Alexis Kestler
Hey there! Artificial intelligence is transforming how we build applications today. As a technology geek and data analyst myself, I am excited to walk you through the top 10 AI platforms that are powering this revolution.
These platforms are making it possible to create complex AI-driven apps with minimal effort. Buckle up, as we are going to uncover what makes these platforms tick and how they can be a gamechanger for your organization.
Why AI Platforms Matter
Let‘s start by understanding why AI platforms have become so critical.
Building machine learning models from scratch requires specialized skills and extensive computing resources. Even for experienced data scientists, the process can be time-consuming and challenging to scale.
This is where AI platforms come into the picture. They abstract away the complexity of building and managing ML models and make AI much more accessible.
AI platforms provide end-to-end capabilities that allow you to develop models faster and maximize their business impact. According to Gartner, by 2025, over 50% of enterprises will be using some form of AI platforms, up from less than 20% in 2025.
Here are some key benefits AI platforms deliver:
Accelerated experimentation
AI platforms allow rapid iteration and testing of multiple models in parallel. This enables faster innovation.
Efficient collaboration
They provide role-based access control, workflow orchestration and visibility to facilitate collaboration between data engineers, scientists and other stakeholders.
Optimized model operations
Platforms offer model monitoring, governance and reproduction capabilities for efficient model maintenance post-deployment.
Reduced development costs
By automating several model development processes, platforms bring down the effort and infrastructure costs substantially.
Scalable deployments
It allows seamless scaling of model training and prediction workloads on demand to support business growth.
Clearly, AI platforms enhance developer productivity, minimize costs and deliver greater overall value. Now let‘s explore the leading options available for you.
Top 10 AI Platforms to Know
Based on capabilities, popularity and use cases, here are the top 10 AI platforms making an impact:
1. Amazon SageMaker
Amazon SageMaker is undoubtedly one of the most widely used AI platforms today. As of 2022, it holds almost 50% market share according to industry estimates.
As a fully managed service, SageMaker radically simplifies how ML models are built, trained and deployed on AWS infrastructure.
It removes heavy lifting by providing pre-built containers to run your code on optimized instances. You can conveniently choose from a variety of frameworks like Tensorflow, PyTorch, Apache MXNet and algorithms such as XGBoost.
SageMaker Studio offers a handy visual interface to prepare data, train models, tune hyperparameters and interpret predictions.
And SageMaker Autopilot automatically generates models using your data by automating data preprocessing, feature engineering and model selection.
Post-training, deploying models for real-time or batch predictions takes just a few clicks. SageMaker handles provisioning servers, scaling and load balancing automatically based on traffic.
These capabilities explain why SageMaker has become the go-to solution for building ML apps on AWS, especially for smaller teams.
2. Google Cloud AI Platform
Google Cloud has emerged as a formidable contender in the AI platform space in recent years. It offers Google Cloud AI Platform, a family of services catering to the end-to-end ML workflow.
For building custom models, AI Platform Training and Prediction services provide auto-scaling infrastructure with support for popular frameworks like scikit-learn, XGBoost, PyTorch and TensorFlow.
To augment custom model development, AutoML provides low-code tools tailored for common use cases like vision, NLP, structured data, etc. I have personally found AutoML Image and Video Intelligence to deliver surprising accuracy for basic computer vision needs.
Vertex AI acts as the central hub to manage, monitor and govern ML resources on Google Cloud. I like how it provides visibility into model telemetry post-deployment.
MLOps capabilities like CI/CD, model monitoring and explainability are areas where Google Cloud AI Platform is maturing rapidly. This makes it a compelling enterprise-ready solution.
3. IBM Watson Studio
For teams using IBM Cloud, Watson Studio offers an interactive environment for building and deploying AI applications.
It provides an easy-to-use visual interface with drag-and-drop components for data preparation, model building, evaluation and deployment.
Watson OpenScale integrates with Watson Studio to monitor models post-production and trigger drift-based retraining. This combination of ML development and operations makes it an attractive end-to-end platform.
