Here are some popular machine
learning services:

1.
Amazon SageMaker (AWS)

• Features:
  Amazon SageMaker provides an end-to-end ML service. It supports data
  preparation, model training, and deployment. SageMaker also has pre-built
  algorithms and offers a built-in Jupyter notebook for easy
  experimentation.


• Use Cases:
  Ideal for users who are already integrated into the AWS ecosystem and need
  scalable solutions for training and deployment.

2.
Google Vertex AI (GCP)

• Features:
  Google’s Vertex AI enables training, deployment, and monitoring of machine
  learning models at scale. It integrates well with other Google Cloud
  services and supports AutoML for users who want to build models without
  much expertise.


• Use Cases:
  Suitable for users in Google’s cloud ecosystem or for those needing AI
  services like AutoML, image recognition, or natural language processing.

3.
Microsoft Azure Machine Learning

• Features:
  Azure ML provides a comprehensive set of tools to build, train, and deploy
  models. It integrates with other Azure services and supports open-source
  frameworks like TensorFlow, PyTorch, and Scikit-learn.


• Use Cases:
  Best for enterprise-grade solutions, especially for organizations already
  utilizing Microsoft services.

4.
IBM Watson Studio

• Features:
  Watson Studio offers tools for building and training models, managing AI
  workflows, and integrating them into business applications. It’s known for
  strong support in NLP and AI-driven business insights.


• Use Cases:
  Ideal for enterprises needing robust AI solutions, especially in fields
  like healthcare, financial services, and customer engagement.

5.
DataRobot

• Features:
  DataRobot focuses heavily on automated machine learning (AutoML). It
  provides a platform that simplifies the process of building predictive
  models and deploying them in production.


• Use Cases:
  Best for users who want to leverage AutoML to build models without deep
  expertise in machine learning.

6.
H2O.ai

• Features:
  H2O.ai is another AutoML service that allows users to build machine
  learning models with limited coding. It supports various data science
  tools and frameworks.


• Use Cases:
  Great for users who need scalable machine learning solutions but don’t
  want to manage the underlying infrastructure.

7.
Algorithmia

• Features:
  Algorithmia specializes in deploying machine learning models. It supports
  the entire MLOps lifecycle, from model deployment to versioning and
  monitoring.


• Use Cases:
  Ideal for organizations looking for a platform to serve machine learning
  models in production environments.

8.
Paperspace Gradient

• Features:
  Gradient provides cloud-based machine learning tools for collaboration,
  training models, and running Jupyter notebooks. It's known for offering
  high-performance GPUs on demand.


• Use Cases:
  Best suited for teams and researchers looking to run intensive machine
  learning experiments with easy access to hardware.

These services can help users
streamline their machine learning workflows, from development to deployment,
without requiring expertise in the technicalities of infrastructure management.