Integrating generative AI into your products can make them smarter and more efficient. But the challenge is finding a way to do it quickly and seamlessly. That's where AI development platforms come in. This article explores AI development platforms, highlighting key features and use cases to help you identify the right tools for your project.
One solution that can help you pinpoint the best AI development tools for your needs is Lamatic's generative AI tech stack. The framework includes several modules and can accelerate the process of integrating AI into your existing systems to enhance functionality and improve performance.
What is an AI Development Platform?
An artificial intelligence development platform is software that provides tools and resources for building, training, deploying, and managing AI applications. It serves as a one-stop shop for:
- Developing AI solutions
- Supporting everyone, from seasoned data scientists to business professionals with limited coding experience.
Types of AI Development Software
In general, the best software for developing AI tools will usually offer the following types of solutions:
AI Development Tools
These complete solutions let you build AI apps from scratch. They have many ready-made algorithms and are easy to use, with features like drag-and-drop. They’re great for developers of all levels and can be used for many AI projects.
Deep Learning
Focus on training artificial neural networks inspired by the human brain for tasks requiring human-like intelligence. Utilizing architectures like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), deep learning software is applied across various fields, including:
- Computer vision
- Natural language processing
- Speech recognition
These tools often rely on large datasets, powerful computational resources, and advanced optimization techniques to improve model performance.
Machine Learning
These tools enable computers to learn from data by identifying patterns and relationships. They use various algorithms and techniques to build predictive models that can make data-driven decisions and adapt over time, including:
- Decision trees
- Clustering
- Regression analysis
Conversational AI
These tools are all about making chatbots that talk like humans. They help developers create chatbots that can have conversations with people. They use natural language processing and AI techniques to understand and respond to user inputs. The best AI development tools will have pre-trained models, easy-to-use user interfaces (UI), and plenty of support material to get one’s bearings.
Who Needs An AI Platform?
Anyone seeking to unlock data's power for smarter business decisions quickly can benefit from an AI platform. These platforms truly shine when it comes to:
- Democratizing AI and Data Insights: Gone are the days when AI was reserved for enterprise tech giants. Today’s platforms offer user-friendly interfaces and prebuilt models, helping companies use their data, from enterprise companies to fledgling start-ups.
- Streamlining Development: No more building custom models or AI solutions from scratch. Today’s AI platforms offer:
- Prebuilt tools
- Workflows
- Resources
- Scaling with Ease: Your AI platform can adapt as your needs evolve. Many platforms offer scalable solutions that can grow alongside your business.
Understanding the Specifics of AI Platforms
AI platforms come in all shapes and sizes, each offering a unique blend of features and functionalities:
- Model Building and Training: Some AI platforms provide tools and prebuilt models for building different types of AI models, like predictive analytics to predict customer churn through machine learning, or generative AI to help you build models with natural language.
- Data Preparation and Integration: Other AI platforms specialize in helping you quickly clean, prepare, and integrate your data for seamless analysis and model construction.
- Deployment and Management: It is crucial to deploy your AI model to production and manage its performance, security, and scalability. Some platforms offer dedicated tools for your MLOps needs.
- API Access and Integrations: Many platforms offer APIs for programmatic access and integration with other enterprise tools and platforms in your tech stack. The key is to choose the platform that aligns with your specific needs and goals.
Generative vs. Predictive AI
There are two common types of AI: one predicts likely outcomes; the other generates media, content, code, and more and can even answer your questions via natural language.
- Predictive AI: Your data-driven fortune teller analyzes historical data to forecast future outcomes. Think “customers who buy socks are likely to buy shoes,” or “marketing campaign X might generate Y% more leads.”
- Generative AI: Your creative AI assistant conjures original text, code, or images based on input. You can write a product description and have AI craft variations for different audiences or feed it code snippets and let it complete the function.
Both are impressive, powerful tools. Some platforms even combine them, allowing you to use generative AI to build a machine-learning model.