According to IBM, Watson Studio helps reduce model deployment time by over 65% compared to traditional coding methods. Visual model building and operational governance accelerate time-to-value.
When working with regulated data like healthcare, I have found Watson Studio‘s enterprise-grade security and governance capabilities to be highly reliable.
4. H2O Driverless AI
If speed and simplicity are your key requirements, Driverless AI by H2O.ai is worth considering.
It aims to democratize data science by enabling teams to build and deploy models quickly without coding. Driverless AI introduced the revolutionary concept of automatic machine learning for the first time.
It automatically runs through several combinations of data preprocessing, feature engineering, model selection and hyperparameters to deliver the best model for your problem.
You can see and review the results through intuitive visual reports. The platform supports advanced algorithms like XGBoost, LightGBM, CatBoost and deep learning.
According to H2O.ai benchmarks, Driverless AI achieves superior predictive accuracy compared to hand-coded models in over 77% of cases. It also improves developer productivity by over 10X.
This makes it especially useful when dealing with tabular data and traditional ML use cases across industries like banking, insurance and retail.
5. DataRobot
DataRobot is another leading automated ML platform similar to Driverless AI. It is trusted by over 20% of Fortune 50 companies for building and scaling AI applications.
It provides an end-to-end toolkit to import data from various sources, automatically engineer features, train ML models using techniques like AutoML, tune and select the best models, and monitor them post-deployment.
The platform also offers AI blueprints – customizable templates to kickstart development for common use cases like demand forecasting, customer churn reduction and personalized recommendations.
DataRobot accelerates the model building process by 10-20X as per vendor benchmarks. Its visual interface and automation allow both data scientists and citizen data scientists to be productive.
The availability of industry-specific solutions tailored for banking, retail, healthcare etc. makes adoption simpler.
6. H2O Wave
While the previous platforms focused on model building, H2O Wave simplifies the application development process on top of ML models.
Think of it as a no-code environment for building interactive web apps powered by AI.
It provides reusable components like charts, forms, maps that can be dragged and dropped to create user interfaces. These components can then be connected to ML models built using Driverless AI or Sparkling Water as the data engine.
The apps can be embedded into dashboards, reports and custom portals for business users to consume predictions and insights.
H2O Wave has really changed my experience of deploying ML apps that need strong visualizations. It allows swiftly going from model to user-friendly application.
With H2O Wave, I can visually build complete apps without extensive front-end coding, which saves tremendous time and effort.
7. SAP Analytics Cloud
SAP Analytics Cloud combines business intelligence, planning, predictive capabilities and machine learning in a unified platform.
It caters to both business users and data scientists within an organization. The business user tooling makes AI adoption easier for domain experts.
For data scientists, it offers AutoML to train models using low-code workflows. Users can also bring customized models built using Python or R.
These models can be operationalized and served to business users through reports, dashboards and planning workflows.
The integrated AI assistant provides an interesting approach to ML with conversational, natural language guidance. It also monitors models post-deployment and adapts them based on concept drift.
For SAP customers, bringing together BI, planning and ML on SAP Analytics Cloud streamlines building data-driven, intelligent applications.
8. KNIME Analytics Platform
KNIME is a popular open-source platform for data science teams with over 250,000 users globally.
It provides a modular environment to build end-to-end data pipelines and applications using a visual, code-free interface.
The drag-and-drop workflow designer allows combining over 1500 modules covering data processing, modeling, visualization and deployment tasks.
For model building, it supports algorithms like random forest, GBM, SVM, deep learning, etc. The unified catalog of modules standardizes reuse of components across teams.
KNIME Server enables collaboration features like sharing workflows, versioning and access management. The KNIME Web Portal allows deploying data applications as web apps, APIs and reports.
The openness, flexibility and community support of KNIME make it attractive to experiment. The free open-source version allows you to get started hands-on without any investment.
9. SAS Viya
SAS Viya brings together SAS proprietary analytics with open-source tools like Python, R, Tensorflow and PyTorch.
