15.5K
CAComment "MLOPs" to get your hands on these videos and playlist.
The playlist is my personal recommendation if you have time to invest.
#mlops #machinelearning #artificialintelligence #youtube #fyp
@cactuss.ai
#Mlops
Dünyanın dört bir yanından insanlardan Mlops hakkında Reels videosu izle.
Giriş yapmadan anonim olarak izle.
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30Trend Reels
(12)
1.4K
PIComment “ML” to get links!
🚀 Want to learn MLOps in a way that actually sticks? This mini roadmap takes you from confusion to confidently deploying, monitoring, and improving ML models like a real ML engineer.
🎓 MLOps Basics
Perfect if you are new. You will understand what MLOps is, why ML projects fail in production, and how training is only a small part of the real job. Great for learning the big picture like pipelines, deployment, monitoring, and iteration.
💻 MLOps Build Course
Now go hands on. You will build an end to end workflow so you stop watching theory and start shipping models. You will see how real ML systems handle data, training, versioning, deployment, and updates.
📘 Andrew Ng MLOps
Build strong fundamentals from one of the best instructors. This helps you understand the core ideas behind reliable ML systems and how to think like a production focused ML engineer.
💡 With these MLOps resources you will:
Understand the full ML lifecycle from data to deployment
Learn how to monitor models and handle model drift
Build portfolio ready MLOps projects for interviews
Connect ML with DevOps concepts like CI CD, automation, and reliability
If you are serious about machine learning engineering, data science in production, or ML system design, MLOps is the skill that makes you stand out.
📌 Save this post so you do not lose the roadmap.
💬 Comment “ML” and I will send you all the links.
👉 Follow for more content on MLOps, AI engineering, system design, and backend.
@pirknn
12.3K
RIMaster MLOps from the ground up
//
{ devops, devopsengineer, softwaredevelopers, devops, ai, promptengineering, prompengineer, automationengineering, chatgptprompts, chatgpt5, skills, skilldevelopment, resources, dataanalysis, dataanalytics, softwareengineering, firstjob, techskills, mlops }
#devops #devopsengineer #ai #softwaredevelopers #promptengineering #automationengineering
#chatgptprompts #chatgpt4 #mlops
@rishcloudops
12.8K
TEPeople often say LLMOps is just MLOps applied to large language models.
But hey solve very different operational problems.
Here’s how I think about the difference.
1️⃣ Model lifecycle vs application lifecycle
MLOps focuses on managing the **model itself**
training → versioning → deployment → monitoring
LLMOps feels closer to managing an **AI application**
model → prompts → tools → workflows → responses
2️⃣ Data pipelines vs context pipelines
In MLOps, most work goes into building pipelines like
raw data → feature engineering → model training
In LLMOps, the pipeline shapes the model’s context
knowledge base → retrieval (RAG) → prompt template → model
3️⃣ Metrics vs evaluation frameworks
MLOps relies on clear metrics
accuracy → precision → recall → RMSE.
LLMOps needs layered evaluation
prompt testing → hallucination checks → LLM-as-judge → human feedback.
4️⃣ Model deployment vs AI system orchestration
MLOps manages
training pipeline → model deployment → performance monitoring.
LLMOps orchestrates the **entire AI system**
agents → tool calling → RAG pipelines → guardrails.
5️⃣ Data drift vs behavior drift
MLOps monitors
data drift → model performance decay.
LLMOps watches for
prompt failures → tool errors → hallucinations → safety issues.
Most real AI systems end up needing both.
Predictive systems like recommendation, forecasting, and fraud detection rely on **MLOps**.
AI assistants, copilots, and agent workflows rely heavily on **LLMOps**.
Different toolkits.
Same goal 🥅 Reliable AI systems.
💾 Save this if you’re trying to understand how modern AI systems are actually built
💬 Comment if you think LLMOps will eventually replace MLOps or live alongside it
🔁 Follow for more practical notes on building real AI systems
@techwithprateek
15.4K
DADevOps vs. MLOps vs. LLMOps, explained visually 🔧
Many teams are trying to apply DevOps practices to LLM apps.
But DevOps, MLOps, and LLMOps solve fundamentally different problems.
DevOps is software-centric:
Write code, test it, deploy it. The feedback loop is straightforward: Does the code work or not?
MLOps is model-centric:
You're dealing with data drift, model decay, and continuous retraining. The code might be fine, but the model's performance can degrade over time because the world changes.
LLMOps is foundation-model-centric:
You're typically not training models from scratch. Instead, you're selecting foundation models and optimizing through three paths:
→ Prompt Engineering
→ Context/RAG Setup
→ Fine-Tuning
But here's what really separates LLMOps: The monitoring is completely different.
MLOps monitoring:
✅ Data drift
✅ Model decay
✅ Accuracy
LLMOps monitoring:
✅ Hallucination detection
✅ Bias and toxicity
✅ Token usage and cost
✅ Human feedback loops
This is because you can't just check if the output is "correct." You need to ensure it's safe, grounded, and cost-effective.
The evaluation loop in LLMOps feeds back into all three optimization paths simultaneously. Failed evals might mean you need better prompts, richer context, OR fine-tuning.
So it's not a linear pipeline anymore.
One more thing: prompt versioning and RAG pipelines are now first-class citizens in LLMOps, just like data versioning became essential in MLOps.
The ops layer you choose should match the system you're building.
👉 Over to you: What does your LLM monitoring stack look like?
#ai #devops #mlops
@dailydoseofds_
29.5K
VOComment “ML” for all the links!
