Docker

Explore resources related to docker to help implement AI solutions for your business.

[D] Docker Containers (Python) vs Direct C++ Implementation for AI on Edge Devices, Performance Tradeoffs

I'm exploring the best approaches for deploying AI products to production, especially on edge devices like smart home systems and cameras. I'm wondering about the tradeoffs between using Docker containers, python code versus implementing Inference code directly in C++ for these scenarios. Key considerations: Performance: Does the overhead of Docker significantly impact AI model performance on resource-constrained edge devices? Hardware utilization: Can C++ implementations better leverage specific hardware features for AI acceleration? I'd love to hear from those with experience deploying AI models to edge devices. What approach have you found most effective, and why? Are there specific use cases where one method clearly outperforms the other?

#reddit #docker #containers

MichaelBaranov•This week

No-code AI platform?

Hey there, A group of like-minded people and I are working on a no-code AI platform. Our goal is to make machine learning technologies accessible to people without any specific knowledge. The workflow would look like the following: Select a task and data types. Upload data (with annotation or annotate it in our platform). Wait for a model to be trained automatically (up to 30-40 minutes). Use the created model (through API, through integration with other no-/low-code platforms, or as a docker image). What do you think about our idea? Would anybody use it? I'd really appreciate it if you could share your thoughts.

#reddit #no-code #platform?

What do you think about no-code AI development

MichaelBaranov•This week

What do you think about no-code AI development

A team of like-minded people and I are working on a no-code AI development platform. Our goal is to make deep learning technologies accessible for everyone, not just for machine learning engineers and big companies. We've decided to start with computer vision tasks. The workflow would look like the following: Select task (image classification, object detection, image tagging). Upload data (with annotation or annotate it in our platform). Wait for a model to be trained automatically (about 10 minutes). Integrate the created model into your product (through API, docker image, or through integration with other no-/low-code platforms). What do you think is there any future for no-code AI development platforms? I'd really appreciate it if you could share your thoughts.

#reddit #think #about

What is the best setup to implement for Ai

raven_29•This week

What is the best setup to implement for Ai

first of all I'm a fresh machine learning eng(not working yet) and my laptop system running on 128gb ssd + usual 1tb hdd + gtx1050. I used to install python locally and its libraries (from numpy and pandas to tensorflow and pytorch) with vscode (instead of anaconda) and I was happy about that. whenever I do project and want to deploy it i used pepenv. I got problems in storage when it comes to docker or cuda or wsl recently I got an storage upgrade to 512gb ssd and looking for new ideas and approaches about my new instlations What Should I do for each aspect use anaconda and make different environments (I feel this gonna drain storage specially I don't know what should each env contain) Shoul I use docker for windows or install wsl and install docker through it. Same for cuda should i install cuda native windows installation or intall it using wsl I don't try how to use programs in wsl for ex. I don't know how to use cuda on local development if it installed in wsl same as docker I know my gtx1050 won't be effecient when it comes to cuda and maybe more effective to use colab or kaggle gpu. but it's better practice i guess to develop on cuda.

