Conference hayaData has place in Tel Aviv in the last week of September 2024. It was really lucky to me attend the event and discover a lot of new stuff.

I would like to leave here my notes per speech I attended:

The Future of Data is Words by Josef Goldstein

• metadata is new gold and companies accurately collect, save and use/reuse it are on the top;

• prompting in sql queries (ai query) is new format of searching in tables

New terms: NSQ, semantic layer, RAG, self-served AI, sql AI query

Evaluating the Unseen: Supervised Evaluation for Unsupervised Algorithms by Ben Harel

• understand the manual label groups and create suitable features for unsupervised learning

Tailor-Made LLM Evaluations: How to Create Custom Evaluations for your LLM by Linoy Cohen

• benchmarking Leaderboard at Hugging Face simplify evaluation process by answering questions such as if training passed as excepted etc.

• automate and versatile evaluation by using LLM-as-a-judge

• LLM has bios such as position, verbosity, self-enhancement, authority

New terms: data contamination, benchmark frameworks

Metric Store by Ben Hababo and Mickey Rozen

• data lake is highly common pool;

• Monte-Carlo algorithms and metrics are widely used;

• for same questions use AI solution such as Metaphor

New terms: Looks tool, Data Pulse, data democratization

Learning the Ropes of Synthetic Data by Noa Zamstein

• image is collection of pixels in rows and columns (5): X, Y, R, G, B;

• synthetic data can be created to present the real data cases especially for data privacy sensitive areas. Synthetic data is reference to original data, like created from that, but without any (privacy) connections;

• combination of data: only one male in that cab or one unique name, so don’t need to specify, but can easy define by few parameters;

• data minimization while analyzing, take only relevant to case fields;

• data selection as hierarchy (like not heard attach, but heard syndrome, more generic);

• correlation metrics between real and synthetic data;

• synthetic data is a chance to create non existed things (like trees on empty roads etc)

New terms: Catalitic practicts, article

LLM and Knowledge Graphs: A case study in Blue(y) by Stav Shamir

• trained model returns vector, our requests to trained vectors, most closed vectors are outputs to request;

• vector embedding turns words to nums;

• graphs with connections becomes complicated neutral networks;

• schemas should not be complex (so you may not receive any answer) and no so easy (so you do not need LLM for that);

• representing LLM connections as graphs help to see them as full pic

Live in Data Wild West: the data contracts sheriff by Tal Peretz

• data issues brings real leaks and legalsituations;

• data quality isthe most important things care about;

New terms: data contracts, DataHub (reviewer) , AvroScheme, source of truth, fields deprecations (silent vs …), scheme evolution, schema.yml, DBT expectation (tests on DB), assertion, elementary

Navigating the Uncharted: Ensuring Prompt Quality in the Age of Language Models by Ortal Ashkenazi

Several way to ensure prompt quality:

• manually: efficient, but no fast ;

• LLM-as-a-judge: fast, but depends on prompt and LLM version;

• leveraging NLM for prompt verification, entity recognition:

  • cons: no models for every thing;

• automate metrics validation;

• unchecked prompts may contain hidden dangerous

New terms: NLP, NLM

Exploring the Depths of Apache Iceberg's Metadata Capabilities by Amit Gilad

• there are another modern and cloud solutions to store data (data lakes) instead of backup disks

Threat Hunting Powered by Efficient and Straightforward anomaly detection on your data lake by Ori Nakar

• anomalies are something that deviates from what is standard, normal, or expected, but should be define per specific case;

• running query with json;

• SQL still the faster way to detect the anomalies;

• LLM with RAG (sequel statement) runs text queries without syntaxes dependence.

New terms: data scanning, CVE, DDoS

Dating with a super model: why good prompt engineering for data monitoring requires some flirting by Reut Vilek

• no long prompts, break it into few smalls;

• take it step by step;

• give references, examples of sites, images etc;

• parsing and analyze results to regenerate in better way

New terms: FML, NoCodeTool

Existed to try the discovers in my daily works!

After few postpones PyData finally hayaData has place in Tel Aviv on 4th of November 2024.

I would like to leave here my notes per speech I attended:

The Dangerous Data Anonymization by Ran Bar Zik

• Anonymization means take a data and remove all private details

• There are different anonymization techniques:

  • masking;

  • dynamic masking;

  • aggregation (age as range, general names);

  • pseudonymisation or reversible anonymization;

  • differentiational - add noise, mixed data;

• more privacy-less accuracy;

• "You are holding a data, think before release it!”

New terms: pii masking, AnonyPyx **python libs; Hippa, CCPA

Unveiling the Journey of Natural Language Processing (NLP): Milestones, Limitations, and Practical Applications by Ortal Ashkenazi

• MLM masked language model;

• transfer learning;

• learning types: zero shot, one shot, few shots;

• LLM has limiting for static data (if there are no slang words in training, model doesn’t understand request with that words);

• RAG overcoming knowledge limitation;

• Multimodal image integration

New terms: NLIK

A Shallow Introduction to Self-Attention by Alon Oring

• Naive self-attention: how similar are embeddings (as word embeddings), score one to others;

• Recurrent Neutral Network;

• Contextualizing embedding

New terms: RNN, QKV

Securing Language Models Against Prompt Injection with the Powerful LangChain Framework by Michael Ethan Levinger

• security through advising testing (LLM learns bad things;

• Rebuff - moderation endpoint detecting and managing harmful content;

• DAN stand for do anything now (in prompt meaning);

• indirect prompt while loading from external source: from docs, cv;

• if you are using and ask with ‘labels’ in model it interact better (IBM models example)

New terms: Lakera

Ibis framework - Making data science work at any scale by Omri Fima

• https://github.com/thegreymatter/ibisframeworks allows with difference data source in one place (for instance join csv and table in database);

• pip install ibis;

• ibis.con.combile - describe query;

• ibis udf

• ibis.udf.scalar.pyarrow ->tokens