Introduction

For years, businesses all around the world have been handling their operations and analysis manually, resulting in increased labor costs, decreased productivity, lower Client Satisfaction, increased human error, and time-consuming while delivering sub-optimal results.

No matter what industry you’re in, businesses need to analyze their data, make data-driven decisions, handle large datasets, automate repetitive, tedious, and complex workflows that tend to lower their production and also decrease their operational cost.

To overcome these challenges, businesses have no option but to use artificial intelligence to analyze data, automate their business process, increase productivity, decrease labor costs, minimize human error, and make complex decisions based on that data.

AI refers to how computer systems handle and process massive amounts of datasets to mimic human intelligence and reasoning to learn, recognize patterns and make decisions based on those patterns, predict and recommend what to do next.

AI systems are trained using big data with the help of Machine Learning that employs algorithms to analyze massive amounts of structured and semi-structured data, learn from them, make predictions, and take actions. Deep Learning uses algorithms and an “artificial neural network” that is driving today’s AI models and applications. 

This blog will look at some of the AI platforms available for businesses that can help them develop intelligent applications and tools.

What is an Artificial intelligence platform?

Top 10 AI Platforms for 2021

AI Platform is a framework designed for developers and data scientists to build, test, and deploy machine learning models and intelligent applications with the aid of open-source products, cognitive technologies, and other tools.

AI software platforms support the ability to analyze, collect, label, organize, and access data from complex, structured & unstructured data, computer code, and even industry-specific jargon to make informed decisions and deliver better experiences.

Some platforms offers pre-integrated algorithms with simplistic workflows that enable them to build models and develop applications using a feature such as drag-and-drop modeling, while others require knowledge of development and coding.

In addition to other machine learning capabilities, these algorithms may include categorization, clustering, filtering, alerting, navigation, hypothesis generation, question visualization, filtering, alerting, navigation, natural language processing, image & voice recognition, predictive analytics, text analytics, and more. 

Top AI platforms

Let us look at some of the top AI platforms and see what they have to offer.

Google AI Platform

Google AI platform offers AI capabilities such as machine learning, Natural Language Processing, deep learning, and Speech and Vision capabilities for the development of applications on the Cloud. Google AI Platform enables businesses to build and deploy ML models faster with custom tooling and pre-trained APIs within a unified AI platform. 

Google AI platform offers a suite of services that covers the end-to-end spectrum of ML services, including data gathering, preparation, training, tuning, deploying, collaborating, and sharing of machine learning models. Google AI platform takes advantage of AutoML to build models with fewer lines of code required to train a model with custom libraries.

Google AI platform offers end-to-end data integration and supports all open-source frameworks such as TensorFlow, PyTorch, and sci-kit-learn.

Pros

Time-saving – You don’t need to spend time customizing AI libraries and algorithms and

data as they are already available in the Cloud

Google BigQuery, Pubsub, dataflow, and storage integration makes it easy to access the data pipeline.

Helps build advanced ML models with custom tooling, without code and less expertise

Offers pre-trained APIs for vision, video, natural language, and more that can be easily integrated with existing applications.

Cons

Customizing existing modules and libraries is more complex and requires time and technical knowledge.

Lack of support and reference materials to build our own architecture.

Microsoft Azure

Microsoft Azure offers a unified platform for machine learning with its APIs and infrastructural services. With cloud-based advanced analytics design, the Microsoft Azure platform simplifies machine learning for businesses.

It offers product features for all skill types via a code-first and drag-and-drop UI and automated machine learning. It also features MLops tooling capabilities to manage, deploy and monitor within the automated pipelines and can be integrated with existing DevOps processes.

Azure Machine Learning supports open-source frameworks and languages like Python, R, PyTorch, MLflow, Kubeflow, ONNX, and TensorFlow. Thereby enabling developers to deploy the finished model as a web service in minutes.

Pros

User-friendly interface with drag and drop functionality to build data models

Machine Learning Experiments can be deployed as a web service.

Offers code samples in different programming languages to execute created Web Services

Integrates well with other Microsoft products

With R and Python integration, machine learning models can be created in R, and python language deploys production-ready models to Azure.