#reddit #best #setup

Randomly asked ChatGPT and Claude for a 4 year roadmap for an ML Engineer

Brilliant_Fishing110•This week

Randomly asked ChatGPT and Claude for a 4 year roadmap for an ML Engineer

Title, Is it actually a good plan ?? If no, why not ?? \\🚀 4-Year Roadmap to Becoming a High-Earning ML Engineer & Entrepreneur\\ \\(With Smartwork & Realistic 60-70% Execution Feasibility)\\ \\🟢 Year 1: Strong Foundation & Initial Projects (0-12 Months)\\ 🎯 \\Goal: Master Python & ML Fundamentals\\ \\🔹 1-4 Months (Python & Math Strengthening)\\ ✅ Python Mastery \- Daily LeetCode Easy problems (minimum 2) \- Build automation projects \- NumPy & Pandas mastery \- DSA fundamentals ✅ Mathematics Foundation \- Linear Algebra basics \- Statistics fundamentals \- Basic calculus concepts ✅ First Mini-Hackathon Participation \- Join beginner-friendly hackathons \- Focus on Python-based challenges \- Team up with other beginners 💡 \\Smart Move:\\ \- Join Discord/Slack hackathon communities \- Practice collaborative coding \- Build network with fellow participants \\🔹 5-8 Months (ML Foundations)\\ ✅ Machine Learning Basics \- Supervised Learning \- Model evaluation \- Feature engineering \- scikit-learn projects ✅ Participate in 2-3 ML Hackathons \- Kaggle Getting Started competitions \- Local ML hackathons \- University hackathons ✅ Start LinkedIn & GitHub Portfolio 💡 \\Smart Move:\\ \- Document hackathon experiences \- Share learnings on LinkedIn \- Focus on completion over winning \\🔹 9-12 Months (Deep Learning Introduction)\\ ✅ Basic Deep Learning \- Neural network fundamentals \- PyTorch basics \- Computer vision tasks \- Basic NLP ✅ Advanced Hackathon Participation \- AI/ML specific hackathons \- Team lead in 1-2 hackathons \- Start mentoring beginners \\🔵 Year 1 Expected Outcome (60-70% Execution)\\ ✔ \\Strong Python & ML foundations\\ ✔ \\5-6 hackathon participations\\ ✔ \\Active GitHub (100+ commits)\\ ✔ \\Growing LinkedIn (300+ connections)\\ 💰 \\Earning Expectation → ₹8K-₹20K per month (Projects/Internship)\\ \\🟢 Year 2: Professional Growth & Specialization (12-24 Months)\\ 🎯 \\Goal: Build Professional Experience & Recognition\\ \\🔹 1-6 Months (Technical Depth)\\ ✅ Advanced ML Topics \- Deep Learning architectures \- Computer Vision OR NLP \- MLOps basics (Docker, FastAPI) \- Cloud fundamentals (AWS/GCP) ✅ Hackathon Achievements \- Win minor prizes in 2-3 hackathons \- Lead teams in major hackathons \- Network with sponsors ✅ Start Technical Blogging 💡 \\Smart Move:\\ \- Focus on hackathon projects that align with career goals \- Build relationships with companies at hackathons \- Create detailed project documentation \\🔹 7-12 Months (Professional Experience)\\ ✅ Secure ML Role/Internship ✅ Advanced Project Building ✅ Open Source Contributions ✅ Organize Small Hackathons 💡 \\Smart Move:\\ \- Use hackathon network for job referrals \- Convert hackathon projects into full products \- Build mentor reputation \\🔵 Year 2 Expected Outcome (60-70% Execution)\\ ✔ \\Professional ML experience\\ ✔ \\10+ hackathon participations\\ ✔ \\1-2 hackathon wins\\ ✔ \\Strong industry network\\ 💰 \\Earning Expectation → ₹40K-₹70K per month (Job/Freelancing)\\ \\🟢 Year 3: Scaling & Business Foundation (24-36 Months)\\ 🎯 \\Goal: Establish Multiple Income Streams\\ \\🔹 1-4 Months (Expertise Building)\\ ✅ Choose Specialization \- MLOps \- Computer Vision \- NLP/LLMs \- Generative AI ✅ Advanced Competitions \- International hackathons \- High-prize competitions \- Corporate ML challenges ✅ Start Consulting Services 💡 \\Smart Move:\\ \- Use hackathon wins for marketing \- Build service packages around expertise \- Network with corporate sponsors \\🔹 5-8 Months (Business Development)\\ ✅ Scale Services ✅ Build Client Network ✅ Create Training Programs ✅ Hackathon Mentorship Program 💡 \\Smart Move:\\ \- Convert hackathon projects to products \- Use event networks for client acquisition \- Build authority through speaking \\🔹 9-12 Months (Growth & Innovation)\\ ✅ Product Development ✅ Team Building ✅ Innovation Focus ✅ Hackathon Organization \\🔵 Year 3 Expected Outcome (60-70% Execution)\\ ✔ \\Established ML business/career\\ ✔ \\Known in hackathon community\\ ✔ \\Multiple income streams\\ ✔ \\Strong industry presence\\ 💰 \\Earning Expectation → ₹1L-₹2L per month (Multiple Streams)\\ \\🟢 Year 4: Scale & Leadership (36-48 Months)\\ 🎯 \\Goal: Build AI Company & Achieve Financial Freedom\\ \\🔹 1-4 Months (Business Scaling)\\ ✅ Company Formation \- AI consulting firm \- Product development \- Training programs ✅ Hackathon Innovation \- Launch own hackathon series \- Corporate partnerships \- Prize sponsorships ✅ Team Expansion 💡 \\Smart Move:\\ \- Use hackathon network for hiring \- Create unique event formats \- Build corporate relationships \\🔹 5-8 Months (Market Leadership)\\ ✅ Product Launch ✅ Service Expansion ✅ International Presence ✅ Innovation Hub Creation 💡 \\Smart Move:\\ \- Create hackathon-to-hiring pipeline \- Build educational programs \- Establish thought leadership \\🔹 9-12 Months (Empire Building)\\ ✅ Multiple Revenue Streams \- AI products \- Consulting services \- Educational programs \- Event organization \- Investment returns ✅ Industry Leadership \- Conference speaking \- Published content \- Community leadership \\🔵 Year 4 Expected Outcome (60-70% Execution)\\ ✔ \\Established AI company\\ ✔ \\Major hackathon organizer\\ ✔ \\Multiple product lines\\ ✔ \\Industry authority status\\ 💰 \\Earning Expectation → ₹3L-₹5L+ per month (Business Income)\\ \\📊 FINAL RATING\\ ✅ \\Comprehensive growth plan\\ ✅ \\Strong community focus\\ ✅ \\Multiple income pathways\\ 💡 \\If 100% Execution → 8.5/10 Feasibility\\ 💡 \\If 50% Execution → 6/10 Feasibility\\ 🔥 \\Conclusion: A balanced path to ML mastery and entrepreneurship, built through consistent growth and community engagement!\\ 🚀 \\Key Success Factors:\\ Regular hackathon participation Strong community involvement Consistent skill development Strategic network building Focus on both technical and business growth

