10 Top Machine Learning Platforms In 2023
AI
02Jan 2023
Table of Contents
With the world becoming increasingly digitized, it's no surprise that the demand for skilled machine learning engineers is increasing at an unprecedented rate. As we move into the next decade, this trend is only going to continue skyrocketing.
But what does that mean for those wanting to enter the field? In this blog, we'll look at the ten best machine learning platforms you can use to up your skills and make a name for yourself in the industry.
Top Machine Learning Platforms in 2023
1) TensorFlow
Google's TensorFlow is a perfect math library for the applications of artificial neural networks and machine learning. It is specially designed for internal use and built by the Google brain team. It is also Apache 2.0 open-source license.
It is compatible with multiple devices and supports distributed training across cluster servers. TensorFlow has been used for various applications, including neural machine translation, image recognition, object detection in images, recommender systems, and natural language processing.
2) scikit-learn
The scikit-learn library is a widely used Python library for machine learning. It provides a range of supervised and unsupervised learning algorithms and tools for data preprocessing, model evaluation, and feature extraction.
It also has a well-documented API and a user-friendly interface. scikit-learn is an open-source project that is widely used in academia and industry. It has been developed by a team of contributors from around the world and is released under the BSD 3-Clause license.
3) Apache Mahout
Apache Mahout is a platform that's specifically designed for large-scale machine learning. It's used by major companies such as Yahoo and eBay, so you know it's powerful. Mahout is a set of Java libraries that implement popular machine-learning algorithms.
However, Apache Mahout can be challenging if you're unfamiliar with Java. So if you're starting, you might consider another platform.
4) Azure Machine Learning Studio
Azure Machine Learning Studio is a cloud-based platform that Microsoft provides. It's used for data analysis and machine learning tasks. The platform is easy to use and can be accessed by anyone with a Microsoft account.
There are many different ways to use Azure Machine Learning Studio. Some everyday tasks include data preparation, model training, and model deployment. Data preparation involves tasks such as data cleansing and feature engineering. This is important because your data's quality will affect your machine-learning models' performance.
5) IBM Watson Studio
IBM Watson Studio is an integrated platform of tools, services, data, and meta-data to help businesses and organizations drive value from their data. You can build and train models, manage deployments, and collaborate with business partners.
You can use Watson Studio to work with all kinds of data, including structured data like CSV files, unstructured data like text and images, and streaming data. You can also use it to build models using popular machine-learning libraries.
Watson Studio is available in two versions: Free and Standard. The Free version offers limited storage and collaboration features, while the Standard version has no limits on storage or collaboration.
Also Read | Machine Learning In Healthcare
6) Amazon SageMaker
Amazon SageMaker is an auto-managed service that allows developers and data scientists to manage their models efficiently. It eliminates heavy lifting from every step of the machine-learning process and makes it simple.
With Amazon SageMaker, data scientists and developers can focus on the core ML algorithm. At the same time, Amazon takes care of all the undifferentiated heavy lifting required to set up and manage a machine learning environment. This includes provisioning and managing instances, storing and retrieving model artifacts, tuning hyperparameters, monitoring training jobs, deploying models into production, and more.
7) Google Cloud AutoML
Google Cloud AutoML is a cloud-based tool that allows developers to train and deploy machine learning models with minimal coding. It offers a drag-and-drop interface that makes it easy to build models without a prior machine-learning experience.
AutoML is designed to be used by businesses with limited data science and machine learning resources. It is also well-suited for new machine learning developers who want to start quickly. Google Cloud AutoML is based on a neural architecture search (NAS) algorithm. NAS is a technique for automatically finding the best neural network architecture for a given problem.
8) Rainbird
Rainbird is a machine-learning platform that enables you to generate and deploy predictive models automatically. It does this by providing you with tools that allow you to quickly and easily build, train, and deploy your models. Rainbird also provides an extensive set of APIs that allow you to integrate your machine-learning models into your existing applications and workflows.
Rainbird is an excellent platform for machine learning because it is easy to use and provides much flexibility. It also has a robust set of tools that allow you to build, train, and deploy your models quickly.
9) Infosys Nia
Infosys Nia is an artificial intelligence platform that enables businesses to design, build and run intelligent applications. It is a part of Infosys' AI portfolio, including the Infosys Mana platform and the Infosys Meadows analytics platform.
The Nia platform has been designed to help businesses harness the power of artificial intelligence (AI) and machine learning. It offers different features that make it easy for businesses to develop and deploy AI applications. Infosys Nia is built on an open, modular architecture that allows businesses to integrate easily with their existing systems and processes.
The platform includes several pre-built AI models that can be used out-of-the-box or customized to meet the specific needs of a business. It also offers multiple tools and services businesses can use to build and train their AI models.
10) BigML
BigML is a cloud-based machine learning platform that makes it easy to build and deploy predictive models. With BigML, you can bring your data to the cloud and use our web-based interface to train and deploy models with just a few clicks.
It is ideal for organizations that want to make data-driven decisions but don't have the resources or expertise to build and maintain their machine-learning infrastructure. BigML makes it easy to get started with machine learning, and our pay-as-you-go pricing model means you only pay for what you use.
Wrapping Up
These are just a few of the many machine-learning platforms available today. To choose the right platform for your needs, it's essential to understand your requirements and objectives. Do you need a platform that is easy to use or offers more flexibility? Do you need a platform that can be integrated with your existing systems or standalone?
Once you understand your needs, you can evaluate the options and choose the best platform.
Harnil Oza is the CEO & Founder of Hyperlink InfoSystem. With a passion for technology and an immaculate drive for entrepreneurship, Harnil has propelled Hyperlink InfoSystem to become a global pioneer in the world of innovative IT solutions. His exceptional leadership has inspired a multiverse of tech enthusiasts and also enabled thriving business expansion. His vision has helped the company achieve widespread respect for its remarkable track record of delivering beautifully constructed mobile apps, websites, and other products using every emerging technology. Outside his duties at Hyperlink InfoSystem, Harnil has earned a reputation for his conceptual leadership and initiatives in the tech industry. He is driven to impart expertise and insights to the forthcoming cohort of tech innovators. Harnil continues to champion growth, quality, and client satisfaction by fostering innovation and collaboration.
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