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#mlops #MachineLearning #ai #viralvideos #code
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TEGPUs failing? It's less common than you think. Infant mortality can be ironed out. Once past the initial debug, they're surprisingly reliable. #GPU #Tech #AI #Hardware #Reliability #DataCenter #DeepLearning #MLOps #NVIDIA #TPU
@tenaxhq
27.2K
COVectorless RAG is quietly changing how we build retrieval systems.And most engineers haven't noticed yet.
For 3 years we've been told RAG = embeddings + vector database.
Chunk your docs.
Embed everything.
Store in Pinecone.
Similarity search at query time.
It works.
But the baggage is real →
→ Embedding drift across model versions
→ Chunk size tuning that never feels right
→ Semantic search that misses exact keywords
→ Vector store infrastructure costs
→ Re-indexing nightmares when your model changesVectorless RAG skips all of that.BM25 for keyword precision.
Knowledge graphs for relationship reasoning.
SQL retrieval for structured data.
Long context LLMs that skip indexing entirely.
The real unlock in 2026?
Hybrid systems.One query. Multiple retrievers. Route to the right method at runtime. Best result wins.Stop defaulting to vector search because the tutorial said so.
Start asking → what does my data actually need?
Save this and send it to an engineer who's still embedding everything 🔥.
.
.#RAG #AI #MachineLearning #LLM #VectorDatabase #AIEngineering #GenerativeAI #ArtificialIntelligence #TechContent #DataEngineering #NLP #BuildInPublic #AIArchitecture #DeepLearning #MLOps
@codewithbrij
5.6K
LIMLOps vs DevOps explained in 40 seconds
What’s the difference between DevOps and MLOps?
Think of DevOps like running a factory.
DevOps engineers handle the hard parts of software reliability:
CI/CD, infrastructure as code, containerization, deployment automation, observability, scaling, resilience.
If the code and inputs stay the same, you expect consistent behavior.
Now ML systems introduce something different.
They’re less like factories ….& more like farming.
You can follow the same pipeline.
But outcomes depend on changing conditions:
data distributions, user behavior, real-world dynamics.
Even if:
✅ the infrastructure is perfectly healthy
✅ the deployment is flawless
✅ the service has zero errors
Model performance can still degrade⬇️
That’s not an operational failure.
It’s distribution shift ….a statistical failure mode.
MLOps doesn’t replace DevOps.
It extends DevOps patterns to manage what ML systems uniquely require:
🔹experiment tracking
🔹data and model versioning
🔹model registries
🔹statistical monitoring in production
🔹retraining and evaluation workflows
DevOps ensures systems are operationally reliable
MLOps ensures models remain statistically valid over time
Both are critical
#devops #mlops #machinelearning
@lindavivah
27.8K
PRkubernetes explained
#learningtogether #womeninstem #progresseveryday #mlops #docker
@priyal.py
2.8K
RIComment “ML” for the guide link 🚀💌
[what is mlops, learn devops, mlops roadmap, devops to mlops, mlops engineer, machine learning, cloud computing, learn cloud, roadmap, devops guide, mlops guide, kubernetes, ai infrastructure, ai solutions architect, llmops, docker, Kubernetes, aws, aws sagemaker, aws solution, projects, devops projects, cloud projects]
#devops #kubernetes #trending #rishabh #mlops
@rishcloudops
93.2K
AITraining is step one. Productionizing with MLOps, FastAPI, and Docker is where real-world GenAI happens.#mlops#genai#fastapi#docker#aiproduction
@aiwithtani
308.5K
RAMost ML beginners stop at a Jupyter notebook.
Real engineers know how to productionize a model end-to-end.
This is how I shipped ML features at Microsoft (Azure-based, but same concepts apply to AWS/GCP).
If you’re early in your career, practice this with free student credits on cloud.
Here’s the path 👇
⸻
1️⃣ Two steps of an ML model
Training pipeline → data → features → training → metrics → registry.
Inference pipeline → real-time endpoint (REST) or batch jobs (nightly scoring).
Choosing the right one varies by usecase.
⸻
2️⃣ Build a reproducible training pipeline
Use Azure ML / Sagemaker / Databricks to automate training, log runs, version data, and store models.
Notebooks are not production. Learn about azure sdk or aws sdk.
⸻
3️⃣ Real-time vs Batch (When to use what)
Real-time: fraud, ranking, personalization, chatbots.
Batch: churn, segmentation, risk scoring.
Wrong choice = poor performance or expensive infra.
⸻
4️⃣ API testing (Nobody teaches this)
DS exposes an endpoint → shares payload schema → engineering integrates → validates latency, errors, retries, auth.
You only learn this once you’re actually in a job. Having this skill earlier is a great plus.
⸻
5️⃣ Deployment workflow (Real-world)
Register model → CI/CD → staging → automated tests → canary/shadow → full rollout.
You should atleast be familar with this process if not hands on.
⸻
6️⃣ Monitor everything
Drift, latency, feature freshness, error rates, business KPIs.
Without monitoring, your model is just “running somewhere,” not “in production.”