#reddit #randomly #asked

How I Built A Simple ‘BPO’ Company, All AI Employees (All Local)

AssistanceOk2217•This week

How I Built A Simple ‘BPO’ Company, All AI Employees (All Local)

Disrupting the BPO Industry: My Journey Building a Fully Automated Company with AI Employees Full Article : https://medium.com/@learn-simplified/how-i-built-a-simple-bpo-company-all-ai-employees-all-local-631e48fa908a  https://preview.redd.it/htjo1mancl2d1.png?width=1586&format=png&auto=webp&s=7e77f4c66e5ca55a8b0ea6969c43a458503ad921 ● What Are We Doing Today? We are building a BPO (Business Process Outsourcing) call center for an imaginary electric company called "Aniket Very General Electric Company". We will create different departments staffed by AI agents who can chat (and eventually speak in next part) with customers to answer questions, handle complaints, or provide services. ● Why Should You Read This Article? Learning how to build AI agents that can do tasks in real setting, co ordinate w/ human, AI, providing technical support will be a highly valuable skill. ● How Are We Going to Build Our All AI Employees Company? ○ We will explain what BPO and call centers are. ○ Our AI company will have departments like Customer Service, Tech Support, Billing & Payments, Outage Management, and Onboarding Customers. ○ We will use Docker containers to run the Dify AI platform as the base. ○ The AI agents will use the LLaMA-3 language model from Meta AI. ○ We may use Groq's AI accelerator chip to make LLaMA-3 faster. ○ Each department will have a knowledge base of text files that the AI agents can reference. ● Let's Get Cooking! This section provides setup instructions for installing Docker, Ollama (for running LLaMA-3), and the Dify AI platform. It also outlines the different AI agents we will create for departments like Reception, Customer Service, Billing, Tech Support, etc. ● Let's Design our Organization ○ We explain how each department's AI agents will have their own knowledge base, like an employee handbook. ○ The knowledge bases will contain policies, procedures, and other key information. ○ The AI agents can quickly reference this information to provide accurate and knowledgeable responses. ● Let's Meet Our AI Employees ○ We chose the LLaMA-3 70B model as the base for all AI agents across departments. ○ We give the AI agents customized prompts to define their personalities and roles. ○ The knowledge bases act as training materials tailored to each department. ○ In the future, AI agents could have additional tools like ticket systems and integrations. ● Let's Run Our BPO Organization Now that the AI workforce and knowledge bases are ready, we can open our BPO company and have the AI agents start handling customer inquiries across different departments like billing, tech support, outages, and new connections. ● Debugging This section highlights the importance of debugging, showing traces of how the language model understands customer queries and retrieves relevant context from knowledge bases to provide good responses. ● Future Work ○ Scale up to handle more customers using cloud services or distributed computing. ○ Move AI agents and knowledge bases to the cloud for accessibility and maintenance. ○ Fine-tune language models for better performance in each department. ○ Use scalable vector databases for faster knowledge retrieval. ○ Enable voice interfaces and computer vision for more natural interactions. ○ Implement continuous learning so AI agents can expand their knowledge over time. The article demonstrates the potential of building an actual AI-powered company and raises thought-provoking questions about the role of humans, ethics, and using AI to create a better world. 

