Pros of KServe

• Comprehensive serverless inference platform for machine learning models
• Supports multiple frameworks and runtimes (TensorFlow, PyTorch, scikit-learn, etc.)
• Provides advanced features like canary rollouts, autoscaling, and model explainability

Cons of KServe

• Steeper learning curve for beginners due to its extensive feature set
• Requires more resources to set up and maintain compared to simpler alternatives
• May be overkill for small-scale or simple inference scenarios

Code Comparison

KServe example:

apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "sklearn-iris"
spec:
  predictor:
    sklearn:
      storageUri: "gs://kfserving-samples/models/sklearn/iris"

As the comparison is between the same repository (kserve/kserve), there isn't a different code example to provide. The code structure and usage would be identical for both cases.

Pros of BentoML

• Simpler setup and deployment process, especially for Python-based models
• Built-in support for various ML frameworks and automatic dependency management
• Flexible serving options, including HTTP, gRPC, and CLI

Cons of BentoML

• Less native integration with Kubernetes compared to KServe
• Fewer advanced features for large-scale production deployments
• Limited support for non-Python models and runtimes

Code Comparison

BentoML:

import bentoml

@bentoml.env(pip_packages=["scikit-learn"])
@bentoml.artifacts([bentoml.SklearnModelArtifact('model')])
class MyService(bentoml.BentoService):
    @bentoml.api(input=bentoml.JsonInput())
    def predict(self, input_data):
        return self.artifacts.model.predict(input_data)

KServe:

from kserve import KFModel

class MyModel(KFModel):
    def __init__(self, name):
        super().__init__(name)
        self.name = name
        self.ready = False

    def load(self):
        self.model = load_model()
        self.ready = True

    def predict(self, request):
        return self.model.predict(request['instances'])

Pros of MLflow

• More comprehensive ML lifecycle management, including experiment tracking and model registry
• Language-agnostic with support for Python, R, Java, and more
• Easier to set up and use for local development and small-scale projects

Cons of MLflow

• Less focused on model serving and deployment compared to KServe
• May require additional tools for production-grade serving at scale
• Limited built-in support for advanced serving features like A/B testing and canary deployments

Code Comparison

MLflow example (Python):

import mlflow

mlflow.start_run()
mlflow.log_param("param1", 5)
mlflow.log_metric("accuracy", 0.95)
mlflow.pytorch.log_model(model, "model")
mlflow.end_run()

KServe example (YAML):

apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "pytorch-model"
spec:
  predictor:
    pytorch:
      storageUri: "gs://kserve-models/pytorch/model"

MLflow focuses on tracking experiments and managing the ML lifecycle, while KServe specializes in model serving on Kubernetes. MLflow is more versatile for different ML workflows, but KServe offers more advanced serving capabilities for production environments.

Pros of Seldon Core

• More flexible and customizable, allowing for complex ML pipelines and custom inference servers
• Supports a wider range of deployment options, including multi-model serving and canary deployments
• Provides advanced features like A/B testing, shadow deployments, and explainers

Cons of Seldon Core

• Steeper learning curve due to its flexibility and complexity
• Requires more configuration and setup compared to KServe's simpler approach
• Less standardized approach to model serving, which may lead to inconsistencies across deployments

Code Comparison

Seldon Core deployment example:

apiVersion: machinelearning.seldon.io/v1
kind: SeldonDeployment
metadata:
  name: iris-model
spec:
  predictors:
  - graph:
      implementation: SKLEARN_SERVER
      modelUri: gs://seldon-models/sklearn/iris
    name: default

KServe deployment example:

apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
  name: sklearn-iris
spec:
  predictor:
    sklearn:
      storageUri: gs://kfserving-examples/models/sklearn/iris

Pros of Cortex

• Simpler setup and deployment process, especially for beginners
• Built-in support for popular ML frameworks like TensorFlow and PyTorch
• Automatic scaling and load balancing out of the box

Cons of Cortex

• Less flexibility in customization compared to KServe
• Smaller community and ecosystem
• Limited support for advanced features like model explainability

Code Comparison

KServe example:

from kubernetes import client, config
from kserve import KServeClient

config.load_kube_config()
kserve_client = KServeClient()

isvc = {
    "apiVersion": "serving.kserve.io/v1beta1",
    "kind": "InferenceService",
    "metadata": {"name": "sklearn-iris"},
    "spec": {
        "predictor": {
            "sklearn": {
                "storageUri": "gs://kfserving-samples/models/sklearn/iris"
            }
        }
    }
}

kserve_client.create(isvc)

Cortex example:

import cortex

cx = cortex.client("aws")

cx.deploy("sklearn-iris.yaml")

# sklearn-iris.yaml
# apiVersion: kind: name: predictor:
#   type: python
#   path: predictor.py
#   model:
#     type: sklearn
#     path: s3://cortex-examples/sklearn/iris-classifier

Both frameworks offer streamlined deployment of machine learning models, but Cortex provides a more straightforward approach with less configuration required. KServe offers more advanced features and greater customization options, making it suitable for more complex deployment scenarios.

Pros of TensorFlow Serving

• Highly optimized for TensorFlow models, offering excellent performance
• Supports model versioning and concurrent model serving
• Integrates seamlessly with TensorFlow ecosystem

Cons of TensorFlow Serving

• Limited to TensorFlow models, lacking support for other frameworks
• Steeper learning curve and more complex setup compared to KServe
• Less flexibility in customization and extensibility

Code Comparison

KServe example:

apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "mnist"
spec:
  predictor:
    tensorflow:
      storageUri: "gs://kserve-samples/models/tensorflow/mnist"

TensorFlow Serving example:

import tensorflow as tf
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2_grpc

channel = grpc.insecure_channel('localhost:8500')
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)

KServe offers a more declarative approach with Kubernetes-native resources, while TensorFlow Serving requires more code for setup and interaction. KServe provides a higher level of abstraction and easier deployment, especially in Kubernetes environments.