About
As a seasoned leader in AI, ML, and Data Engineering, I thrive at the intersection of…
Articles by Abdullah Cetin
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Summary of First Ankara Big Data Meetup
Summary of First Ankara Big Data Meetup
Hi, We have completed our first Ankara Big Data Meetup on 5th of August at Bilkent Cyberpark. We had over 120 attendees…
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2 Great Speakers from Silicon Valley on Big Data
2 Great Speakers from Silicon Valley on Big Data
Hi, We have scheduled our first meetup. It’s going to be on 5th of August at Bilkent Cyberpark, Ankara.
2 Comments
Activity
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Crazy week. If anyone wants to catch up on GTC tomorrow - ping me!
Crazy week. If anyone wants to catch up on GTC tomorrow - ping me!
Liked by Abdullah Cetin Cavdar
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Artificial intelligence is at the heart of innovation, but building and managing AI infrastructure remains a challenge for many organizations. At…
Artificial intelligence is at the heart of innovation, but building and managing AI infrastructure remains a challenge for many organizations. At…
Liked by Abdullah Cetin Cavdar
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New update will land in the next 72 hours. Not much to say, we're literally building the future of design... Watch it for yourself, and let me know…
New update will land in the next 72 hours. Not much to say, we're literally building the future of design... Watch it for yourself, and let me know…
Liked by Abdullah Cetin Cavdar
Experience
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Education
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Activities and Societies: Member at IEEE METU
- Theoretical and practical aspects of modelling, virtual environments, and computer simulation
- To foster and support interdisciplinary research in the field of modelling and simulation
- To meet modelling and simulation needs of defence industry, and public and private sectors in general -
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Activities and Societies: Member & Chairman at Computer Club
- Theory, principles, practice and know-how of computing for the critical analysis, design, evaluation, and improvement of computer-based systems in the contexts of computers and man, computers and the society, computers and the industry and services
Licenses & Certifications
Publications
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A Case Study on Interoperability Between Simulation and Command Control Systems
SAVTEK
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Projects
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T2 Big Data Hackathon
- Present
Turkey's first big data hackathon.
Assemble a team.Learn & Share. Make friends.
Code Challenge and Project Challenge!
Workshops & talks on Big Data and Data Science.
Prizes, free goodies, free data science books.
Free food & drinks, loads of snacks.
Twitter hashtag #T2HackathonOther creators -
Honors & Awards
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Databricks / O'Reilly Certified Developer for Apache Spark
Databricks
I have successfully completed the developer certification exam for Apache Spark with a score of 85/100.
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Udemy Hackathon
Udemy
1st place in "Most Innovative" product category in Udemy internal hackathon with "Class or Pass" project which is a Tinder like mobile app for discovering new courses.
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AWS Certified Solution Architect - Associate
Amazon Web Services
I have successfully completed the AWS Certified Solutions Architect - Associate exam.
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Big Data Hackathon
Turkcell
1st place in Turkcell Big Data Hackathon Code Challenge
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Cloud Based Mobile Hackathon
T2
1st place in T2 Cloud Based Mobile Hackathon (http://t2hackathon.com) with MovieCircle which is a social movie discovery and recommendation application.
Languages
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Turkish
Native or bilingual proficiency
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English
Full professional proficiency
More activity by Abdullah Cetin
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🚀 Sharing my AI Stack 🚀 I love asking folks how they use AI in their day-to-day jobs, and sharing my stack with everyone! AI Tools I Use…
🚀 Sharing my AI Stack 🚀 I love asking folks how they use AI in their day-to-day jobs, and sharing my stack with everyone! AI Tools I Use…
Liked by Abdullah Cetin Cavdar
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A Global Vision, One Community: Treeo VC’s Founders Unite in SF 🌍✨ Last week, Treeo VC hosted our first-ever portfolio dinner in San Francisco at…
A Global Vision, One Community: Treeo VC’s Founders Unite in SF 🌍✨ Last week, Treeo VC hosted our first-ever portfolio dinner in San Francisco at…
Liked by Abdullah Cetin Cavdar
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Investor & Founder Mixer | March 17 | SHACK15 We’re excited to bring together two powerhouse startup ecosystems—Istanbul & Silicon Valley—for an…
Investor & Founder Mixer | March 17 | SHACK15 We’re excited to bring together two powerhouse startup ecosystems—Istanbul & Silicon Valley—for an…
Liked by Abdullah Cetin Cavdar
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Explore more posts
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Clemens Vasters
I like the consistency and simplicity and extensibility of the Apache #Avro schema model. I also think that the JSON encoding defined in the Apache Avro specification is a comical mistake. It's so unintuitive, that none of you would be able to guess how to write a conformant JSON document reading an Avro schema. The JSON document you would quite likely write intuitively instead would rather conform to this alternate JSON encoding for Apache Avro schemas: "Plain JSON" https://lnkd.in/eEHPbJrj
2 Comments -
Anthony Giza
Increase Model Flexibility and ROI for GenAI App Delivery With Kubernetes https://buff.ly/4e7WlZW As with past technology adoption journeys, initial experimentation costs eventually shift to a focus on ROI. In a recent post on X, Andrew Ng extensively discussed GenAI model pricing reductions. This is great news, since GenAI models are crucial for powering the latest generation of AI applications. However, model swapping is also emerging as both an innovation enabler, and a cost saving strategy, for deploying these applications. Even if you've already standardized on a specific model for your applications with reasonable costs, you might want to explore the added benefits of a multiple model approach facilitated by Kubernetes. A Multiple Model Approach to GenAI A multiple model operating approach enables developers to use the most up-to-date GenAI models throughout the lifecycle of an application. By operating in a continuous upgrade approach for GenAI models, developers can harness the specific strengths of each model as they shift over time. In addition, the introduction of specialized, or purpose-built models, enables applications to be tested and refined for optimal accuracy, performance and cost.
