TensorFlow Alternatives
TensorFlow Alternatives
TensorFlow Serving is a deployable, adaptable, high-performance solution for machine learning models built for production usage. It allows easy deployment of algorithms and tests while retaining the same server architecture and APIs. TensorFlow Serving integrates easily with TensorFlow models and can be readily extended to incorporate other forms of data.
Tensorflow is a machine learning and artificial intelligence software that helps search engine optimization.
In this post, we’ll look at some alternatives to TensorFlow.
Top 10 Alternatives to TensorFlow Alternatives
1. MATLAB
MATLAB is a software package for mathematical computation, data analysis, and visualization. It is also used for engineering and scientific applications.
MATLAB is available for different platforms, including Windows, Mac OS X, and Linux. It can be downloaded from the MathWorks website.
IBM Watson Studio is a cloud-based platform that helps you build, train and deploy AI models. It’s powered by the Watson AI engine, which gives you access to the latest advancements in AI.
Google Cloud AI Platform is a suite of services that enables developers to build, train, and deploy AI models. It includes a range of pre-built AI services, such as vision, speech, translation, and natural language processing, as well as tools for data preparation, training, debugging, and monitoring.
Amazon SageMaker is a machine learning platform that enables you to quickly build, train, and deploy machine learning models at scale.
Amazon SageMaker includes a wide variety of built-in algorithms and pre-built models, so you can get started quickly without any prior experience in machine learning. You can also use Amazon SageMaker to train custom models using your own data.
AutoML is a suite of machine learning products and services offered by Google Cloud. It enables developers with limited machine learning expertise to build high-quality models.
6. RapidMiner
RapidMiner is a software platform used by data scientists to make data-driven decisions. It enables users to quickly and easily build predictive models, identify trends and correlations, and recommend actions.
Azure Machine Learning Studio is a cloud-based service for developing and deploying machine learning models. It provides a drag-and-drop interface, making it easy to build models without having to write any code. Models can be trained using your own data or pre-trained models from the Azure Machine Learning Gallery.
8. RStudio
RStudio is a software development environment for R, a programming language for statistical computing and graphics. It includes a console, editor, and debugger.
9. Alteryx
Alteryx is a data science platform that enables analysts to easily prep, blend, and analyze data for predictive insights. Alteryx provides a complete end-to-end solution from data preparation all the way through to deployment and predictive modeling.
Alteryx is designed for both business and technical users, making it the perfect tool for data-driven organizations. Alteryx is used by some of the world’s leading companies, including Coca Cola, Salesforce, and Experian.
If you’re looking for a powerful data science platform, Alteryx is the tool for you.
10. Qubole
Qubole is an analytics solution that harnesses the power of Hadoop, MapReduce, HBase and Spark clusters to generate insights from data collected in real time from websites, social media channels, connected devices and other sources.
With Qubole Server you can deploy and manage Hadoop, MapReduce, HBase and Spark clusters on any cloud provider or on-premises environment. Qubole’s visual interface makes it easy to analyze data stored in any format, including CSV, JSON, Parquet and Avro. You can also use SQL to query data stored in Hive tables.