⸻
7️⃣ My real learning
Anyone can train a model.
But taking it to production, testing it, proving reliability and explainability to stakeholders, running previews → GA, and having BCDR—that’s what makes you a real ML engineer.
I learned this as my experience grew with Microsoft.
Start with a simple model, but practice the FULL pipeline.
⸻
🔖 Tags
#machinelearning #mlops #aiengineering #azure #aws datascience mlsystemdesign productionml mlengineer genai techreels ai ml trend engineering
@ranatahirbilalnr
11.7K
MADrowning in ML algorithms? 😵💫 Choosing the right model, feature engineering, hyperparameter tuning... it’s a marathon with no finish line in sight!
Enter AutoGluon: your game-changer for simplified, high-performance machine learning. With just three lines of code, it automates model selection, training, and even handles messy data like a pro! Say goodbye to endless trial-and-error.
Want to revolutionize your ML workflow? Drop a comment ‘REPO’ below, and I’ll send you the official AutoGluon GitHub repository link to get started! 👇
#AutoML #MachineLearning #DataScience #AutoGluon #MLOps Python AI TechCareer SimplifyingML DeveloperTools FutureOfAI Coding AI2025 MLCommunity
@madhav_somani
1.4K
K2Comment ‘MLOps’ for a FREE Interview Questions Guide!
Let’s see if we can cover the ML pipeline in 60 seconds ⏰
Machine learning isn’t just training a model. A production ML lifecycle typically looks like this:
1️⃣ Define the problem & objective
2️⃣ Collect and (if needed) label data
3️⃣ Split into train / validation / test sets
4️⃣ Data preprocessing & feature engineering
5️⃣ Train the model (forward pass + backpropagation in deep learning)
6️⃣ Evaluate on held-out data to measure generalization
7️⃣ Hyperparameter tuning (learning rate, architecture, etc.)
8️⃣ Final testing before release
9️⃣ Deploy (batch inference or real-time serving behind an API)
🔟 Monitor for data drift, concept drift, latency, cost, and reliability
1️⃣1️⃣ Retrain when performance degrades
Training updates weights.
Evaluation measures performance.
Deployment serves predictions.
Monitoring keeps the system healthy.
It’s not linear. It’s a loop.
And once you move beyond a single experiment, that loop becomes a systems problem.
At scale, the challenge isn’t just modeling … it’s building reliable, scalable infrastructure that supports the entire lifecycle.
#machinelearning #mlpipeline #ai #techjobs #cloudcomputing
@k21academy
96.8K
SYMost RAG tutorials skip straight to vectors and agents - and that’s exactly why production systems break.
This free, open-source 7-week roadmap teaches you production RAG *in the right order*, with real tools: Airflow, FastAPI, Redis, Ollama, and LangGraph.
No fluff. No paid courses. Just how it’s actually built at scale.
What You’ll Build
- Week 1: Complete infrastructure with Docker, FastAPI, PostgreSQL, OpenSearch, and Airflow
- Week 2: Automated data pipeline fetching and parsing academic papers from arXiv
- Week 3: Production BM25 keyword search with filtering and relevance scoring
- Week 4: Intelligent chunking + hybrid search combining keywords with semantic understanding
- Week 5: Complete RAG pipeline with local LLM, streaming responses, and Gradio interface
- Week 6: Production monitoring with Langfuse tracing and Redis caching for optimized performance
- Week 7: Agentic RAG with LangGraph and Telegram Bot for mobile access
🔗 I have pinned the repo link in the comments.
💾 Save this if you’re building anything with LLMs.
#RAG #LLM #MLOps #DataEngineering #AIEngineering LangGraph MachineLearning OpenSource ProductionAI MLEngineer
@systemsbyakshay
4.7K
FE4 Weeks. 4 Skills. 100% Free.
Most people spend months overthinking where to start with AI Engineering. Here’s a simple plan: one skill per week, all free courses, zero excuses.
Week 1: Prompt Engineering
Week 2: RAG & LLM APIs
Week 3: Fine-Tuning Models
Week 4: MLOps & Deployment
Now let me be completely honest with you.
Will you become an expert in 4 weeks? No. That would be a lie. Nobody masters these skills in a month, and anyone telling you otherwise is selling something.
But here’s what WILL happen.
You’ll go from “I don’t even know where to start” to actually understanding how these systems work. You’ll have the vocabulary. You’ll have the mental models. You’ll stop feeling lost when people talk about vector databases, LoRA, or CI/CD pipelines.
These courses give you solid foundational knowledge. The kind that makes everything else click faster. You’ll be able to read documentation without feeling overwhelmed. You’ll understand what tools to pick and why. You’ll have enough confidence to start building small projects on your own.
And that’s where the real learning begins.
Because the truth is, building real world projects will always teach you more than any course ever could. Courses give you the map. Projects give you the scars, the lessons, and the actual skills employers care about.
Save this. Share it with someone who needs it. And start today.
#AIEngineer #PromptEngineering #RAG #FineTuning #mlops
@fellowtechiebuddy
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