36.7K
TH3 Free resources to learn MLOps 🚀🔥
1. Machine Learning Operations with Github
2. Made with ML @goku.mohandas
3. MLOps specialization by Andrew Ng - Coursera
#ml #ai #mlops
@the.datascience.gal
#Mlops Projects
Schauen Sie sich Reels-Videos über Mlops Projects von Menschen aus aller Welt an.
Anonym ansehen ohne Anmeldung.
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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
4.7K
PIComment "Book" for the full list 👇
So many people waste time jumping between random AI courses without direction.
If you actually want to become an AI Engineer, these books are your roadmap:
📚 Hands-On Machine Learning
Gives you the coding and ML fundamentals with real examples you can follow
📚 AI Engineering
Shows you how to design, build, and deliver AI systems that actually work in production
📚 The LLM Engineer's Handbook
A comprehensive guide for building and deploying end-to-end LLM applications, from fine-tuning to production
📚 Practical MLOps
Teaches you how to productionize ML models (because building ≠ shipping)
Read. Apply. Stop spinning in circles with endless courses.
Pro tip: Pick ONE book. Read a chapter. Build something. Repeat.
The difference between wannabe AI engineers and real ones? Real ones read books, build systems, and ship products.
Stop course-hopping. Start book-building.
#aiengineer #machinelearning #mlops #techbooks #careeradvice #datascience #artificialintelligence #selflearning #programming
@pirknn
23.1K
CO🚀 Machine Learning Roadmap (2025 Edition)
Unlock your journey into AI, Machine Learning & Deep Learning with this step-by-step guide designed for beginners to advanced learners.
📌 What You’ll Learn in This Video:
⚙️ Phase 1 – Core Foundation
📐 Math Basics | 🐍 Python Programming
🧹 Phase 2 – Data Preparation
🧽 Data Cleaning | 🎛 Feature Engineering | 📊 Visualization
🤖 Phase 3 – Machine Learning Concepts
🎯 Supervised & Unsupervised Learning | 🔍 Key Algorithms
🧪 Phase 4 – Model Optimization
📈 Cross-Validation | 🛠 Hyperparameter Tuning | 📍 Metrics
🧠 Phase 5 – Advanced ML
🌀 Neural Networks | 👁 Computer Vision | 💬 NLP
🚀 Phase 6 – Deployment & Real-World Use
🗃 Model Serialization | 🌐 APIs | ☁ Cloud | 🧩 MLOps
---
💡 Whether you're a beginner, student, or career switcher, this roadmap will help you become job-ready in AI and ML.
📚 Save this video and start learning step by step.
👇 Comment "ROADMAP" if you want a downloadable PDF version.
---
🔍 Keywords:
Machine Learning Roadmap 2025, AI learning path, Deep Learning, Data Science Roadmap, Python for ML, Best way to learn AI, MLOps, Cloud AI skills.
---
🔥 Hashtags:
#MachineLearning #AI #ArtificialIntelligence #DeepLearning #DataScience #Python #MLRoadmap #LearnML #TechCareers #Programming #NLP #ComputerVision #MLOps #DataEngineer #FutureSkills #Roadmap2025 #AIEducation #AIRevolution #CodingJourney
@codewithprashantt
93.2K
AITraining is step one. Productionizing with MLOps, FastAPI, and Docker is where real-world GenAI happens.#mlops#genai#fastapi#docker#aiproduction
@aiwithtani
2.4K
DATo learn MLOps:
1. Basics: Understand ML workflows and DevOps concepts (CI/CD, version control).
2. Tools: Learn Git, Docker, Kubernetes, MLflow, Airflow, and cloud platforms (AWS/GCP/Azure).
3. Key Concepts: Focus on model deployment, monitoring, and automation.
4. Projects:
• Deploy a model with Flask/Streamlit + Docker.
• Build a CI/CD pipeline.
• Use MLflow or DVC for versioning.
• Automate workflows with Airflow or Prefect.
5. Resources:
• Courses: Coursera’s MLOps specialization.
• Books: Practical MLOps by Noah Gift.
