-5 performs worse than it should in your applications.

The Responses API is the workaround.

✅ It's stateful (OpenAI stores conversation history on their servers)
✅ They maintain the hidden reasoning traces in the backend
✅ They plug it into conversations automatically
✅ They strip it out before sending responses back to you

You get full model capability without ever seeing the reasoning.

Here's the uncomfortable truth:

The old stateless API is actually simpler.

Prefix caching works in both. Parallel tool calling works in both.

There's nothing inherently better about stateful architecture here.

The Responses API only exists because OpenAI won't show you what their models are thinking.

Meanwhile, Anthropic keeps their reasoning traces completely transparent.

If you're building with AI APIs in 2026, this matters.

The question is whether you're comfortable with your provider hiding critical information from you.

Link to article: https://lnkd.in/drZjD9Xj


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AI makes your team faster.

But it also puts your data at risk.

Files are moving to new tools constantly.

AI models are training on things they shouldn't see.

And old security tools just can't see what is happening.

They are blind to the context that actually matters.

That is why I'm watching Cyberhaven’s launch on February 3.

They are revealing a new platform that combines data security and protection into one tool.

It's built to handle how messy and fast data moves today.

Join the live event to see:
📍 A live demo of this new AI-native platform.
📍 How to spot the few dangerous actions hidden inside millions of normal ones.
📍 Real stories from leaders on why old rules don't work anymore.

Don't wait for a data leak to find the holes in your security.

Grab your spot here: https://lnkd.in/dC-aGjjM

Struggling with slow data processing for AI training?

Try 𝗹𝗶𝘁𝗱𝗮𝘁𝗮, the Python library that transforms and optimizes datasets at scale!

𝗹𝗶𝘁𝗱𝗮𝘁𝗮 offers:
🟢 Streaming large cloud datasets without local downloads
🟢 Accelerating training by 20x with optimized data
🟢 Parallelizing tasks across multiple machines effortlessly
🟢 Securely handling sensitive data with advanced encryption

Perfect for images, text, video, and more.

Check it out: github(dot)com/Lightning-AI/litdata


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weet.ai

Wearing multiple hats (ML Engineer, AI Engineer, Solution Architect) to provide technical implementation, architecture, guidance and advisory services around the following topics and technologies:

- Large Language Models (LLMs)
- Vector Databases
- Retrieval Augmented Generation (RAG)
- Prompt Engineering
- Prompt Injection Mitigation & Guardrails
- LLM Observability
- RAG Evaluation
- LLM Evaluation
- LLMOps
- Cloud & AI Architecture
- AI Security
- Open Source LLMs / Hugging Face
- OpenAI & ChatGPT
- Azure Databricks & MLflow
- Infrastructure as Code (IaC) & Terraform
- European AI Act
- Gen AI & LLM Red Teaming

Principal Gen AI Engineer

Fractional CTO & Technical Advisor For Startups

I have helped many startups & companies understand, fine tune and deploy their Large Language Models and Gen AI applications following Software Engineering, MLOps and LLMOps best practices.

My biggest learning during this journey was that enterprises face both technical and business challenges when trying to leverage Generative AI and Large Language Models.

This was my biggest motivation for starting weet.ai: helping startups & companies navigate this complex landscape and unlock unique use cases powered by Generative AI.

Keywords: Python, PyTorch, Generative AI, LLMs, MLOps, Machine Learning Engineering, Artificial Intelligence, LLMOps, Kubernetes, MLflow, Weights & Biases, Hugging Face, Docker, FastAPI, Terraform, Azure, AWS, GCP, Apache Spark, Apache Kafka, ReactJS, Langchain, LlamaIndex, Text Generation Inference (TGI), vLLM

Founder

O'Reilly

Technical reviewer for the upcoming book "Hands-on Large Language Models" by Jay Alammar (Cohere) and Maarten Grootendorst (creator of BERTopic).

Topics covered:

- Build advanced LLM pipelines to cluster text documents and explore the topics they belong to
- Build semantic search engines that go beyond keyword search with methods like dense retrieval and rerankers
- Learn various use cases where these models can provide value
- Understand the architecture of underlying Transformer models like BERT and GPT
- Get a deeper understanding of how LLMs are trained
- Optimize LLMs for specific applications with methods such as generative model fine-tuning, contrastive fine-tuning, and in-context learning

To be released in December 2024

Technical Reviewer

Hugging Face

As part of Hugging Face I'm currently developing and growing the following products and initiatives:

- Inference Endpoints
- Text Generation Inference (TGI)
- Autotrain
- Hugging Face on AWS & AWS SageMaker
- GCP & Vertex AI
- Hugging Face on Azure
- NVIDIA DGX Cloud / NVCF Serverless Training

Keywords: Docker, Kubernetes, AWS, AWS SageMaker, Transformers, PyTorch, Unit Testing, CICD, Large Language Models, Generative AI, Machine Learning Engineering, FastAPI, ReactJS, Svelte