Cons

Offers limited machine learning Algorithms

Hard to integrate the data for creating the model.

TensorFlow

TensorFlow is an open-source software library that uses data flow graphs to perform numerical computations. It offers a flexible ecosystem of tools, libraries, and community resources that lets users assigning the computation across multiple platforms like CPUs, GPUs, TPUs in a desktop, mobile device, or server with an API.

Tensorflow is said to possess true portability, deep flexibility, multiple language options, auto differentiation option, multiple language options that are said to maximize performance. 

Pros

Offers easy integration with high-level libraries like Keras and Estimators

Support for many libraries and programming languages.

Offers great data visualization under the TensorFlow board for training models.

Cons

TensorFlow has a unique structure, which makes it hard to find an error and debug it.

Long learning curve—it takes a lot of time to learn its basics.

AWS AI Services

Amazon offers an extensive list of services when it comes to artificial intelligence, machine learning services, and supporting cloud infrastructure that businesses can utilize to gain better insights into their customers.

It offers pre-trained artificially intelligent services for the fields of computer vision, recommendation, language, and forecasting.

AWS provides a broad portfolio of ML infrastructure services and supports Machine Learning frameworks, open-source toolkits, and libraries to customize machine learning algorithms. 

Pros

Offer wide products and features with an expanding list of add-ons and integrations from third parties.

Offers wide support with different machine learning frameworks

AWS provides a broad array of computing choices

Cons

Not enough documentation available on the latest features

IBM Watson

IBM Watson offers an open AI for any public, private, or hybrid cloud environment and comes with pre-trained & pre-integrated flexible information architecture to build and deploy AI models and applications.

Watson offers you developers resources and tools like SDKs with detailed documentation and offers easy integration of conversation, language, and search into your applications. It makes complex NLP technologies accessible to easy integration into your applications to reduce workload, increase employee productivity and simplify business operations.

IBM Watson offers a tailored solution for different industries like healthcare, oil & gas, the Internet of Things (IoT), financial services, advertising, media, and more. 

Pros

Facilitates easy deployments on web services.

offers Built-in libraries and frameworks for different programming languages

Enables data manipulation, using Jupyter notebooks, via R or Python scripts

Cons

Interfacing with non-IBM technologies is often cumbersome and sometimes restrictive.

Long learning curve—it takes a lot of time to learn its basics.

Rainbird

Rainbird offers a decision-making system that incorporates all the right aspects. Due to its AI-driven decision-making capabilities paired with visual models, Rain Bird makes human-like decisions by analyzing data in a flash.

Rainbird provides a virtual user interface, RBLang – An intuitive language, smart data import, controlled learning algorithms, natural language processing, analytics, and insight. It is suitable for financial agencies, fraud protection services, and other services that encounter critical business scenarios and those that run on decisions.

RainBird lets businesses put together every bit of information available to create a robust model that forms as the base of your decision-making system, providing businesses with complete, actionable insights into all possible outcomes.

Pros

Knowledge Maps can be integrated seamlessly with third systems.

Offers seamless integration with third-party API and other third-party systems.

Create smart automation with an easy-to-use modeling interface.

 

Cons

Lacks in the availability of helpful resources material for getting started. 

H2O

H2O is an open-source platform for Machine Learning that powers major industries in the market, like banking, healthcare, finance, telecom, retail, pharma, marketing, and several others. H2O supports the use of programming languages like Python and R to build models.

H2O follows a distributed in-memory structure, offers AutoML functionality, and supports many algorithms like deep learning, generalized linear models, gradient boosted machines, etc.

H2O is an artificial intelligence tool that is business-oriented that prepares data, calibrates different parameters, employs optimal algorithms for industry-specific problems, and helps draw insights from data.

Pros

Easy to use WebUI and familiar technologies

Bigdata support with H2O’s Sparkling Water

Readily available algorithms make it easy to use in your analytical projects

Cons

Lack of documentation needs improvement

Infosys Nia

Infosys Nia is an AI platform that aims to simplify the AI adoption journey for IT and businesses. It digitizes data management, the process of document extraction, processing & comprehension and helps develop AI models for the same.

