Thomas Hirschmann ✨’s Post

🌟 What is the relation among the booming terms like Big Data, Machine Learning, and AI? How do these things work together? 🌟 In the current technology world, three powerful forces converge to revolutionize the way we understand and interact with data: Big Data Engineering, Machine Learning (ML), and Artificial Intelligence (AI). Let's dive into the relationship among them: 🔍 Big Data Engineering: Think of it as the backbone, the infrastructure that enables the storage, processing, and management of vast volumes of data. Big Data Engineers architect the systems that handle petabytes of information, ensuring its accessibility and scalability. 🧠 Machine Learning: Here's where things get fascinating. Machine Learning algorithms empower systems to learn from data, identify patterns, and make predictions or decisions without being explicitly programmed. It's the brain behind data-driven insights and automation. 🤖 Artificial Intelligence: AI is the culmination of Big Data Engineering and Machine Learning. It encompasses systems that can reason, learn, and adapt, mimicking human intelligence to solve complex problems. AI extends beyond pattern recognition to decision-making, natural language understanding, and more. 🔄 The Loop: Big Data feeds into Machine Learning algorithms, providing the raw material for learning. As models process data, they generate insights, which in turn inform AI systems to make intelligent decisions or take actions autonomously. This loop of data ingestion, analysis, and action is the heartbeat of modern AI-driven solutions. 💡 The Impact: Together, these disciplines revolutionize industries. From personalized recommendations on streaming platforms to optimizing supply chains and healthcare diagnostics, the synergy of Big Data, Machine Learning, and AI fuels innovation and drives efficiency. In a nutshell, Big Data Engineering lays the groundwork, Machine Learning extracts value, and Artificial Intelligence orchestrates intelligent responses. It's a journey from data to wisdom, transforming the way we perceive and interact with the world. #BigData #MachineLearning #AI #DataScience #Innovation #Dataengineering TrendyTech #cloud Sumit Mittal #ArtificialIntelligence

🚀 Attention AI Developers, Data Scientists, and AI Engineers! 🌟 Are you working on building and deploying generative AI applications?  working on building RAGs? Are you struggling with collecting human feedback (need a front end?) , evaluating quality metrics, identifying issues clearly, and optimizing configurations in your Gen AI projects? The new Mosaic AI Agent Framework and Agent Evaluation tools are here to streamline your workflow. Integrated with MLflow and the Databricks Data Intelligence Platform, they ensure efficient workflows and comprehensive lifecycle management. Don't miss out on enhancing your Gen AI development process! #AI #DataScience #GenerativeAI #Innovation #TechNews

In today's fast-paced world, more businesses are turning to AI to revolutionize their operations and gain a competitive edge. However, success in AI isn't just about having the most data or sophisticated technologyit's about bringing the right people into your team. From AI strategists to data scientists, and from ethical AI officers to machine learning engineers, having a diverse set of skills and perspectives is crucial. What steps is your company taking to build a winning AI team? Share your thoughts below! #aicompliance #airisk #aistrategicimplementation

Building a good ML model is one challenge. Then, deploying and running it is another one. And then there's the monitoring - how do we know when a model's predictions start dropping in quality? There has to be a process to find out when that happens. What if someone's affected by the model's prediction, and wants to find out why? There needs to be ways to answer that, other than just "the neurons speaketh, we dost not know why, we trusteth their great wisdom". This why we're seeing AI/ML roles being dividing into several specializations - the "data scientist" builds the model, the "ML engineer" productionizes the code, and the "MLops engineer" looks after the environment where it's deployed. Have I got those descriptions right? #ai #ml #artificialintelligence

A big problem when transitioning to an AI position is getting real-world experience. It's the chicken and egg: I can't get into AI, then I don't get experience, so I can't get into AI. Does this ring a bell? In particular, many people are rejected for AI positions because of the ML solutions they have used in small datasets. In a way it makes sense. The problems you face when doing ML with petabytes of data are very different to the ones you face using a Kaggle dataset. The most effective way I found to address the problem of getting into AI without having worked in AI before is what I call the bridge story. A bridge story is a bridge between your real-world experience of your past position and your knowledge of AI. For example, if you are a Software Engineer, your real-world experience is building robust solutions, aligning stakeholders, executing tasks, collaborating effectively, communicating results, and adapting to changes. Then you mix that experience with your knowledge of ML algorithm development, deployment, monitoring, management of big datasets, etc. A strong bridge story provides clear evidence that you have the knowledge and real-world experience to be successful in an AI job. If you want to improve your knowledge on managing Petabytes of data in real-time, SingleStore about the topic. When: July 25, 2024 | 10:00am PDT Link: https://lnkd.in/dQGhVRp9

Everything you browse :), you can see "do x with AI". Being a nerdy data scientist myself, I though think of AI in business a bit differently. I believe current Gen AI is very, very good at 2 biz use cases: - Do any visual cognitive / rule-based decision-making in the blink of an eye, which means you can get a million repeats of an otherwise ops intensive and mundane task within a sec. - Understand definite patterns based on data indexing, a bit of word vectoring extremely well especially in straight context scenarios. Not very good at "creative" bits, creating "context" out of thin air as is the wont of us humans is still a bridge too far. So far with me? If these also resonate with you, then ping me directly. We at Arthmate are trying to bolster our AI game with high impact use cases and are looking for a Gen AI Data Scientist with a bit of chops, but a very high degree of exploratory wanderlust. A lot of what is worth knowing in this domain, is really known yet and is updated everyday. So prior experience especially rigid ones is actually a burden. Ashok Sharma and I are looking forward to hearing from a bunch of you! #fintechhiring #GenerativeAI #startuphiring

Excellent article from Cindi Howson about the future of data analytics and how technology, sound governance and general democratization of data are setting up the next wave of successful Analytic platforms. Personally, I think this quote about providing access to larger groups within an org is where I see these platforms excelling and what excites me the most. Awesome article! “Self-service BI accelerates decision-making and fosters a culture of data-driven innovation and collaboration within organisations. By democratising access to data and insights, these platforms enable employees at all levels to make informed decisions, drive performance improvements, and unlock new growth opportunities.” #dataanalytics

Gain expert insights from Swaroop Shivakumar, Sr. Data Engineer, on the future of #AI and #AgenticFrameworks! 🚀 In this blog, he explores how AI is moving beyond language models to make autonomous decisions, transforming industries like #Healthcare and #Finance. Discover how these advancements are shaping the next era of #Automation and #Innovation. Check out Swaroop's in-depth breakdown— https://shorturl.at/HWdC4 #AIRevolution #TechInsights #MachineLearning #ArtificialIntelligence #DataEngineering #FutureOfTech Webknot Technologies

Supporting developers in building AI workloads: A guide to success awaits! Dive into expert tips from @thenewstack. https://oal.lu/EFhEe #AIDevelopment #DeveloperSupport

Here is my article showcasing how AI engineers and developers can leverage the Azure Document Intelligence tool to transform unstructured data into a structured format. This is particularly useful for organizations managing invoices in formats like PDF, Word, or Excel and seeking to store all PDF invoices in an SQL database. The article guides developers in extracting key-value pairs and tables from invoices efficiently. The project's working code is available on GitHub for download and can be customized to meet specific business needs. #AzureDocumentIntelligence #NLP #UnstructuredData #PrebuiltModel #AI #Invoice