As part of my journey as a VMware vExpert 2025 🌟, I’ve started exploring labs that combine Artificial Intelligence (AI), Machine Learning (ML), and the Tanzu Platform.
This first lab gave me a hands-on view of how to host, serve, and manage AI/ML models on private infrastructure — while also introducing the essential concepts behind MLOps, the DevOps-flavored discipline tailored to machine learning.
Let me take you through the highlights 👇
🔹 Part 1: AI, ML, and MLOps – A Global View
Before diving into VMware Tanzu, the lab walks through the foundations of AI and ML:
- 🤖 Artificial Intelligence (AI): Systems simulating human intelligence.
- 📊 Machine Learning (ML): Algorithms learning from data without explicit programming.
- 🧠 Deep Learning (DL): Neural networks mimicking the human brain.
- ✨ Generative AI: Producing text, images, and more from learned patterns.
What about MLOps?
- ⚙️ MLOps: Applying DevOps principles to ML — automating deployment, monitoring, and retraining of models.
- 🧬 DLOps: Focusing specifically on deep learning models.
- 📚 LLMOps: Targeted at Large Language Models and Generative AI.
The Key Personas
In any ML project, multiple roles collaborate:
- 👷 ML Platform Engineer – Builds and manages the environment.
- 🛠️ MLOps Engineer – Operationalizes models into production pipelines.
- 🔬 Data Scientist – Designs, trains, and experiments with models.
And of course, depending on the company size, roles may overlap or diversify (Data Engineer, ML Engineer, AI Engineer…).
👉 Takeaway: MLOps is iterative. Models must adapt to data drift and concept drift, making monitoring and retraining essential.
🔹 Part 2: VMware Tanzu in Action
After the theory, the lab shifts into practice with VMware Tanzu 🌀.
The use case: 📸 Build an object detection platform for images (CIFAR-10 dataset with 60,000 labeled images, 10 classes).
The Required Stack
To achieve this, the lab sets up a full MLOps pipeline on Tanzu, including:
- 📓 Experimentation environment (Jupyter notebooks & alternatives).
- 🔄 Pipelines & orchestration with Argo Workflows.
- 📦 Model registry & versioning with MLflow.
- 📚 Data catalog with Datahub.
- 👀 ML Observability with Evidently.
- 🚀 CI/CD integration (GitOps ready) for automated workflows.
All of this is built on Tanzu’s cloud-agnostic foundation, meaning you can run ML workloads across any cloud or on-prem infrastructure — without becoming a Kubernetes guru.
Why Tanzu?
Because Tanzu provides: ✅ A unified way to deploy ML workloads. ✅ Flexibility to mix open-source tools. ✅ Scalability and governance with enterprise readiness.
👉 Takeaway: Tanzu makes it possible to manage end-to-end ML lifecycles — from experimentation to production, observability, and retraining.
🎯 Conclusion
This first lab was a perfect way to:
- Get familiar with AI/ML fundamentals 🔍
- Understand the roles & lifecycle of MLOps 🔄
- See how VMware Tanzu enables real ML projects ⚡
As a vExpert 2025, I’ll keep sharing my journey with Tanzu and AI/ML here on LinkedIn and on my dedicated blog 📝. Stay tuned for the next modules where we’ll go even deeper into hands-on MLOps with Tanzu.