#Ai Functions Databricks

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#Ai Functions Databricks Reel by @rotichdata.ai - It's a quite chilly morning and you need to run the company's system first we're building datasets with bigdata.com one of the biggest AI powered data
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@rotichdata.ai
It’s a quite chilly morning and you need to run the company’s system first we’re building datasets with bigdata.com one of the biggest AI powered data company we use API keys from different companies including Claude and OpenAI we have a total of 3,696 chunks and we need to utilize the available tokens so we perform token optimization #ai #datascientist #datascience #bigdata #aiengineer
#Ai Functions Databricks Reel by @datamindshubs - Want to break into AI the RIGHT way? Save this. 🔖 Step 1: Learn Python basics Variables, loops, functions, lists, dictionaries. Build tiny scripts (c
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@datamindshubs
Want to break into AI the RIGHT way? Save this. 🔖 Step 1: Learn Python basics Variables, loops, functions, lists, dictionaries. Build tiny scripts (calculator, number guesser). Tools: Python Step 2: Understand data (non-negotiable) Read, clean, analyze datasets. Tools: CSV files, SQL basics, Pandas, NumPy Step 3: Build a tiny project ASAP Something small that actually works. Examples: price predictor, sentiment analyzer, basic chatbot Tools: Small dataset, Jupyter Notebook Step 4: Learn ONE AI concept deeply Know how a model learns, predicts, and fails. Tools: Linear/Logistic Regression or basic Neural Network, scikit-learn (Depth > variety) Step 5: Learn modern AI tools See how AI is used in real products. Tools: APIs (LLMs) Step 6: Show your work publicly Explain what you built in simple language. Platforms: GitHub (code + README), LinkedIn (project breakdown) Step 7: Repeat with harder problems Upgrade the SAME project. Use bigger datasets, better features, clear evaluation metrics. 👇 Follow @datamindshubs for more 🚀 #aiapps #ai #python #programming #artificialintelligence
#Ai Functions Databricks Reel by @datamindshubs - Want to break into AI the RIGHT way? Save this. 🔖 Step 1: Learn Python basics Variables, loops, functions, lists, dictionaries. Build tiny scripts (c
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@datamindshubs
Want to break into AI the RIGHT way? Save this. 🔖 Step 1: Learn Python basics Variables, loops, functions, lists, dictionaries. Build tiny scripts (calculator, number guesser). Tools: Python Step 2: Understand data (non-negotiable) Read, clean, analyze datasets. Tools: CSV files, SQL basics, Pandas, NumPy Step 3: Build a tiny project ASAP Something small that actually works. Examples: price predictor, sentiment analyzer, basic chatbot Tools: Small dataset, Jupyter Notebook Step 4: Learn ONE AI concept deeply Know how a model learns, predicts, and fails. Tools: Linear/Logistic Regression or basic Neural Network, scikit-learn (Depth > variety) Step 5: Learn modern AI tools See how AI is used in real products. Tools: APIs (LLMs) Step 6: Show your work publicly Explain what you built in simple language. Platforms: GitHub (code + README), LinkedIn (project breakdown) Step 7: Repeat with harder problems Upgrade the SAME project. Use bigger datasets, better features, clear evaluation metrics. 👇 Comment “SENIOR” and I’ll send you a masterpiece website! Follow @datamindshubs for more 🚀 #aiapps #ai #python #programming #artificialintelligence
#Ai Functions Databricks Reel by @dataanddreamscapes - Day 5: Built a Binary Purchase Label, Joined with Silver Features, Split train/test, and Validated Class Distribution. Moving from Feature Engineering
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@dataanddreamscapes
Day 5: Built a Binary Purchase Label, Joined with Silver Features, Split train/test, and Validated Class Distribution. Moving from Feature Engineering to AI System Preparation. #databrickswithidc #DataEngineering #databricks #featureengineering #training Activity Log: https://pewter-porch-d86.notion.site/Databricks-14-Days-AI-Challenge-2-30947a4b88b880a5a363f2181e43601e?source=copy_link
#Ai Functions Databricks Reel by @datasciencebrain (verified account) - Master these 10 libraries and companies will pay you $150K+ to build their AI products. Saved this yet? 📌 � Want the complete 92 page cheat sheet w
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@datasciencebrain
Master these 10 libraries and companies will pay you $150K+ to build their AI products. Saved this yet? 📌 � Want the complete 92 page cheat sheet with code examples? 👉 Follow me(so i can message) & Comment , I'll send it to you in DM📥 1. LangGraph Build stateful AI agents with complex workflows 2. Instructor Structured outputs from LLMs using Pydantic 3. LlamaIndex RAG systems for document Q&A 4. OpenAI SDK Access GPT models with function calling 5. FastAPI Deploy AI models as fast APIs 6. Anthropic SDK Claude integration for complex reasoning 7. DSPy Auto-optimize prompts like ML models 8. ChromaDB Simple vector database for embeddings 9. LiteLLM Unified interface for 100+ LLMs 10. Transformers Access & fine-tune open-source models 📲 Follow @datasciencebrain for Daily Notes 📝, Tips ⚙️ and Interview QA🏆 . . . . . . [dataanalytics, artificialintelligence, deeplearning, bigdata, agenticai, aiagents, statistics, dataanalysis, datavisualization, analytics, datascientist, neuralnetworks, 100daysofcode, llms, datasciencebootcamp, ai] #datascience #dataanalyst #machinelearning #genai #aiengineering
