ai platformsAs a new decade starts, much is expected to change in the world of artificial intelligence. During the last decade and beyond, AI technology proved its abilities to even the most fervent critics. Aspects of artificial intelligence, such as machine learning and natural language processing, have been incorporated in many important areas of daily living today that we can no longer live without them. 

As for data scientists and other software engineers that build AI projects, the one thing they can’t do without is an artificial intelligence framework. A long list of top AI platforms has been providing this service, and that includes some of the best machine learning and NLP application examples ever (think TensorFlow, Matlab, etc.)

But in 2020, as new updates and improvements take over the field of AI, the ranking of these AI platforms will have to shift. We look at the nine best artificial intelligence platforms for 2020, and why we think they deserve to be on the list.

1. Google Cloud AI Platform

Google’s Cloud AI platform simply deserves to be on the top of the list. Most of the other platforms below refer to it in one way or another. Millions of users love it for its ease of use, its unique models, its incredible cloud infrastructure, its architecture, its stunning visual interfaces, its library and more. 

Organizations looking to further streamline their machine learning and enterprise AI needs can only do worse without the Google Cloud AI platform.

2. Salesforce Einstein

It’s no small honor to be considered one of the best customer service software providers by many companies in the US. Salesforce knows how that feels like thanks to supplying some of the most popular customer service tools ever. The company’s foray into AI will be supported further in 2020 with its own AI platform ‘Salesforce Einstein.’ 

It is described as being very good at predictions and very spot-on with recommendations, basing on provided data.

3. IBM Watson Studio (plus IBM Watson Machine Learning Studio)

IBM’s Watson Studio has been an industry leader in the machine learning space and a top choice for enterprise AI solutions everywhere. 

The IBM Machine Learning Studio makes it simple to create, train and deploy processes of models through automation and collaboration. IBM’s AI platforms are, therefore worth a look in 2020, as they promise to get even better.

4. Microsoft Azure (plus Microsoft Machine Learning Studio)

From the software giant, Microsoft comes Microsoft Azure Machine Learning, an AI platform that has fast become popular for its enterprise solutions to practical problems. Microsoft Azure Machine Learning specializes in machine learning and the way it can be simplified for business enterprise problems. 

Data scientists that employ Microsoft Azure can model their algorithms from Python packages, Xbox, or even R to create instantly deployable web services. Some of its most attractive perks include its cloud-based options, its mobile option and its ability to support most tools, languages and frameworks.

5. TensorFlow

TensorFlow has been a popular AI platform for the last decade or so and will remain popular even in 2020. As one of the best open-source libraries around for machine learning and other aspects of artificial intelligence, it’s easy to see the attraction. But TensorFlow is more than just a cool AI platform. 

As one of the many products of Google’s Machine Intelligence Team, TensorFlow is designed for machine learning and neural networks research. Its data flow structure employs mathematical equations and graphs to create an architecture that works wonderfully for computation across platforms. 

If you’re new to TensorFlow, you’ll love its flexibility across a wide range of AI solutions, its deep learning framework, its portability and more. 

6. PegaFlow

For 2020, organizations already using the Pega AI platform can rest assured knowing they’ll derive more efficiency and automation than they already have. 

Pega is a wonderfully built platform designed to make simple the design of applications or programs in the AI space. Users build their own using proven models and can always make swift changes thanks to the agility and flexibility of the platform.

7. SparkNLP

Spark NLP is barely three years old, but it is already one of the most popular open-source AI platforms to consider. 

Its emphasis is on natural language processing and it comes with one of the biggest libraries and various NLP examples for data scientists to refer to. It’s built upon Apache Spark and TensorFlow, so its architecture is impressive too.

8. Infosys Nia

Among data scientists today, Infosys Nia and its system offerings are the stuff of legend. Ask one and they’ll highlight its unique core business renovation processes or its deep understanding of people in unique fragments. Its automation, knowledge and information platforms are some of the most referenced in the machine learning world. 

Its Aikido framework remains one of the most cost-effective, and many users report it to be an easy AI platform to use. For many enterprises in 2020, Infosys Nia will remain a favorite, thanks to its ability to deploy machine learning’s attributes for more innovation and automation.

9. Rain Bird

In 2020, one artificial intelligence platform to watch out for is Rain Bird, the award-winning software designed to make business operations smarter and more efficient. Rain Bird is the AI platform of choice for enterprises looking to automate the knowledge-work process for efficiency. 

It works by enabling the creation of smart systems that are derived from a combination of business data and existing business information. Users love it for its RBLang language, unique analytics, its controlled learning algorithms and helpful visual user interface. 

Final words

A good artificial intelligence platform makes things easier for both designers and the enterprises that use the resulting application. A good AI intelligence platform means your enterprise will make its best bets on big business without unnecessary doubts. 

Good intelligence platforms for AI also means better frameworks and bottom-less libraries, plus smarter NLP application examples and smarter machine language development. If you’re thinking of choosing an AI intelligence platform this year or just upgrading from one platform to another, these above platforms have the most potential for 2020.