Organizations use Databricks for analytics queries, data processing, ETL, machine learning, AI, and data engineering on multi-node clusters. They appreciate its ease of use and scalability with features like a collaborative notebook interface, support for SQL, Python, and R, and excellent data processing. Databricks needs better visualization, integration, and support for improvement.
Licensing on site I would counsel against, as on-site hardware issues tend to really delay and slow down delivery.
I do not exactly know the costs, but one of our clients pays between $100 USD and $200 USD monthly.
Licensing on site I would counsel against, as on-site hardware issues tend to really delay and slow down delivery.
I do not exactly know the costs, but one of our clients pays between $100 USD and $200 USD monthly.
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.
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.
Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. No machine learning experience required.
The DataScience.com Platform makes it easy and intuitive for data science teams to work collaboratively on the data-driven projects that transform how companies do business. Explore and visualize data, share analyses, deploy models into production, and track performance - all from one place.
Amazon Augmented AI (Amazon A2I) makes it easy to build the workflows required for human review of ML predictions. Amazon A2I brings human review to all developers, removing the undifferentiated heavy lifting associated with building human review systems or managing large numbers of human reviewers.
WPS is used in production on a huge range of systems at sites around the world. Thousands of analysts are using WPS in their daily data processing and analysis tasks, and thousands more rely on the heavy-lifting ability of WPS overnight and scheduled processing to deliver the information they require. Customers are able to choose to run WPS software on IBM z/OS Mainframes, servers and workstations running UNIX, Linux, Solaris, AIX, macOS and the full range of Microsoft Windows operating systems to fit in with existing or planned infrastructure.
IBM Watson OpenScale makes it easier for data scientists, application developers, IT and AI operations teams, and business-process owners to collaborate in building, running, and managing production AI. This empowers businesses to confidently integrate machine learning capabilities into their applications and scale seamlessly as the demand for AI grows.
SAS Visual Data Mining and Machine Learning combines data wrangling, data exploration, visualization, feature engineering, and modern statistical, data mining and machine learning techniques all in a single, scalable in-memory processing environment. This provides faster, more accurate answers to complex business problems, increased deployment flexibility and one easy-to-administer and fluid IT environment.