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Seckin Dinc
Topics in Kafka are divided into partitions, which means that a single topic’s log is split across multiple logs distributed across different Kafka brokers. This partitioning allows the workload of storing, writing, and processing messages to be distributed across many nodes in the cluster, enhancing scalability and performance. If you are interested in Kafka Partitioning, don't hesitate the new blog post https://lnkd.in/dSmancXf #data #dataengineering #kafka #confluent
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Shiv Iyer
Materialized views in #PostgreSQL store pre-computed results of frequent, complex queries to boost performance. They are periodically refreshed for up-to-date data but require careful management of refresh frequency, disk space, and resources. #PostgreSQLDBA @MinervaDB https://zurl.co/7ixU
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Dipankar Mazumdar, M.Sc
I presented about Apache XTable's architecture last week at LinkedIn's Big Data Meetup. For context, XTable is an omni-directional translation layer on top of open table formats such as Apache Hudi, #ApacheIceberg & Delta Lake. It is NOT ❌ a new table format! Essentially what we are doing is this: SOURCE ---> (read metadata) ---> XTable's Model ---> write into TARGET We read the metadata from the SOURCE table format, put it as a unified representation & write the metadata in the TARGET format. * Note that we are only touching metadata, not the actual data files (such as #Parquet) with XTable. Let's breakdown its architecture. XTable’s architecture consists of three key components: 1. Conversion Source: ✅ These are table format specific modules responsible for reading metadata from the source ✅ They extract information like schema, transactions, partitions & translate it into XTable’s unified internal representation 2. Conversion Logic: ✅ This is the central processing unit of XTable ✅ It orchestrates the entire translation process, including initializing of all components, managing sources and targets, among other critical things 3. Conversion Target: ✅ These mirror the source readers ✅ They take the internal representation of the metadata & maps it to the target format’s metadata structure Read the paper (link in comments) for details. #dataengineering #softwareengineering
5 Comments -
Anthony Giza
Use AWS Generative AI CDK Constructs To Speed up App Development https://buff.ly/3xIbVwm In this blog, we will use the AWS Generative AI Constructs Library to deploy a complete RAG application composed of the following components: Knowledge Bases for Amazon Bedrock: This is the foundation for the RAG solution. OpenSearch Serverless collection: It supports the vector search collection type that provides similarity search capability. An S3 bucket: This will act as the data source for the Knowledge Base. AWS Lambda function (written in Python) along with an API Gateway that uses the RetrieveAndGenerate API to query the knowledge base and generate responses from the information it retrieves.