6. Practice: Contribute to open-source MLOps projects and build end-to-end systems.
[ artificial intelligence, machine learning, data science, python, job]
#datascience #jobs #programming #trending #coding #ai
@data_.couple
179.7K
JAHere is the MLOps roadmap:
(with resources and courses included)
📌 𝗡𝗼𝘁𝗲:
Red asterisks = Must-have skills
Others = familiarity is enough (especially early on)
▶️ 𝗠𝗟 & 𝗠𝗟𝗢𝗽𝘀 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻*
→ Machine Learning Specialization –
🔗 comment “mlops” for link
→ Designing Machine Learning Systems (Book)
🔗 comment “mlops” for link
→ Model deployment in production
🔗 comment “mlops” for link
▶️ 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 (𝗣𝘆𝘁𝗵𝗼𝗻)*
→ Python general → comment “mlops” for link
→ FastAPI / Flask → comment “mlops” for link
→ Git (Version Control) → comment “mlops” for link
→ Unit Testing → comment “mlops” for link
→ Integration Testing → comment “mlops” for link
→ Docker (Must) → comment “mlops” for link
→ CI/CD (only one) → comment “mlops” for link
🔗 GitHub Actions → comment “mlops” for link
🔗 CircleCI → comment “mlops” for link
🔗 Jenkins → comment “mlops” for link
→ Load Testing (Locust) - comment “mlops” for link
→ A/B Testing - 🔗 comment “mlops” for link
▶️ 𝗖𝗹𝗼𝘂𝗱 𝗜𝗻𝗳𝗿𝗮 (𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁)*
Pick one cloud provider. Learn via certification paths.
→ AWS SageMaker (overview) - comment “mlops” for link
→ AWS Learning Path:
1️⃣ AWS Cloud Practitioner (free) → comment “mlops” for link
2️⃣ AWS ML Associate → comment “mlops” for link
3️⃣ AWS ML Specialty → comment “mlops” for link
(Also: GCP Vertex AI, Azure ML)
▶️ 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁 𝗧𝗿𝗮𝗰𝗸𝗶𝗻𝗴 & 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴
→ MLflow* - 🔗 comment “mlops” for link
→ Grafana + Prometheus - 🔗 comment “mlops” for link
→ DataDog - 🔗 comment “mlops” for link
▶️ 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗦𝗲𝗿𝘃𝗶𝗻𝗴
→ Apache Airflow (good to know) - 🔗 comment “mlops” for link
→ Kubeflow (best with GCP)
→ MetaFlow
→ EC2 / ECS / Kubernetes / Step Functions
(Skip Kubernetes early on if possible)
🚨𝗥𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗲𝗱 𝗖𝗼𝘂𝗿𝘀𝗲𝘀
→ MLOps Zoomcamp (DataTalksClub)
🔗 comment “mlops” for link
→ MadeWithML by Goku
🔗 comment “mlops” for link
→ Train & Deploy ML (GitHub)
🔗 comment “mlops” for link
→ Taking Python to Production
🔗 comment “mlops” for link
#machinelearning #mlops #datascience #ai #coding
@jam.with.ai
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
116.9K
MAWant to become an AI Engineer and earn the highest packages? 🚀 This roadmap breaks down exactly what you need to learn, step by step, to master AI from foundations to advanced MLOps.
Stop guessing, start building your dream career! 👇
Drop a comment ‘AI’ and I’ll send you the full roadmap to master AI engineering with top resources!
#AIEngineer #AIRoadmap2025 #MachineLearning #DeepLearning #CareerGrowth #TechJobs #AICommunity #Python #MLOps #CodingLife
@madhav_somani
253.7K
DEMost 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 #india datascience mlsystemdesign productionml mlengineer genai techreels ai ml trend engineering
@devtrist
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
96.7K
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
✨ #Mlops Projects Entdeckungsleitfaden
Instagram hostet thousands of Beiträge unter #Mlops Projects und schafft damit eines der lebendigsten visuellen Ökosysteme der Plattform.
#Mlops Projects ist derzeit einer der beliebtesten Trends auf Instagram. Mit über thousands of Beiträgen in dieser Kategorie führen Creator wie @devtrist, @jam.with.ai and @madhav_somani mit ihren viralen Inhalten. Durchsuchen Sie diese beliebten Videos anonym auf Pictame.
Was ist in #Mlops Projects im Trend? Die meistgesehenen Reels-Videos und viralen Inhalte sind oben zu sehen.
Beliebte Kategorien
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Häufige Fragen zu #Mlops Projects
Mit Pictame können Sie alle #Mlops Projects Reels und Videos durchsuchen, ohne sich bei Instagram anzumelden. Ihre Aktivität bleibt vollständig privat - keine Spuren, kein Konto erforderlich. Suchen Sie einfach nach dem Hashtag und entdecken Sie sofort trendige Inhalte.
Content Performance Insights
Analyse von 12 Reels
💡 Top-Posts erhalten durchschnittlich 161.8K Aufrufe (2.3x über Durchschnitt)
Regelmäßig 3-5x/Woche zu aktiven Zeiten posten
Content-Erstellung Tipps & Strategie
🔥 #Mlops Projects zeigt hohes Engagement-Potenzial - strategisch zu Spitzenzeiten posten
✍️ Detaillierte Beschreibungen mit Story funktionieren gut - durchschnittliche Länge 878 Zeichen
✨ Viele verifizierte Creator sind aktiv (58%) - studieren Sie deren Content-Stil
📹 Hochwertige vertikale Videos (9:16) funktionieren am besten für #Mlops Projects - gute Beleuchtung und klaren Ton verwenden
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