Hiring Kubeflow developers? Then you should know!
How and where is Kubeflow used?
- Model Training: Training ML models
- Hyperparameter Tuning: Optimizing model parameters
- Experiment Tracking: Tracking model training experiments
- Model Serving: Serving ML models
- AutoML: Automating machine learning workflows
- Data Versioning: Managing data versioning
- Multi-Framework Support: Supporting various ML frameworks
- Scalability: Scaling ML workloads efficiently
- Monitoring: Monitoring ML model performance
- Resource Management: Efficiently managing computing resources
Compare Junior, Middle, Senior, and Expert/Team Lead Kubeflow Developer roles
Seniority Name | Years of experience | Responsibilities and activities | Average salary (USD/year) |
---|---|---|---|
Junior | 0-2 years |
| $60,000 |
Middle | 3-5 years |
| $80,000 |
Senior | 6-8 years |
| $100,000 |
Expert/Team Lead | 9+ years |
| $120,000 |
Quick Facts about Kubeflow.
- Kubeflow, the open-source machine learning toolkit for Kubernetes, was created in 2017.
- It is widely used for projects involving machine learning pipelines, model serving, and hyperparameter tuning.
- The entry threshold for using Kubeflow is having a basic understanding of Kubernetes and machine learning concepts.
- Kubernetes is the most popular related technology to Kubeflow, as it provides the underlying container orchestration.
- Fun Fact: Kubeflow’s logo is a cute Kubeflow octopus, symbolizing its flexibility and adaptability in orchestrating ML workflows.
TOP Kubeflow Related Technologies
- TensorFlow
- PyTorch
- XGBoost
- Scikit-learn
- Keras
What are top Kubeflow instruments and tools?
- Katib: Hyperparameter optimization tool by Google, released in 2018
- KFServing: Model serving toolkit by NVIDIA, released in 2019
- Kubebench: Benchmarking tool by Google, released in 2020
Talk to Our Talent Expert
Our journey starts with a 30-min discovery call to explore your project challenges, technical needs and team diversity.
Maria Lapko
Global Partnership Manager