ML & Software Engineer

Databricks

- Global SME in the fields of MLOps, Gen AI and LLMs

- Data and AI Summit Trainer for Machine Learning Engineering on Databricks

- Speaker at Big Data London 2022: Fine Tuning and Deploying Chatbots on Databricks with Hugging Face and MLflow

- As a Specialist Solutions Architect, I have a heavy hands-on focus on helping customers achieve the best outcomes from AI and Machine Learning. Specialties:

- MLOps
- Machine Learning Engineering
- Deep Learning
- Computer Vision
- Natural Language Processing
- Natural Language Understanding
- Large Language Models (LLMs)
- Generative AI

In terms of tech stack, my work usually entails:

- PyTorch
- PyTorch Lightning
- Hugging Face
- MLflow
- Apache Spark & SparkML
- Apache Kafka
- Scikit-Learn
- Terraform
- Petastorm
- Horovod
- Deepspeed
- FastAPI
- Kubernetes

Specialist Solutions Architect Gen AI & ML Engineering

At ING, I was part of the Global Customer Dialogue Analytics CoE. 

My role was to leverage my Data Science, Data Engineering, Machine Learning Engineering, Large Language Models (LLMs) and Solution Architecture skills in order to deliver the customer service of the future.

Keywords: Data Engineering, Python, Docker, Kubernetes, OpenShift, Helm, Apache Airflow, Machine Learning Engineering, MLOps, Natural Language Processing (NLP), CICD, Chatbots, Automatic Speech Recognition (ASR), Tensorflow, PyTorch, Hugging Face, Kaldi, Named Entity Recognition (NER), Sci-Kit Learn, spaCy, Postgres, LLMs

Senior Machine Learning Engineer | Customer Service AI

ABN AMRO Bank N.V.

As part of Chief Architecture and Data Management team, my role was to architect and implement a bank wide Data Orchestration Platform based in Apache Airflow. 

This was structured as a PaaS offering on top of Kubernetes, so that multiple domain data stores within ABN could onboard their orchestration workloads in a seamless manner. This was a critical step in the road towards the implementation of a Data Mesh architecture.

Consulting assignment through Altius/Avanade.

Keywords: Azure, Python, Data Engineering, Databricks, Docker, Kubernetes, Helm, Pyspark, Apache Airflow, CICD, Hashicorp Vault, Data Mesh

Data Engineer

Ahold Delhaize

At Albert Heijn my role was to create an Enterprise Data Lake for customer data, which allowed data scientists, data analysts, marketers and other business users to unlock unique business insights and opportunities.

Consulting assignment through Altius/Avanade.

Keywords: Azure, Delta Lake, Data Lake, Data Engineering, Databricks, Python, Apache Spark, Pyspark, Hashicorp Terraform, Infrastructure as Code, CICD

Vattenfall

Worked as a consultant within the Renewable Energy Forecasting and Optimization department in Hamburg, Germany, where my main objective was to create a streamlined, realtime data platform based in microservices, with the focus of taking the Digital Power Purchase Agreement (PPA) - day-ahead and intraday energy trading business to the next level

Consulting assignment through Sia Partners.

Keywords: Azure, Python, Docker, Kubernetes, Machine Learning, Sci-Kit Learn, Gradient Boosted Decision Trees, LightGBM, CICD, DevOps

Bradesco

Managed IT products in order to implement digital change and transformation within Private Banking and Foreign Exchange departments in Argentina, Luxembourg, Cayman Islands and New York.

Product Manager FX & Private Banking IT

HSBC Global Banking and Markets

Managed the IT products for Equities Derivatives & Delta One and Broker Dealer (Cash Equities, Futures and Derivatives) departments, including initiatives such as algorithmic trading strategies (futures, index and ETF arbitrage) & platforms, anti-money laundering, front office / trading, risk and back office / post trading platforms.

Keywords: High Frequency Trading (HFT), Direct Market Access (DMA), Python, FIX Protocol, Apama, Complex Event Processing

Product Manager High Frequency Trading & Broker Dealer

Gen AI | LLMs | Machine Learning Engineering | AI Engineering | Azure | AWS | CKAD | MLOps | LLMOps

I have a strong background in software engineering, cloud architecture, solution architecture, machine learning engineering, generative AI, large language models, MLOps and LLMOps.

Some of the companies that I've worked with in the past include Hugging Face, Databricks, ING Bank, ABN Amro, Koninklijke Ahold Delhaize, Vattenfall AB and HSBC Bank - in Freelance, FTE and Contract capacities.

My tech stack currently includes:

- Python
- Rust
- DSPy
- Langchain
- LlamaIndex
- Autogen 
- Crew AI
- Phoenix Arize
- MLflow
- Python
- React JS & TypeScript
- Terraform
- Azure Databricks
- Docker
- Kubernetes
- Vector DBs
- Pytorch
- Apache Spark 

Last but not least, I am a huge believer of teamwork, learning, teaching, autonomy, accountability, user-centricity, and data-driven decisions. I am passionate about learning new technologies and advancing the state of the art in data and AI.

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Banias Baabe

🚀 Rafael V. Pierre, MSc.

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🚀 Rafael V. Pierre, MSc.

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