The drag-and-drop workflow designer allows building data processing and ML pipelines visually using any programming language.
Model development capabilities are enhanced by SAS Model Manager which provides MLOps functionality like model monitoring, explainability, deployment and governance post-training.
There are also industry-specific AI solutions for major sectors like banking, healthcare and communications available out-of-the-box.
The cloud-native architecture allows leveraging elastic infrastructure on Amazon Web Services, Microsoft Azure and Google Cloud Platform.
For long-time SAS customers, SAS Viya offers a future-facing platform to tap into open-source innovation while building on existing SAS investments.
10. MATLAB
MATLAB by MathWorks has been the long-standing standard platform for engineers and scientists to analyze data and develop mathematical models.
It offers a vast catalog of prebuilt ML models and algorithms for areas like predictive maintenance, signal processing, computer vision and finance.
Developers can build classical ML systems as well as deep learning models using the MATLAB programming language and tools designed for engineering tasks.
You can scale model training on GPU clusters on premises or in the cloud to accelerate experiments. The models can be integrated in production through generated code, APIs and database connectors.
For engineering teams, MATLAB combines wide-ranging analytics, simulation and modeling capabilities in one proven platform. The tight integration between tools improves productivity.
How Do You Pick The Right Platform?
With an array of excellent platforms now available, how do you go about picking the best match? Here are key aspects to consider for your selection process:
Your team‘s skill sets
Review the size and capabilities of your data scientists and ML engineers. Platforms like Driverless AI may be suitable for citizen data scientists while MATLAB requires engineers well-versed in coding.
Data sizes and types
Assess your data infrastructure and types of use cases – tabular, text, image or video. This determines scalability and tooling required.
Programming language and framework support
Align platform capabilities with your technology stack like Python, R, Tensorflow etc. to maximize reuse.
Ease of use and learning curve
A platform with intuitive interfaces and automation speeds up initial productivity and lowers skill barriers.
MLOps capabilities
Evaluate model monitoring, explainability, governance and integration options to ensure long-term reliability in production.
Cloud, on-premise or hybrid deployment
Choose platforms compatible with your infrastructure – public cloud, private cloud or hybrid based on security and policy needs.
Budget and total cost of ownership
Factor available budgets and pricing models – subscriptions, consumption-based, open-source – for optimal value.
By taking stock of these aspects, you can determine the most fitting AI platform for your needs and constraints.
Most leading options offer free trials, so I recommend test driving a few platforms with samples of your data and use cases. This hands-on evaluation gives the best signal on suitability.
Now that you know how these platforms can accelerate building machine learning apps, let‘s look at why this technological shift matters.
Democratizing Access to AI
The emergence of these enterprise-ready AI platforms has truly democratized access to cutting-edge ML technology.
Platforms like SageMaker, Watson Studio and Driverless AI have made AI much more consumable than complex toolkits like TensorFlow and PyTorch for all but the most advanced experts.
They have opened the doors for mainstream adoption of ML by abstracting away infrastructure complexities and automating large parts of development workflows.
As per Gartner, using AI platforms improves productivity of data scientists and ML engineers by over 30%. The low-code capabilities allow citizen data scientists to be productive too.
For business leaders, AI platforms reduce the skills gap while delivering faster time-to-value from AI investments.
The availability of open-source platforms like KNIME and H2O Wave is further accelerating experimentation and learning. Students and budding professionals can build production-grade applications using these platforms even without enterprise resources.
According to surveys, over 65% of organizations using AI platforms have already developed multiple production applications. And Gartner projects worldwide AI software revenue to cross $62 billion by 2025 propelled by increasing platform adoption.
As mature offerings like SageMaker and DataRobot continue to evolve and innovative options like Vertex AI grow, enterprises worldwide have an expansive palette to harness AI meaningfully.
So my friend, that was my deep-dive into the top 10 AI platforms bringing the power of ML within reach. I hope you found the analysis insightful for your own AI journey. Hit reply and let me know your key takeaways or any other questions!
