MLOps — How to start this journey?
In this post, I will discuss step-by-step guidance to learn MLOps. But before we start, few important points or cautions
- MLOps is not about throwing a tool.
- MLOps is an Engineering discipline like DevOps or SDLC.
- Mastering MLOps needs continuous learning and it will only come by spending sufficient time in various kinds of project implementation, reading various patterns, architectures, and so on.
- MLOps is a multi-disciplinary area that includes knowledge of AI/ML, DevOps, Data Engineering, Cloud Native patterns, Infrastructure, Version Control, ethics, etc.
So, the moral is, there is no shortcut or a quicker way to master. But we can always take a curated and methodical step to start the journey.
So, let's get started…
- Basic training on classical Machine Learning.
- Basic training on either Pytorch or Tensorflow framework to understand the code construct.
- Knowledge of Pandas, Numpy, SQL
Step #1: A High-level Overview
A quick high level read on the basics of MLOps with Architecture in this series (MLOps aka Operational AI)
Duration: 30 mins
MLOps aka Operational AI (Part-2)
In the previous post, we have seen a brief introduction of MLOps, steps, and ML production challenges.
MLOps aka Operational AI (Part-3)
In this article, we will see the E2E flow in operationalizing AI on popular open-source tools. The idea is to make the…
Step #2: MLOps for beginners — Objective to Production
I found this series quite good and covers E2E of MLOps.
- The 2nd top Github repo in MLOps
- Videos and hands-on with Notebooks to practice
- Very lucid and easy to progress tutorials
- Not specific to any particular cloud products
Duration: 3–4 weeks
Outlining Objectives for ML Systems - Made With ML
Repository ·Video 📬 Receive new lessons straight to your inbox (once a month) and join 30K+ developers in learning how…
Step #3: Understand related concepts
This may be in parallel with Step #2 as individuals are going through the chapters. Please note that below are many curated lists. But please feel free to explore more.
Duration: 1 week
- What is Cloud Native and why we need it?
What is cloud-native and why does it exist? | Cloud Native Computing Foundation
Program Speakers: Alexis Richardson, CNCF TOC Chair & CEO Weaveworks This talk will provide insights into the history…
- Core Principals of Cloud Native architecture.
5 principles for cloud-native architecture-what it is and how to master it | Google Cloud Blog
At Google Cloud, we often throw around the term 'cloud-native architecture' as the desired end goal for applications…
- Adoption of Cloud-Native Architecture, Part 1: Architecture Evolution and Maturity
Adoption of Cloud-Native Architecture, Part 1: Architecture Evolution and Maturity
In this article, authors Srini Penchikala and Marcio Esteves discuss what organizations should assess when adopting…
- Understand what is CNCF landscape
The beginner's guide to the CNCF landscape | Cloud Native Computing Foundation
The cloud native landscape can be complicated and confusing. Its myriad of open source projects are supported by the…
CNCF Cloud Native Interactive Landscape
The Cloud Native Trail Map ( png, pdf) is CNCF's recommended path through the cloud native landscape. The cloud native…
- Read about Microservices architecture and its relevance and challenges
Why do we hit a wall when introducing microservice architecture? | Cloud Native Computing…
Guest post by Fred Chien （錢逢祥, Brobridge) Understanding various technical issues and pitfalls of microservice…
Step #4: Explore concepts and technologies more deeper
This step is going into more detail and getting yourself familiar with cloud-native tools and technologies and concepts worth understanding it. It is not a complete list as this area is extremely vast and diverse.
Duration: 4–6 weeks (May change depends on the depth you are planning to go)
- Docker Tutorial for Beginners
2. Kubernetes for container orchestration
- Complete Kubernetes Tutorial for Beginners
- Understanding Kubernetes networking basics
3. Understand API Gateway and Service Mesh concept
- Difference between API Gateway and Service Mesh
The difference between API Gateways and Service Mesh | Cloud Native Computing Foundation
Note: The goal of this piece is to provide a cheat sheet that guides the architect in deciding when to use an API…
- Istio Service Mesh Explained
Why Do You Need Istio When You Already Have Kubernetes? - The New Stack
Tetrate sponsored this post. If you've heard of service mesh and tried Istio, you may have the following questions: Why…
- Tales of the Kubernetes ingress networking
Tales of the Kubernetes ingress networking: deployment patterns for external load balancers | Cloud…
Program Speakers: Manuel Zapf, Solution Architect @Containous As workloads move from legacy infrastructure to…
5. CI/CD tools like Jenkins-X, Argo CD, Flux CD
FluxCD, ArgoCD or Jenkins X: Which Is the Right GitOps Tool for You?
GitOps-the idea to fully manage applications and infrastructure using a Git-based workflow-is gaining a lot of traction…
6. GitOps and Kubernetes CD
- GitOps and Kubernetes: An overview
- Tutorial: Everything You Need To Become a GitOps Ninja — Alex Collins & Alexander Matyushentsev
7. Seldon Core for deploying ML Models and Monitoring
- Seldon Core installation on Kubernetes (Openshift)
Seldon Core Installation on Kubernetes (OpenShift)
In this post, we will explore the installation of Seldon Core (the latest stable) with OpenShift Kubernetes.
- Seamless MLOps with Seldon and MLflow
- Sidecar Pattern
- Microservices Design Pattern
Microservices Pattern: Microservice Architecture pattern
You are developing a server-side enterprise application. It must support a variety of different clients including…
- What are microservices really all about? — Microservices Basics Tutorial
The list is endless, but I will stop here. These steps and links will give quick boost to learn MLOps and all associated concepts faster.
- An awesome list of references for MLOps
- Awesome production machine learning