Related Reading
- How to Build AI
- Gen AI vs AI
- GenAI Applications
- Generative AI Customer Experience
- Generative AI Automation
- Generative AI Risks
- How to Create an AI App
- AI Product Development
- GenAI Tools
- Enterprise Generative AI Tools
- Generative AI Development Services
24 Popular AI Development Platforms For Faster Deployment
1. Lamatic: The Managed Generative AI Tech Stack
Lamatic offers a managed Generative AI tech stack that includes:
- Managed GenAI Middleware
- Custom GenAI API (GraphQL)
- Low-Code Agent Builder
- Automated GenAI Workflow (CI/CD)
- GenOps (DevOps for GenAI)
- Edge Deployment via Cloudflare Workers
- Integrated Vector Database (Weaviate)
Lamatic empowers teams to rapidly implement GenAI solutions without accruing tech debt. Our platform automates workflows and ensures production-grade deployment on the edge, enabling fast, efficient GenAI integration for products needing swift AI capabilities.
Start building GenAI apps for free today with our managed generative AI tech stack.
2. Dataiku
Dataiku is a comprehensive AI analytics platform that empowers users to build, deploy, and manage AI solutions. It caters to both technical and non-technical users. Strengths include:
- Drag-and-drop interface
- Flexibility over the entire analytics lifecycle
- A design ideal for customization and tinkering
Dataiku has a steeper learning curve than low-code options. The platform is ideal for teams with data science expertise who need granular control over their AI models.
3. H2O.ai
H2O.ai is an open-source AI platform offering powerful tools and algorithms for building and deploying AI models. This platform is open-source, scalable, and packed with tools and algorithms for advanced users. H2O.ai does require coding knowledge and has a limited number of pre-built models compared to competitors. It is ideal for experienced programmers who enjoy building and experimenting with AI models from scratch.
4. Google Cloud AI Platform
Google Cloud AI Platform is a managed cloud platform that provides tools and services for building, deploying, and managing AI models at scale. Its strengths include:
- A comprehensive suite for building and deploying AI solutions
- Prebuilt models
- Seamless integration with other Google Cloud services
Its weaknesses include a learning curve and potential cost considerations, especially with high-volume usage. Google Cloud AI Platform is ideal for enterprises with existing Google Cloud infrastructure who need a comprehensive AI solution.
5. Amazon SageMaker
Amazon SageMaker streamlines the process of building, training, and deploying AI models in the cloud. Designed for data scientists and developers, this service empowers users to work efficiently and confidently across various development environments. Strengths include:
- Cloud-based tools
- Integration with AWS services
- A comprehensive solution for building and deploying AI models
Weaknesses include a learning curve and potential costs associated with cloud-based infrastructure. It is ideal for organizations using AWS that need a scalable AI solution with access to cloud resources.
6. Microsoft Azure Machine Learning
Azure Machine Learning is a cloud platform that offers an end-to-end solution for accelerating AI and machine learning projects. Strengths include:
- Tools and integration with Azure services
- Similar features and functionalities to Google and Amazon platforms
Its weaknesses include complexity and potentially high costs. Nevertheless, it is ideal for Azure organizations that need a familiar platform for building and deploying AI models.
7. OpenAI
OpenAI develops and provides access to advanced AI tools, primarily focusing on large language models (LLMs) for:
- Image and text generation
- Code completion
- And more
Strengths include access to powerful language models like:
- GPT-4 for text generation
- Translation
- Code completion
A Closer Look at OpenAI
Weaknesses include limited control and customization, high costs for large-scale usage, and ethical considerations surrounding potential bias and misuse. OpenAI is ideal for specific use cases requiring advanced text generation or code completion, with careful consideration of ethical implications and costs.