#reddit #built #simple

Brilliant_Fishing110•This week

Randomly asked ChatGPT and Claude for a 4 year roadmap for an ML Engineer

#reddit #randomly #asked

chipmux•This week

Backend dev wants to learn ML

Hello ML Experts, I am staff engineer, working in a product based organization, handling the backend services. I see myself becoming Solution Architect and then Enterprise Architect one day. With the AI and ML trending now a days, So i feel ML should be an additional skill that i should acquire which can help me leading and architecting providing solutions to the problems more efficiently, I think however it might not replace the traditional SWEs working on backend APIs completely, but ML will be just an additional diamention similar to the knowledge of Cloud services and DevOps. So i would like to acquire ML knowledge, I dont have any plans to be an expert at it right now, nor i want to become a full time data scientist or ML engineer as of today. But who knows i might diverge, but thats not the plan currently. I did some quick promting with ChatGPT and was able to comeup with below learning path for me. So i would appreciate if some of you ML experts can take a look at below learning path and provide your suggestions 📌 PHASE 1: Core AI/ML & Python for AI (3-4 Months) Goal: Build a solid foundation in AI/ML with Python, focusing on practical applications. 1️⃣ Python for AI/ML (2-3 Weeks) Course: [Python for Data Science and Machine Learning Bootcamp]() (Udemy) Topics: Python, Pandas, NumPy, Matplotlib, Scikit-learn basics 2️⃣ Machine Learning Fundamentals (4-6 Weeks) Course: Machine Learning Specialization by Andrew Ng (C0ursera) Topics: Linear & logistic regression, decision trees, SVMs, overfitting, feature engineering Project: Build an ML model using Scikit-learn (e.g., predicting house prices) 3️⃣ Deep Learning & AI Basics (4-6 Weeks) Course: Deep Learning Specialization by Andrew Ng (C0ursera) Topics: Neural networks, CNNs, RNNs, transformers, generative AI (GPT, Stable Diffusion) Project: Train an image classifier using TensorFlow/Keras 📌 PHASE 2: AI/ML for Enterprise & Cloud Applications (3-4 Months) Goal: Learn how AI is integrated into cloud applications & enterprise solutions. 4️⃣ AI/ML Deployment & MLOps (4 Weeks) Course: MLOps Specialization by Andrew Ng (C0ursera) Topics: Model deployment, monitoring, CI/CD for ML, MLflow, TensorFlow Serving Project: Deploy an ML model as an API using FastAPI & Docker 5️⃣ AI/ML in Cloud (Azure, AWS, OpenAI APIs) (4-6 Weeks) Azure AI Services: Course: Microsoft AI Fundamentals (C0ursera) Topics: Azure ML, Azure OpenAI API, Cognitive Services AWS AI Services: Course: [AWS Certified Machine Learning – Specialty]() (Udemy) Topics: AWS Sagemaker, AI workflows, AutoML 📌 PHASE 3: AI Applications in Software Development & Future Trends (Ongoing Learning) Goal: Explore AI-powered tools & future-ready AI applications. 6️⃣ Generative AI & LLMs (ChatGPT, GPT-4, LangChain, RAG, Vector DBs) (4 Weeks) Course: [ChatGPT Prompt Engineering for Developers]() (DeepLearning.AI) Topics: LangChain, fine-tuning, RAG (Retrieval-Augmented Generation) Project: Build an LLM-based chatbot with Pinecone + OpenAI API 7️⃣ AI-Powered Search & Recommendations (Semantic Search, Personalization) (4 Weeks) Course: [Building Recommendation Systems with Python]() (Udemy) Topics: Collaborative filtering, knowledge graphs, AI search 8️⃣ AI-Driven Software Development (Copilot, AI Code Generation, Security) (Ongoing) Course: AI-Powered Software Engineering (C0ursera) Topics: AI code completion, AI-powered security scanning 🚀 Final Step: Hands-on Projects & Portfolio Once comfortable, work on real-world AI projects: AI-powered document processing (OCR + LLM) AI-enhanced search (Vector Databases) Automated ML pipelines with MLOps Enterprise AI Chatbot using LLMs ⏳ Suggested Timeline 📅 6-9 Months Total (10-12 hours/week) 1️⃣ Core ML & Python (3-4 months) 2️⃣ Enterprise AI/ML & Cloud (3-4 months) 3️⃣ AI Future Trends & Applications (Ongoing) Would you like a customized plan with weekly breakdowns? 🚀

