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Neptune vs Amazon SageMaker

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Neptune

Amazon SageMaker

Commercial Requirements

Commercial requirements

Standalone component or a part of a broader ML platform?

Standalone component

Part of the AWS SageMaker ecosystem

Is the product delivered as commercial software, open-source software, or a managed cloud service?

Managed cloud service

What is the pricing model?

Free plan for individuals and small teams, paid plans for larger teams and enterprises

Pricing depends on usage. You can get started for free with AWS free tier. Costs can be estimated using the AWS Pricing Calculator. Read more here

SLOs / SLAs: Does the vendor provide guarantees around service levels?

Only for enterprise customers

Yes

Support: Does the vendor provide 24×7 support?

Only for enterprise customers

Only for business and enterprise plans

SSO, ACL: does the vendor provide user access management?

Only for enterprise customers

Only for business and enterprise plans

Security policy and compliance

Yes

Only for business and enterprise plans

General Information

Deployment

Cloud (SaaS)

Yes

Self-hosted (your infrastructure)

Yes

It cannot be deployed on-premises. You can, however, deploy it on your Virtual Private Cloud on AWS.

– On-prem bare metal

Yes

– Private Cloud: Amazon Web Services (AWS)

Yes

It cannot be deployed on-premises. You can, however, deploy it on your Virtual Private Cloud on AWS.

– Private Cloud: Google Cloud Platform (GCP)

Yes

– Private Cloud: Microsoft Azure

Yes

Setup

What are the infrastructure requirements?

No special requirements other than having the neptune-client installed and access to the internet if using managed hosting. Check here for infrastructure requirements for on-prem deployment.

Just internet access is needed to access the AWS ecosystem

How much do you have to change in your training process?

Minimal. Just a few lines of code needed for tracking. Read more

AWS Application Migration Service lets you lift-and-shift your code to AWS without any change required. Minimal change required to migrate Jupyter notebooks from local to Sagemaker Studio

Does it integrate with the training process via CLI/YAML/Client library?

Yes, through the neptune-client library

Sagemaker CLI and Sagemaker Python SDK

Does it come with a web UI or is it console-based?

Web UI

Both web UI and CLI

Serverless UI

Flexibility, speed, and accessibility

Customizable metadata structure

Yes

How can you access model metadata?

– gRPC API

Yes

– CLI / custom API

Yes

– REST API

Requires using additional AWS services like Lambda or API Gateway

– Python SDK

– R SDK

– Java SDK

– Julia SDK

Supported operations

– Search

– Update

– Delete

– Download

Distributed training support

Yes

Pipelining support

Yes

Logging modes

– Offline

– Disabled/off

– Asynchronous

– Synchronous

Live monitoring

Mobile support

Webhooks and notifications

Capabilities

Log and display of metadata

Dataset

– location (path/s3)

– hash (md5)

– Preview table

– Preview image

– Preview text

– Preview rich media

– Multifile support

Code versions

– Git

– Source

– Notebooks

Parameters

Metrics and losses

– Single values

Yes

– Series values

Yes

– Series aggregates (min/max/avg/var/last)

Yes

Tags

Yes

Descriptions/comments

Yes

Limited

Rich format

– Images (support for labels and descriptions)

Yes

– Plots

Yes

– Interactive visualizations (widgets and plugins)

– Video

– Audio

– Neural Network Histograms

– Prediction visualization (tabular)

Limited to logging

– Prediction visualization (image)

– Prediction visualization (image – interactive confusion matrix for image classification)

– Prediction visualization (image – overlayed prediction masks for image segmentation)

– Prediction visualization (image – overlayed prediction bounding boxes for object detection)

Hardware consumption

– CPU

Yes

– GPU

Yes

– TPU

– Memory

Yes

System information

– Console logs (Stderr, Stdout)

Yes

– Error stack trace

Yes

– Execution command

Yes

– System details (host, user, hardware specs)

Yes

Environment config

– pip requirements.txt

Yes

– conda env.yml

Yes

– Docker Dockerfile

Yes

Files

– Model binaries

Yes

Can only log model binaries saved in S3

– CSV

Yes

Can only log files saved in S3

– Any file

All files can be logged, but some formats might not be rendered

Can only log files saved in S3

– External file reference (s3 buckets)

Yes

Can only log files saved in S3

Explanations (SHAP, DALEX)

Limited

Yes

Comparing experiments

Table format diff

Yes

Overlayed learning curves

Yes

Parameters and metrics

– Groupby on experiment values (parameters)

Yes

– Parallel coordinates plots

Yes

– Parameter Importance plot

Rich format (side by side)

– Image

Yes

– Video

– Audio

– Plots

– Interactive visualization (HTML)

– Text

Yes

– Neural Network Histograms

– Prediction visualization (tabular)

Yes

– Prediction visualization (image, video, audio)

Code

– Git

– Source files

– Notebooks

Yes

Environment

– pip requirements.txt

– conda env.yml

– Docker Dockerfile

Hardware

– CPU

Yes

– GPU

Yes

– Memory

Yes

System information

– Console logs (Stderr, Stdout)

– Error stack trace

– Execution command

– System details (host, owner)

Yes

Data versions

– Location

Yes

– Hash

Yes

– Dataset diff

Yes

– External reference version diff (s3)