8. Pecan
Pecan AI is an AI-powered predictive analytics platform that empowers users with little coding experience to make data-driven decisions. Its low-code interface uses guided prompts to help users quickly navigate the platform and build predictive models. Strengths include:
- Low-code interface
- Guided prompts for quick AI insights
- Emphasis on marketing and sales use cases
- Predictive GenAI features for problem identification and generation using natural language
Limitations and Ideal Use Cases
Weaknesses include the fact that the predictive notebook may require some SQL knowledge to adjust the model training dataset, and highly complex models could require more support from the Pecan team. Pecan is ideal for business and data teams with limited technical expertise who need user-friendly AI tools for customer analysis, trend prediction, and campaign optimization.
9. Iguazio
Iguazio is best for MLOps acceleration. The Iguazio MLOps platform is a comprehensive tool designed to accelerate and scale the development, deployment, and management of machine learning and generative AI applications. It offers end-to-end machine and deep learning pipeline automation, allowing users to transform AI projects into real-world business outcomes.
The platform’s methodology for accelerating MLOps through various mechanisms is impressive. For example, its integrated feature store automates offline and online feature engineering for real-time and batch data.
Iguazio also integrates CI/CD across code, data, and models and supports mainstream ML frameworks. This enables users to automate:
- Testing and deployment of ML models
- Associated data pipelines
10. PyTorch
PyTorch is an open-source machine learning framework that provides a flexible platform for deep learning developments. One of its major strengths is its support for dynamic computation graphs, which offers adaptability when constructing and modifying neural networks in real-time.
A Versatile Deep Learning Framework
Among the myriad of features PyTorch offers, its interactive TorchScript mode is invaluable for Python-friendly debugging. The torchvision library is another asset that provides users with pre-trained models and datasets for numerous vision tasks. Regarding integrations, PyTorch dovetails well with popular Python frameworks such as:
- NumPy
- SciPy
- Cython
This makes the development process more fluid.
11. Aporia
Aporia is best for monitoring machine learning in production. Aporia offers an advanced platform to monitor machine-learning models once deployed into production environments. With its deep focus on providing visibility and understanding into how models behave in real-world scenarios, Aporia is a top choice for those prioritizing machine learning monitoring.
Monitoring and Maintaining Model Performance
Aporia’s platform allows users to track model drift, anomalies, and performance degradation over time. It offers custom alerting to notify stakeholders when the model’s behavior deviates from the expected. Aporia supports a variety of machine learning frameworks and platforms, ensuring compatibility and ease of implementation.
12. Keras
Keras is a high-level neural networks API built on TensorFlow, Theano, and Microsoft Cognitive Toolkit. This makes it uniquely versatile, accommodating various backends and catering to those looking to experiment and refine deep learning models.
Keras offers model visualization utilities, which allow users to visualize their neural network architectures graphically. Importantly, being integrated with TensorFlow means it can tap into TensorFlow’s vast ecosystem and extensions.
13. IBM Watson Assistant
IBM Watson Assistant is a robust solution that facilitates designing, developing, and deploying AI-powered chatbots and virtual assistants. Given its tailored focus on creating conversational interfaces, it is uniquely positioned for businesses seeking a leading edge in conversational AI applications.
A Powerful Conversational AI Tool
IBM Watson Assistant supports multi-turn conversations, ensuring users receive context-aware responses. With its integrated analytics dashboard, businesses can monitor and improve their bot’s performance by analyzing user interactions. As for integrations, Watson Assistant can be integrated with:
- Most messaging channels
- CRM solutions
- Voice channels
This makes it versatile for diverse business needs.
14. SAP HANA Cloud
SAP HANA Cloud is a fully managed in-memory cloud database offering from SAP. It provides data processing, storage, and integration capabilities while ensuring real-time insights. Its prowess in real-time database management makes it essential for businesses needing instant data access and decision-making.
In-Memory Powerhouse for Data-Driven Insights
SAP HANA Cloud’s core strength lies in its in-memory computing, which permits lightning-fast data access. The platform also boasts advanced analytics processing capabilities, facilitating OLTP and OLAP on a single data copy. When considering integrations, SAP HANA Cloud aligns well with SAP’s broad ecosystem, from SAP S/4HANA to various third-party applications, ensuring smooth data flow across systems.