#reddit #backend #wants

[R] AutoDev: Automated AI-Driven Development - Microsoft 2024

Singularian2501•This week

[R] AutoDev: Automated AI-Driven Development - Microsoft 2024

Paper: https://arxiv.org/abs/2403.08299 Sorry posted a wrong github link. The real code sadly isnt public yet! Thank you for everyone who pointed that out to me! ~~Github includes Code + AutoDev Coder Model:~~ ~~https://github.com/unit-mesh/auto-dev~~ Abstract: The landscape of software development has witnessed a paradigm shift with the advent of AI-powered assistants, exemplified by GitHub Copilot. However, existing solutions are not leveraging all the potential capabilities available in an IDE such as building, testing, executing code, git operations, etc. Therefore, they are constrained by their limited capabilities, primarily focusing on suggesting code snippets and file manipulation within a chat-based interface. To fill this gap, we present AutoDev, a fully automated AI-driven software development framework, designed for autonomous planning and execution of intricate software engineering tasks. AutoDev enables users to define complex software engineering objectives, which are assigned to AutoDev's autonomous AI Agents to achieve. These AI agents can perform diverse operations on a codebase, including file editing, retrieval, build processes, execution, testing, and git operations. They also have access to files, compiler output, build and testing logs, static analysis tools, and more. This enables the AI Agents to execute tasks in a fully automated manner with a comprehensive understanding of the contextual information required. Furthermore, AutoDev establishes a secure development environment by confining all operations within Docker containers. This framework incorporates guardrails to ensure user privacy and file security, allowing users to define specific permitted or restricted commands and operations within AutoDev. In our evaluation, we tested AutoDev on the HumanEval dataset, obtaining promising results with 91.5% and 87.8% of Pass@1 for code generation and test generation respectively, demonstrating its effectiveness in automating software engineering tasks while maintaining a secure and user-controlled development environment. https://preview.redd.it/5nxqajnvbkoc1.jpg?width=924&format=pjpg&auto=webp&s=8343c5fb33d2914bbfbf2dd9c164b5970b9743ab https://preview.redd.it/z5fkkjnvbkoc1.jpg?width=1364&format=pjpg&auto=webp&s=bc434ff384d2ed67ea0382dbbb68b9a90313cd44

Docker

[D] Docker Containers (Python) vs Direct C++ Implementation for AI on Edge Devices, Performance Tradeoffs

No-code AI platform?

What do you think about no-code AI development

What is the best setup to implement for Ai

Randomly asked ChatGPT and Claude for a 4 year roadmap for an ML Engineer

How I Built A Simple ‘BPO’ Company, All AI Employees (All Local)

Randomly asked ChatGPT and Claude for a 4 year roadmap for an ML Engineer

Backend dev wants to learn ML

[R] AutoDev: Automated AI-Driven Development - Microsoft 2024

n8n

anything-llm

mentals-ai

xpert

LLMs-from-scratch

AITreasureBox

h2o-llmstudio

Production-Level-Deep-Learning

AI-Scalpel-Trading-Bot

aima-python

RD-Agent

LLMStack

instill-core

introduction-to-ai-native-vector-databases-4470531

CrewAI-Studio

freeciv-web

TornadoVM

magic

Vibe Coding | My Thoughts

airplay2-receiver

obsei

ai-flow

spring-ai-intro

bubbln_network-automation

bytom

AI-and-Business-Rules-for-Excel-Power-Users

node-red-contrib-ml

Why "vibe coding" is NOT my future

engine-core

aion

studio

aioamqp

responsible-ai-hub