Infosys Nia supports big data/analytics, machine learning, knowledge management, cognitive automation, RPA, optical character recognition (OCR), natural language processing (NLP) capabilities, and infrastructure management services into one unified platform to offer industry and function-specific solutions to clients, allowing them to custom experiences to suit their business needs.

Pros

Offer remarkable data ingestion and data transformations and visualizations

Ability to capture knowledge like application source code, database schema

Nia integrates with existing AI infrastructure

Cons

The user interface is complicated.

Dialogflow

Dialogflow is a human-computer interaction platform that works on natural language conversations that enable businesses to build voice and text-based conversational interfaces for their app like voice apps and chatbots.

With Google’s Infrastructure and Google’s ML capabilities, companies can connect with their users on both web and mobile apps via Google Assistant, Amazon Alexa, Facebook Messenger, and other popular platforms and devices.

The platform supports integration, cross-platform support, conversation support, multilingual support, and high performance. 

Pros

Create intuitive chatbots without having to write codes.

Offers API support in many languages such as Node.JS and Python.

Offers Integrations on major chat platforms

Cons

Limited webhooks and integrations available:

Customizing chatbot flow becomes challenging and requires technical expertise.

Wipro Holmes

Wipro Holmes is an artificial intelligence & automation solution platform that offers various cognitive computing processes that accelerate business processes through automation. Wipro Holmes AI platform is used in developing digital virtual agents, predictive analysis, knowledge virtualizations, cognitive processes, digital computing applications, robotics, and drones.

Wipro Holmes AI platform is designed to offers new solutions for unique problems of IT, business sectors, and related enterprise cases. The platform supports frameworks like Cognitive Image Processing, Cognitive Search, Deep Text Extraction, and Mimictron- Intelligent Process Automation.

The platform is equipped with inbuilt metering and governance framework to ensure transparency of usage, efficiency, and productivity while complying with necessary and relevant corporate and regulatory policies and guidelines.

Pros

Predicts future outcomes and trends

Cognitive capabilities for decision-making ease

Well documented and suitable for self-studying or fast training

Cons

Lacks in the availability of helpful resources material for getting started. 

0ML

0ML is a no-code, self-service platform that enables anyone to train and create custom machine learning models fast without any ML expertise. 0ML offers a variety of machine learning models to choose from that can be trained and built code-free. Just select a model type and add your data and parameters to start training.

0ML enables developers to implement image classification, object detection, Chatbot, text classification, structure data, face recognition solutions. This platform offers a user-friendly interface and easy to use even for non-tech-savvy people.

Xprt.ai

Xprt.ai is an advanced data annotation tool that creates evaluation and training data for machine learning models. Xprt.ai has impressive features for both labelers and project managers that effortlessly perform various annotation processes.

Also, it helps reduces workload, maximizes accuracy, and subsequently improves the rate of algorithm success. Xprt.ai lets you precisely annotate images, music, and videos while offering gamified UI with simple drag and drop elements.

Conclusion

Artificial intelligence (AI) is rapidly transforming everything today by delivering better insights, streamlined operations, personalized experiences, and reduced risk. AI platforms are vital for businesses.

They offer tons of benefits like model building, training and deployment, robust data management, automation, deeper analytics, forecasting capabilities, and many others. There’s no shortage of AI platforms today;

The above-mentioned AI platforms have very specific features and qualities that perform best and are beneficial under different circumstances and for specific departments. 

An excellent artificial intelligence platform makes things easier for designers, users, and the enterprises who use the final product.

If you are looking for an agile and cost-effective no-code development solution, try out our 0ML and Xprt.ai platform to see the true power of agile software development for your business requirements.

If you’re thinking of choosing an AI intelligence platform or planning to build an AI-based solution, or curious to unlock the power of AI within your business that involves the processing of big data, automation of the business process, Contact Sparity to explore machine learning services that you need to embrace to fit your business needs learn how to get started.