15. DataRobot
DataRobot is a pioneering AI platform designed to democratize data science. It offers an environment where users can develop machine learning models without diving deeply into the underlying algorithms, especially suiting those who desire an automated approach to machine learning.
DataRobot is about more than just simplifying model creation. It also offers tools for:
- Model validation
- Deployment
- Monitoring
The platform boasts built-in feature engineering, ensuring better model performance.
Regarding integrations, DataRobot pairs effectively with platforms like Tableau for visualization and data storage solutions like Snowflake and AWS Redshift, facilitating smoother data workflows.
16. Databricks Lakehouse Platform
Databricks Lakehouse offers a unified approach to data engineering, machine learning, and analytics. Bridging the gap between data lakes and warehouses enables businesses to streamline their data management and analytics processes.
Databricks Lakehouse uses Delta Lake, ensuring ACID transactions even on vast datasets. With its auto-optimizing capabilities, businesses can handle large volumes of data without compromising performance. In terms of integrations, Databricks Lakehouse:
- Offers compatibility with popular BI tools and data sources
- Provides native connectors for various enterprise applications
This reinforces its versatility.
17. Infosys Nia
Infosys Nia is an AI platform for business process automation and data analysis. It offers:
- Predictive analytics
- Machine learning
- Natural language processing tools
Features include advanced automation capabilities for business processes and powerful tools for:
- Predictive analytics and data analysis
- Seamless integration with various enterprise systems
- Comprehensive machine learning tools and frameworks
- Robust support and resources
18. Salesforce Einstein AI
Salesforce AI delivers trusted, extensible AI grounded in the fabric of the Salesforce Platform. Utilize the AI in your customer data to create customizable, predictive, and generative AI experiences that safely fit all your business needs. With Einstein, you can bring conversational AI to any workflow, user, department, and industry.
19. Rainbird
Rainbird is an award-winning Artificial Intelligence platform that makes business operations smarter. It enables enterprises to build systems with human-like decision-making abilities, resulting in greater efficiency and increased quality.
Rainbird enables users to combine existing human and business knowledge with the company’s data to automate knowledge work and deliver consultative systems that can transform how the company’s staff and customers interact.
20. Wipro HOLMES
Wipro HOLMES is a rich set of cognitive computing services for developing digital virtual agents, predictive systems, cognitive process automation, visual computing applications, knowledge virtualization, robotics, and drones.
It is developed using:
- Machine learning
- Natural language processing
- Genetic and deep learning algorithms
- Semantic ontologies
- Pattern recognition
- Knowledge modeling technologies
This helps provide solutions that enhance experience and productivity through cognitive enhancement, accelerate processes through automation, and achieve autonomous abilities at the highest stage of maturity.
21. MindMeld
MindMeld provides a Deep-Domain Conversational AI to Power the Next Generation of Voice and Chat Assistants. This has been one of the most useful personal or professional tools for users.
Users will now be able to avoid common mistakes and confusion from former voice and chat assistants, which usually include:
- Hit-or-miss accuracy that quickly frustrates users
- Preset rules that break when users go slightly off-script
- Everyday slang that often goes misunderstood
- Shallow knowledge that fails to answer even basic questions.
22. BigML
BigML is an online machine learning tool that helps developers create and use prediction models using techniques like decision trees, random forests, and deep neural networks. It’s one of the easiest tools to start using.
BigML allows for building custom AI models, applying machine learning algorithms, and deploying apps locally or in the cloud. It’s a great choice for anyone looking to build, deploy, and manage AI models.
