We can help you set up your ML development pipeline or solve your ML challenge.

We are a team of experienced ML developers. We constantly test and evaluate numerous ML tools, including those of our competitors, to provide you with the most accurate and objective information.

This table represents the scores our ML developers have given to the most popular ML development platforms, including Neuro Platform. The scoring procedure is ongoing and is described in detail below the table. If you click on a platform’s name, you will see a detailed overview. If you have any comments or would like a similar platform to be evaluated by our team, please let us know through our ML Developers Forum.

Developer ExperienceML EnvironmentsData IngestionAI Starter KitsCollaborationBring Your Own CloudEnterprise-readyTotal
Neuro Platform333332118
AWS SageMaker113331315
Google AI Platform211331314
Azure Machine Learning332331318
Paperspace Gradient232333117
Determined AI212333216

We evaluate the platforms by seven major features (see below) and give each product a score from 0 to 3, where 3 denotes the highest level of this feature’s support. Thus, the maximum score a platform might get is 21.

We also provide a benchmark project that implements the basic ML development scenario: uploading the data, training the model, and evaluating the results. In the master branch you can see how to implement this scenario on your remote server, while other branches will show you the same scenario on different platforms. Comparing these branches with the master branch will show you how many changes are required to move your project to a given platform.

Developer Experience#

It is simple to start a new project or to integrate existing work into a project. The platform encourages best practices through carefully-designed project templates and a rich collection of tutorials. Complex tasks such as remote debugging, hyperparameter tuning, and training progress monitoring are executed easily. All through a combination of common operations, easily accessible through command-line and browser interfaces. For more advanced scenarios a platform client is available as a Python package.

ML Environments#

The platform provides the basic environment with tools and packages required for an ML project. It also lets you bring in ML frameworks, languages and the tools of your choice. Beyond that, it makes it possible for you to integrate a broad range of open source frameworks and to connect to commercial ML services, such as data labeling or experiment tracking.

Data Ingestion#

You can upload datasets from your local environment, object storage, or web URLs and access them from your ML environment. The platform performs equally well on large and small files, and files can be synchronized between local and remote environments. For file system operations, you can use command line, browser, or both.

AI Starter Kits#

Designed to solve common high-level tasks end to end, with little or no coding required, AI started kits are available on the platform: ready-to-use ML algorithms and examples for Vision, Language, Recommendation, Speech, and more.


Engineers can securely share and access ML environments, datasets and development sessions, while non-engineers are also integrated into the process and can upload data and access results.

Bring Your Own Cloud#

The platform can be deployed into and integrated with your own cloud environment, utilizing the resources and infrastructure of your cloud provider and helping to reduce cloud provider lock-in risk. Your development environment and data continue to reside within your cloud security perimeter. The platform supports major cloud providers (AWS, GCP, and Azure).


The platform is packed with features essential for successful rollout and operation in an enterprise environment. Managing users from a single, central directory. A detailed trail of account activity. Allocation of privileges by user role. Reports necessary to demonstrate operational status and value gained from the use of the platform.