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
Dünyanın dört bir yanından insanlardan Mlops Projects hakkında Reels videosu izle.
Giriş yapmadan anonim olarak izle.
İlgili Aramalar
Trend Reels
(12)
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
23.2K
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
93.3K
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
118.0K
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
254.6K
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
97.1K
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 Keşif Rehberi
Instagram'da #Mlops Projects 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.
#Mlops Projects etiketi, Instagram dünyasında şu an en çok ilgi gören akımlardan biri. Toplamda thousands of üzerinde paylaşımın bulunduğu bu kategoride, özellikle @devtrist, @jam.with.ai and @madhav_somani gibi üreticilerin videoları ön plana çıkıyor. Pictame ile bu popüler içerikleri anonim olarak izleyebilirsiniz.
#Mlops Projects 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.
Popüler Kategoriler
📹 Video Trendleri: En yeni Reels içeriklerini ve viral videoları keşfedin
📈 Hashtag Stratejisi: İçerikleriniz için trend hashtag seçeneklerini inceleyin
🌟 Öne Çıkanlar: @devtrist, @jam.with.ai, @madhav_somani ve diğerleri topluluğa yön veriyor
#Mlops Projects Hakkında SSS
Pictame ile Instagram'a giriş yapmadan tüm #Mlops Projects 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 162.4K görüntüleme alıyor (ortalamadan 2.3x 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
✨ Çok sayıda onaylı hesap aktif (%58) - ilham almak için içerik tarzlarını inceleyin
✍️ Hikayeli detaylı açıklamalar işe yarıyor - ortalama açıklama uzunluğu 878 karakter
📹 #Mlops Projects için yüksek kaliteli dikey videolar (9:16) en iyi performansı gösteriyor - iyi aydınlatma ve net ses kullanın
#Mlops Projects İle İlgili Popüler Aramalar
🎬Video Severler İçin
Mlops Projects ReelsMlops Projects Reels İzle
📈Strateji Arayanlar İçin
Mlops Projects Trend Hashtag'leriEn İyi Mlops Projects Hashtag'leri
🌟Daha Fazla Keşfet
Mlops Projects Keşfet#mlops#joyprakash kalitas mlops projects