23. Viso Suite
Viso Suite is a computer vision software powered by AI. It’s a tool for businesses to create their applications easily. You can use it to monitor traffic, count inventory, and complete many other tasks. It’s also a simple option for people unfamiliar with coding, making it a good choice for beginners. Viso Suite can:
- Detect objects
- Recognize faces
- Segment images
- Provide behavior analysis through pre-trained or custom models
24. Observe.AI
Observe.AI is a smart call center tool that uses AI to instantly understand and write down what customers say during calls. It helps businesses understand customers:
- Feelings
- Spot patterns
- Improve agents’ jobs
For AI developers focused on improving customer service, Observe can speed up how quickly they learn from data.
Related Reading
- Gen AI Architecture
- Generative AI Implementation
- Gen AI Platforms
- Generative AI Challenges
- Generative AI Providers
- How to Train a Generative AI Model
- Generative AI Infrastructure
- AI Middleware
- Top AI Cloud Business Management Platform Tools
- AI Frameworks
- AI Tech Stack
Selection Criteria for Choosing an Artificial Intelligence Development Platform
Selecting the right AI software and machine learning platform is a challenge in the park. With many tools available in the market, zeroing in on the perfect fit requires:
- Precision
- Understanding
- Thorough research
I've evaluated dozens of AI technology tools, but in this instance, I was drawn to the practicality of the tools' functionality, which I'll delve into below:
- Data Pre-Processing: Ability to clean, normalize, and rebuild raw data into usable formats.
- Model Training: Should allow the user to train models using a variety of algorithms.
- Evaluation and Testing: Tools to test the accuracy and reliability of the models.
- Deployment: Capability to integrate the trained models into real-world applications.
Key Features: AI Development Platforms Need These Capabilities to Succeed
AI development platforms are not one-size-fits-all solutions. Each tool has unique features that improve different aspects of AI model development. Here are some key features to look for when selecting AI software:
- AutoML: Automated machine learning feature that allows the software to select the best algorithm automatically.
- Collaboration Tools: Features that enable multiple users to:
- Work simultaneously
- Share insights
- Track changes
AI improves team collaboration with features such as:
- Scalability: Can adapt to both small-scale and large-scale data processing and model training.
- Regular Updates: Continuous improvement and addition of new algorithms and methodologies.
- Security: Robust security measures to ensure data protection and privacy.
- Integration Capabilities: Easy to integrate with other platforms or tools, such as CRMs or analytics dashboards.
Usability: A User-Friendly Experience Matters
When searching for the best AI development platforms, consider the usability of each tool. You want something that will be easy for AI and non-AI experts. Look for the following qualities:
- Intuitive Interface: The dashboard should be clear with well-labeled sections, making navigation easier for novice and expert users.
- Drag-and-Drop Features: Tools, especially those focusing on visual-based machine learning models, should have drag-and-drop capabilities for easy modeling.
- Role-Based Access: Given the sensitive nature of data, the software should allow easy configuration of role-based access to ensure data protection.
- Learning Resources: As AI tools can be complex, it is crucial to have an extensive learning library, tutorial videos, or training programs for onboarding users.
- Responsive Support: A dedicated and knowledgeable customer support team that can promptly address queries and provide solutions.
Start Building GenAI Apps for Free Today with Our Managed Generative AI Tech Stack
Lamatic offers a managed Generative AI tech stack that includes:
- Managed GenAI Middleware
- Custom GenAI API (GraphQL)
- Low-Code Agent Builder
- Automated GenAI Workflow (CI/CD)
- GenOps (DevOps for GenAI)
- Edge Deployment via Cloudflare Workers
- Integrated Vector Database (Weaviate)
Lamatic empowers teams to rapidly implement GenAI solutions without accruing tech debt. Our platform automates workflows and ensures production-grade deployment on the edge, enabling fast, efficient GenAI integration for products needing swift AI capabilities.
Start building GenAI apps for free today with our managed generative AI tech stack.
Related Reading
- AI Application Development
- Best AI App Builder
- AI Development Cost
- SageMaker Alternatives
- Gemini Alternatives
- LangChain Alternatives
- Flowise AI
