Azure Machine Learning is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions.
From a developer's perspective, I find the price of this solution high.
The licensing cost is very cheap. It's less than $50 a month.
From a developer's perspective, I find the price of this solution high.
The licensing cost is very cheap. It's less than $50 a month.
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.
The pricing is complicated as it is based on what kind of machines you are using, the type of storage, and the kind of computation.
The support costs are 10% of the Amazon fees and it comes by default.
The pricing is complicated as it is based on what kind of machines you are using, the type of storage, and the kind of computation.
The support costs are 10% of the Amazon fees and it comes by default.
Build, deploy, and scale ML models faster, with pre-trained and custom tooling within a unified artificial intelligence platform.
The price structure is very clear
The solution's pricing is moderate.
The price structure is very clear
The solution's pricing is moderate.
The Azure OpenAI service provides REST API access to OpenAI's powerful language models including the GPT-3, Codex and Embeddings model series. These models can be easily adapted to your specific task including but not limited to content generation, summarization, semantic search, and natural language to code translation. Users can access the service through REST APIs, Python SDK, or our web-based interface in the Azure OpenAI Studio.
The cost structure depends on the volume of data processed and the computational resources required.
The pricing is acceptable, and it's delivering good value for the results and outcomes we need.
The cost structure depends on the volume of data processed and the computational resources required.
The pricing is acceptable, and it's delivering good value for the results and outcomes we need.
TensorFlow is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.
TensorFlow is free.
We are using the free version.
TensorFlow is free.
We are using the free version.
Google AI Platform is a managed service that enables you to easily build machine learning models, that work on any type of data, of any size. Create your model with the powerful TensorFlow framework that powers many Google products, from Google Photos to Google Cloud Speech.
The price of the solution is competitive.
For every thousand uses, it is about four and a half euros.
The price of the solution is competitive.
For every thousand uses, it is about four and a half euros.
We've built this course as an introduction to deep learning. Deep learning is a field of machine learning utilizing massive neural networks, massive datasets, and accelerated computing on GPUs. Many of the advancements we've seen in AI recently are due to the power of deep learning. This revolution is impacting a wide range of industries already with applications such as personal voice assistants, medical imaging, automated vehicles, video game AI, and more.
It is free.
PyTorch is an open-source solution.
It is free.
PyTorch is an open-source solution.
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors.
