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
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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
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
33.2K
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
16.5K
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_
11.4K
PIComment “ML” to get links!
🚀 Want to learn MLOps and actually deploy machine learning like a real ML engineer? This mini roadmap helps you go from understanding the basics to building production grade ML pipelines.
🎓 What is MLOps
Perfect first step if you are new. You will understand what MLOps means, why teams need it, and how it connects training, deployment, monitoring, and iteration. Great for building the right mental model before touching tools.
📘 MLOps Course Build
Now go deeper and get hands on. You will learn core workflows like data versioning, experiment tracking, model registry, CI CD for ML, and automated pipelines. This is where you start thinking like production not notebooks.
💻 Andrew Ng MLOps
Time to level up with a structured path. You will learn how to design reliable ML systems, handle real world data issues, manage model drift, set up monitoring, and build end to end ML production processes used in industry.
💡 With these MLOps resources you will:
Understand the full ML lifecycle from data to deployment
Build reusable training and inference pipelines
Learn model monitoring, drift detection, and retraining strategies
Create portfolio ready ML engineering projects that stand out
If you are serious about machine learning engineering, data science careers, or shipping AI products, MLOps is the skill that separates hobby projects from real systems.
📌 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, machine learning, and ML engineering.
@pirknn
29.6K
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
94.8K
JA𝗧𝗵𝗲 𝗮𝘄𝗸𝘄𝗮𝗿𝗱 𝘁𝗿𝘂𝘁𝗵:
Most “AI companies” charge thousands for what you can build with these free tools.
With a laptop and solid internet, you can build production-grade AI systems today!
Every tool you need to become a top 1% AI/MLOps engineer is already out there.
Every concept you need to master:
- monitoring,
- A/B testing,
- RAG pipelines,
- vector databases,
- model versioning,
- prompt engineering,
- experiment tracking,
- agent orchestration,
- automated ML pipelines - it’s on GitHub, in the docs, in open courses, waiting for someone willing to break stuff and build again.
Nobody is stopping you from:
- reading research papers,
- joining the AI community,
- building your first RAG system,
- contributing to open-source ML projects,
- deploying your first ML model to production.
You can:
- create AI agents,
- automate ML pipelines,
- practice chunking strategies,
- deploy a local RAG on your laptop,
- build semantic search with free embeddings,
- set up model monitoring and experiment tracking
- and see exactly how modern AI applications work at scale behind the scenes.
Don’t wait for:
- a PhD,
- expensive GPUs,
- the “perfect dataset”,
- expensive cloud credits,
- enterprise infrastructure,
- someone’s course or certification to validate your AI skills.
The models are there.
The community is there.
The frameworks are there.
The infrastructure patterns are there.
Start shipping systems.
What are you waiting for?
Go build!
What do you think?
@jam.with.ai
12.9K
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
5.7K
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
309.0K
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
93.9K
AITraining is step one. Productionizing with MLOps, FastAPI, and Docker is where real-world GenAI happens.#mlops#genai#fastapi#docker#aiproduction
@aiwithtani
✨ #Mlops Discovery Guide
Instagram hosts thousands of posts under #Mlops, creating one of the platform's most vibrant visual ecosystems. This massive collection represents trending moments, creative expressions, and global conversations happening right now.
#Mlops is one of the most engaging trends on Instagram right now. With over thousands of posts in this category, creators like @ranatahirbilalnr, @jam.with.ai and @aiwithtani are leading the way with their viral content. Browse these popular videos anonymously on Pictame.
What's trending in #Mlops? The most watched Reels videos and viral content are featured above. Explore the gallery to discover creative storytelling, popular moments, and content that's capturing millions of views worldwide.
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💡 Top performing posts average 132.7K views (2.4x above average). Moderate competition - consistent posting builds momentum.
Post consistently 3-5 times/week at times when your audience is most active
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✨ Many verified creators are active (67%) - study their content style for inspiration
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✍️ Detailed captions with story work well - average caption length is 1127 characters
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