Yes

Files

– Models

– CSV

Custom compare dashboards

– Combining multiple metadata types (image, learning curve, hardware)

Yes

– Logging custom comparisons from notebooks/code

– Compare/diff of multiple (3+) experiments/runs

Yes

Limited

Organizing and searching experiments and metadata

Experiment table customization

– Adding/removing columns

Yes

– Renaming columns in the UI

Yes

– Adding colors to columns

Yes

– Displaying aggregate (min/max/avg/var/last) for series like training metrics in a table

Yes

– Automagical column suggestion

Yes

Experiment filtering and searching

– Searching on multiple criteria

Yes

– Query language vs fixed selectors

Query language

Fixed Selectors

– Saving filters and search history

Yes

Custom dashboards for a single experiment

– Can combine different metadata types in one view

Yes

– Saving experiment table views

Yes

– Logging project-level metadata

Yes

– Custom widgets and plugins

Tagging and searching on tags

Yes

Nested metadata structure support in the UI

Yes

Reproducibility and traceability

One-command experiment re-run

Experiment lineage

– List of datasets used downstream

Yes

– List of other artifacts (models) used downstream

Yes

– Downstream artifact dependency graph

Yes

Reproducibility protocol

Limited

Is environment versioned and reproducible

Yes

Saving/fetching/caching datasets for experiments

Collaboration and knowledge sharing

User groups and ACL

Only for enterprise customers

Yes

Sharing UI links with project members

Yes

Sharing UI links with external people

Yes

Commenting

Limited

Interactive project-level reports

Model lineage and evaluation history

History of evaluation/testing runs

Yes

Support for continuous testing

Yes

Users who created a model or downstream experiments

Yes

Access control, model review, and promoting models

Locking model version and downstream runs, experiments, and artifacts

Adding annotations/comments and approvals from the UI

Limited

Yes

Model compare (current vs challenger etc)

Limited

Compatibility audit (input/output schema)

Compliance audit (datasets used, creation process approvals, results/explanations approvals)

Limited

CI/CD/CT compatibility

Webhooks

Model accessibility

Support for continuous testing

Yes

Integrations with CI/CD tools

Limited

Model packaging

Native packaging system

Yes

Compatibility with packaging protocols (ONNX, etc)

Yes

One model one file or flexible structure

One model one file

Integrations with packaging frameworks

Yes

Integrations and Support

Languages

Java

Yes

Julia

Yes

Python

Yes

REST API

Model training

Catalyst

CatBoost

fastai

FBProphet

Amazon’s own Prophet distribution

Gluon

Yes

HuggingFace

H2O

LightGBM

Paddle

PyTorch

PyTorch Ignite

PyTorch Lightning

Scikit Learn

Skorch

Spacy

Spark MLlib

Yes

Statsmodel

TesorFlow / Keras

Yes

XGBoost

Yes

Hyperparameter Optimization

Hyperopt

Yes

Keras Tuner

Limited

Optuna

Yes

Ray Tune

Scikit-Optimize

Limited

Model visualization and debugging

DALEX

Yes

Netron

SHAP

Yes

TensorBoard

Yes

IDEs and Notebooks

JupyterLab and Jupyter Notebook

Google Colab

Deepnote

AWS SageMaker

N/A

Data versioning

DVC

Yes

Orchestration and pipelining

Airflow

Yes

Argo

Kedro

Yes

Kubeflow

Yes

ZenML

Yes

Experiment tracking tools

MLflow

Sacred

TensorBoard

CI/CD

GitHub Actions

Yes

Gitlab CI

Yes

CircleCI

Yes

Travis

Jenkins

Model serving

Seldon

Yes

Databricks

Model versioning

Seldon

Fiddler.ai

Yes

Arthur.ai

LLMs

LangChain

Yes

This table has been updated on 6 November 2023. Some information may be outdated.
Report outdated information here.

What are the key advantages of Neptune then?

Standalone component, easy-to-integrate with multiple ML frameworks
Customizable metadata structure and custom compare dashboards
Dataset and model versioning
More developed collaboration and sharing features

Explore all features

Already using Amazon SageMaker?

You can use Neptune to improve the tracking component. SageMaker and Neptune can be easily integrated and provide even more value together.

Many teams have successfully integrated Neptune with their SageMaker pipelines and achieved better results this way.

Read case study

It only takes 5 minutes to integrate Neptune with your code

Don’t overthink it

neptune.ai demo [20min]

How deepsense.ai Tracked and Analyzed 120K+ Models Using Neptune

How ReSpo.Vision Uses Neptune to Easily Track Training Pipelines at Scale

Building a Machine Learning Platform

Learnings From Building the ML Platform at Mailchimp

neptune.ai demo [20min]

How deepsense.ai Tracked and Analyzed 120K+ Models Using Neptune

How ReSpo.Vision Uses Neptune to Easily Track Training Pipelines at Scale

Building a Machine Learning Platform

Learnings From Building the ML Platform at Mailchimp

Neptune vs Amazon SageMaker

Neptune

Amazon SageMaker

Commercial Requirements

General Information

Capabilities

Integrations and Support

What are the key advantages of Neptune then?

Already using Amazon SageMaker?

It only takes 5 minutes to integrate Neptune with your code