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Gopi Subramanian
A minimal QA NLT pipline using Adalflow, with locally hosted Microsoft Phi3 and Alibaba's Qwen using Ollama. Adalflow is a library to build NLT pipelines. The "minimal abstraction" design principle is highly useful for quick prototyping, especially its similarity with nn.Module in pytorch and Jinja2 templating for prompts. Ollama AdalFlow
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Anthony Giza
Real-Time GenAI With RAG Using Apache Kafka and Flink To Prevent Hallucinations https://ift.tt/KrAWjxp How do you prevent hallucinations from large language models (LLMs) in GenAI applications? LLMs need real-time, contextualized, and trustworthy data to generate the most reliable outputs. This blog post explains how RAG and a data streaming platform with Apache Kafka and Flink make that possible. A lightboard video shows how to build a context-specific real-time RAG architecture. Also, learn how the travel agency Expedia leverages data streaming with Generative AI using conversational chatbots to improve the customer experience and reduce the cost of service agents. What Is Retrieval Augmented Generation (RAG) in GenAI? Generative AI (GenAI) refers to artificial intelligence (AI) systems that can create new content, such as text, images, music, or code, often mimicking human creativity. These systems use advanced machine learning techniques, particularly deep learning models like neural networks, to generate data that resembles the training data they were fed. Popular examples include language models like GPT-3 for text generation and DALL-E for image creation. via DZone AI/ML Zone https://dzone.com/ai-ml September 11, 2024 at 08:00AM
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Anthony Giza
Unleashing the Power of Redis for Vector Database Applications https://buff.ly/3XCLliI In the world of machine learning and artificial intelligence, efficient storage and retrieval of high-dimensional vector data are crucial. Traditional databases often struggle to handle these complex data structures, leading to performance bottlenecks and inefficient queries. Redis, a popular open-source in-memory data store, has emerged as a powerful solution for building high-performance vector databases capable of handling large-scale machine-learning applications. What Are Vector Databases? In the context of machine learning, vectors are arrays of numbers that represent data points in a high-dimensional space. These vectors are commonly used to encode various types of data, such as text, images, and audio, into numerical representations that can be processed by machine learning algorithms. A vector database is a specialized database designed to store, index, and query these high-dimensional vectors efficiently.
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Alessandro Benedetti
The first milestone to integrate Large Language Models with Apache Solr has been set! This introduces in the upcoming Solr 9.8 release the ability to use external text to vector inference models, directly in your textual query parser! This means that you will be able to run semantic search via text (and Solr will encode to vector and run vector search behind the scenes). This is a first step and a huge enabler, which I've been happy to contribute! I hope that this will open the doors to many more experiments, applications, bug fixes and new contributors! This wouldn't have been possible without the support of Cedric Ulmer and the help of fellow committers David Smiley, Christine Poerschke, Eric Pugh and Christos Malliaridis! What's coming next? In early 2025 the indexing side, to give the possibility of encoding vectors transparently, while indexing. And then? Retrieval Augmented Generation, from natural language, to structured query, cross encoders rerankers and more! Stay tuned!
1 Comment -
Craig Kerstiens
Postgres for analytics has always been a huge question mark. By using PostgreSQL's extension APIs, integrating DuckDB as a query engine for state-of-the-art analytics performance without forking either project could Postgres be the analytics database too? Bringing an analytical query engine into a transactional database system raises many interesting possibilities and questions. What makes these workloads and system architectures so different? What could bringing them together mean? My colleague Marco Slot states the above beautifully. But he doesn't just stop there, this deep dive is going to be a seminal piece for many years to come on the convergence of analytical workloads on Postgres: https://lnkd.in/gfKDgBu9
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Shiv Iyer
Bloom indexes in #PostgreSQL efficiently test for the presence of a set of values, especially useful for multi-column queries where traditional indexes may be less efficient. They are ideal for checking combinations across columns without needing explicit indexes on each. #PostgreSQLPerformance @MinervaDB #dba https://zurl.co/OKDl
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Shiv Iyer
In #PostgreSQL, hints and costs guide query optimization by providing the optimizer with additional execution insights. Hints suggest execution plans, while cost adjustments help refine query performance when the default plan isn’t optimal. #PostgreSQLPerformance @MinervaDB #dba https://zurl.co/3JMn
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Shiv Iyer
In #PostgreSQL, hints and costs guide query optimization by providing the optimizer with additional execution insights. Hints suggest execution plans, while cost adjustments help refine query performance when the default plan isn’t optimal. #PostgreSQLPerformance @MinervaDB #dba https://zurl.co/3JMn
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Shiv Iyer
Query performance latency in #Amazon RDS for #MySQL involves analyzing query execution times using #SQL scripts. DBAs can identify and troubleshoot latency issues to optimize database performance by querying the performance schema or slow query logs. #dba #dbaas @MinervaDB https://zurl.co/1Ybx
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EDB
Postgres just got smarter—and EPAS 17 takes it even further. Thanks to the PostgreSQL community (and maybe a few hardworking magic elves?), EPAS 17 scales up to 2x better than its predecessor. Think better WAL performance, smarter partitioning, and CPU utilization gains that will make your hardware feel brand new. We tested it with HammerDB under intense workloads... no IO bottlenecks, no user delays—just raw transactional power. The result? 132% better performance at peak concurrency compared to EPAS 16. Dive into the new blog by Alessandro F. for all the technical details, benchmarks, and why EPAS 17 is the upgrade your database deserves ➡️ https://bit.ly/3VrT6q1 #EDBPostgresAI #JustSolveItWithPostgres #postgresql #DatabasePerformance #OpenSource #HammerDB #performancegains Alyssa Mazziotta
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