#Ai Functions Databricks Reel by @engineerofdata - Fabric environment audit? Done! AI's got your back, expanding tree views & finding your pipelines. Subscribe at engineerofdata.substack.com for more d
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@engineerofdata
Fabric environment audit? Done! AI's got your back, expanding tree views & finding your pipelines. Subscribe at engineerofdata.substack.com for more data engineering insights. #DataEngineering #MicrosoftFabric #DataPipelines #ArtificialIntelligence
#Ai Functions Databricks Reel by @datavisionhub - Most ML projects don't fail because of bad models… They fail because of bad data 💻📊 In this presentation, I shared the real problems behind data lab
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@datavisionhub
Most ML projects don’t fail because of bad models… They fail because of bad data 💻📊 In this presentation, I shared the real problems behind data labeling: ❌ Inconsistent labels ❌ No clear guidelines ❌ Conflicting opinions All of these confuse AI and reduce accuracy. 💡 Remember: Garbage In = Garbage Out Strong AI starts with strong data ✅ Keep learning. Keep building. Keep growing 🚀 #MachineLearning #DataScienceLife #AIEngineer
#Ai Functions Databricks Reel by @matt_forrest - Working with massive datasets shouldn't mean waiting hours for downloads. ⏳ If you're working with spatial data, cloud-native formats stored in buck
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@matt_forrest
Working with massive datasets shouldn't mean waiting hours for downloads. ⏳ If you're working with spatial data, cloud-native formats stored in buckets let you stream exactly what you need, instantly. No massive downloads required. #TechCommunity #DataScience #SpatialData #CloudNative #DevLife #DataArchitecture
#Ai Functions Databricks Reel by @mrk_talkstech (verified account) - How I do Data Engineering tasks using AI? #AIForDataEngineering #DataEngineering #AIAutomation #GenAI #ModernDataStack AI for data engineering, AI d
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@mrk_talkstech
How I do Data Engineering tasks using AI? #AIForDataEngineering #DataEngineering #AIAutomation #GenAI #ModernDataStack AI for data engineering, AI data pipelines, automated ETL, schema generation with AI, SQL query generation, PySpark code generation, data validation automation, pipeline debugging with AI, log analysis using AI, data modeling assistance, documentation generation, code refactoring, test case generation, data quality checks, metadata extraction, AI copilots, prompt engineering for data, AI workflow automation, cloud data automation, productivity boost with AI
#Ai Functions Databricks Reel by @curlybracketstechnology (verified account) - Built on {D.E.A.T.H.}™ Data is noisy. We don't guess. We structure. ⸻ ⚙️ {D.I.E.}™ for File Automated cleaning + AI insight generation for CSV, XM
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@curlybracketstechnology
Built on {D.E.A.T.H.}™ Data is noisy. We don’t guess. We structure. ⸻ ⚙️ {D.I.E.}™ for File Automated cleaning + AI insight generation for CSV, XML, EDI. CRON-enabled. API-ready. ⸻ 📊 {D.I.E.}™ for Workbook Macro-embedded intelligence for Excel & Sheets. Auto insights. Auto charts. Zero chaos. ⸻ 🗄 {D.I.E.}™ for SQL Database-native transformation engine. Creates insight tables, views & APIs automatically. ⸻ 🌐 {D.I.E.}™ for NoSQL JSON interpretation + big data harmonization. Dynamic structure → Decision-ready form. ⸻ 📈 {D.I.E.}™ for BI Power BI & Tableau integrity enforcement. Clean pipelines only. ⸻ 🤖 {D.I.E.}™ for AI Protect AI from dirty data. Pre-model validation layer for ChatGPT & Copilot ecosystems. ⸻ 🔐 {D.I.E.}™ ULTIMATE Source code + commercial licensing. Enterprise-grade ownership. ⸻ 🧠 Ultimate {D.E.A.T.H.}™ Decision-Enabling Analytical Transformation Hub. It doesn’t decide. It prepares. Deterministic. Replay-safe. Contract-driven. The foundation beneath every serious decision engine. #curlybracketstechnology #arcreactor #die #death
#Ai Functions Databricks Reel by @gojutechtalk - Rules-based vs. stats-based algorithms: Which should you use? Addition? Rules. Dog photo? Stats. Simple as that. #AlgorithmDesign #Algorithms #Rules
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@gojutechtalk
Rules-based vs. stats-based algorithms: Which should you use? Addition? Rules. Dog photo? Stats. Simple as that. #AlgorithmDesign #Algorithms #RulesBased #StatsBased #Stats #Rules #MachineLearning #MachineProgramming #SoftwareEngineering #DataScience #Science #AI #Tech #Research #LLMs #SLMs

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