You Will Never Struggle With MLOps Again 🤖
📌 Watch these beginner-friendly videos:
1️⃣ What is MLOps? – IBM Technology
2️⃣ MLOps Course – Build Machine Learning Production Grade Projects (by freeCodeCamp)
3️⃣ MLOps Roadmap – My Roadmap.sh Guide
Stop feeling overwhelmed by deployment pipelines, model monitoring, and CI/CD for machine learning. These tutorials guide you step by step — from understanding what MLOps is, to building and deploying production-grade ML models like a pro.
Whether you’re a data scientist, ML engineer, or just starting your AI journey, this is the fastest way to master MLOps and bring your models to the real world.
Save this post, share it with your tech friends, and start building smarter ML systems today.
@volkan.js
28.1K
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
8.7K
MEMLOps Engineer Career Roadmap?
"Your Ultimate Career Roadmap to Becoming an MLOps Engineer"
🗓️ Early-Bird Offer on My Live Weekday MLOps Engineer Projects-MLOps Engineer Focused Program with Internship, To learn more, Whatsapp Us at: +919347125815 / +919121516181
#MLOps #CareerRoadmap #DataScience #MachineLearning #AI #MLOpsEngineer #CareerTips #MLPipeline #DevOps #ModelDeployment #AIEngineering #CloudComputing #MLOpsSkills #MLInfrastructure #DataEngineering #TechCareers #Automation #ModelMonitoring #ContinuousIntegration #MachineLearningOps #CareerDevelopment #MLOpsCareers
@meet_kanth
31.4K
THWTH is MLOps vs. LLMOps? 🤔
If you’re building with traditional ML models (like XGBoost or CNNs), you’re in the MLOps world — where pipelines, data versioning, and model deployment are key.
But if you’re working with foundation models, prompt tuning, RAG, or agent stacks — you’re in LLMOps land.
Here, you’re managing prompts, fine-tuning checkpoints, vector stores, context windows, and model evaluations with language as the interface.
Same goals — different toolchains. 🛠️
MLOps is about training models.
LLMOps is about orchestrating intelligence.
PS: This video is entirely AI generated ❤️
[AI tools, machine learning, MLOps, LLMOps, data science, foundation models, AI engineers, prompt engineering, fine-tuning, genAI, vector databases, transformers, RAG, model deployment, model evaluation, neural networks, AI workflows]
@the.datascience.gal
39.5K
JAMost ML engineers know how to train a model. Very few know how to keep it alive in production.
Comment “MLOps” and I’ll send you all the resources to learn it based on my personal experiences.
A real MLOps pipeline is a self-healing loop. Training code gets packaged into a Docker image, triggers the training pipeline, and the trained model gets versioned, tested, and deployed as a live prediction service. Once live, it is monitored. Data drifts. Performance drops. The pipeline retrains automatically. The loop never stops.
That is the difference between a model and a production ML system.
[mlops, ci cd, machine learning engineering, mlops pipeline, continuous training, continuous deployment, continuous integration, model deployment, model monitoring, data drift, ml pipeline, production ml, docker, model registry, feature engineering, llmops, ml engineering, data science, ai engineering]
@jam.with.ai
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
1.5K
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
✨ #Mlops Keşif Rehberi
Instagram'da #Mlops etiketi altında thousands of paylaşım bulunuyor ve platformun en canlı görsel ekosistemlerinden birini oluşturuyor. Bu devasa koleksiyon, şu an gerçekleşen trend anları, yaratıcı ifadeleri ve küresel sohbetleri temsil ediyor.
Instagram'ın devasa #Mlops havuzunda bugün en çok etkileşim alan videoları sizin için listeledik. @jam.with.ai, @the.datascience.gal and @volkan.js ve diğer içerik üreticilerinin paylaşımlarıyla şekillenen bu akım, global çapta thousands of gönderiye ulaştı.
#Mlops dünyasında neler viral? En çok izlenen Reels videoları ve viral içerikler yukarıda yer alıyor. Yaratıcı hikaye anlatımını, popüler anları ve dünya çapında milyonlarca görüntüleme alan içerikleri keşfetmek için galeriyi inceleyin.
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📹 Video Trendleri: En yeni Reels içeriklerini ve viral videoları keşfedin
📈 Hashtag Stratejisi: İçerikleriniz için trend hashtag seçeneklerini inceleyin
🌟 Öne Çıkanlar: @jam.with.ai, @the.datascience.gal, @volkan.js ve diğerleri topluluğa yön veriyor
#Mlops Hakkında SSS
Pictame ile Instagram'a giriş yapmadan tüm #Mlops reels ve videolarını izleyebilirsiniz. İzleme aktiviteniz tamamen gizli kalır - hiçbir iz bırakılmaz, hesap gerekmez. Hashtag'i aratın ve trend içerikleri anında keşfetmeye başlayın.
İçerik Performans Analizi
12 reel analizi
💡 En iyi performans gösteren içerikler ortalama 32.1K görüntüleme alıyor (ortalamadan 1.9x fazla). Orta seviye rekabet - düzenli paylaşım momentum oluşturur.
Kitlenizin en aktif olduğu saatlerde haftada 3-5 kez düzenli paylaşım yapın
İçerik Oluşturma İpuçları & Strateji
💡 En iyi içerikler 10K üzeri görüntüleme alıyor - ilk 3 saniyeye odaklanın
✍️ Hikayeli detaylı açıklamalar işe yarıyor - ortalama açıklama uzunluğu 1025 karakter
📹 #Mlops için yüksek kaliteli dikey videolar (9:16) en iyi performansı gösteriyor - iyi aydınlatma ve net ses kullanın
✨ Çok sayıda onaylı hesap aktif (%67) - ilham almak için içerik tarzlarını inceleyin
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Mlops Keşfet#mlops solutions#mlops engineer#mlop#mlops tools#mlops engineer salary#mlops projects#mlops and llmops#how to learn mlops