10 AI Data Apps to Explore

July 9th, 2024

Guess what? Another AI tool list.. arrrgh. Not fully jumping on the bandwagon but stepping away from GenAI to focus on our sweet-spot, data. There are a bunch of tools available that make the process of implementing AI insights into your stack or processes easier and more accessible. Here are 10 AI data apps to consider, ranging from enterprise-level solutions to … um.. not-enterprise options for smaller businesses and individual users:

1. Tableau with Einstein Discovery

• Overview: Tableau is a well-known data visualisation tool that now integrates with Salesforce’s Einstein Discovery to provide AI-driven insights. This combination allows users to create interactive and shareable dashboards, enriched with predictive analytics and automated recommendations.
• Use Case: Ideal for businesses looking to visualise complex data sets and derive actionable insights through predictive analytics. But it’s Salesforce so you’ll be looking for a sales / marketing focus
• Why Try It: The integration of AI capabilities enhances Tableau’s powerful visualisation features, making it easier to spot trends and make data-driven decisions.

2. DataRobot

• Overview: DataRobot is an automated machine learning (AutoML) platform that helps businesses build and deploy accurate predictive models. Its user-friendly interface allows non-experts to create robust AI models quickly.
• Use Case: Suitable for organisations that need to deploy predictive models for various applications, from customer churn prediction to sales forecasting.
• Why Try It: DataRobot simplifies the machine learning process, making advanced analytics accessible to all team members, regardless of their technical expertise. You’ll probably need data engineers of analysts ready and waiting if you’re choosing DataRobot.

3. MonkeyLearn

• Overview: MonkeyLearn offers a suite of AI tools for text analysis, including sentiment analysis, topic classification, and keyword extraction. It’s designed to make machine learning accessible through a no-code platform.
• Use Case: Perfect for businesses that need to analyze large volumes of text data, such as customer reviews, social media posts, and support tickets. There are a bunch of use cases here and we are exploring a few which start as time-saving, but can evolve to driving insights.
• Why Try It: Its intuitive interface and pre-built models allow for quick setup and deployment, making it easy to extract insights from text data without coding.

4. RapidMiner

• Overview: RapidMiner provides an end-to-end data science platform that supports data preparation, machine learning, and model deployment. It offers both a visual workflow designer and a coding environment.
• Use Case: Ideal for data scientists and analysts who need a comprehensive tool for the entire data science lifecycle.
• Why Try It: Its combination of a visual interface and coding flexibility caters to a wide range of users, from beginners to advanced practitioners. Worth exploring if you are scaling and want to simplify / centralise your data tools

5. H2O.ai

• Overview: H2O.ai offers open-source machine learning platforms, including H2O, Driverless AI, and H2O Sparkling Water. These tools are designed for both data scientists and developers to build and deploy machine learning models.
• Use Case: Suitable for enterprises that require scalable machine learning solutions for big data analytics.
• Why Try It: H2O.ai’s platforms are highly scalable and integrate well with existing data infrastructure, making it a robust choice for large-scale data analysis. It works well with Snowflake if you’re all over that!

6. IBM Watson

• Overview: IBM Watson offers a range of AI services, including natural language processing, machine learning, and visual recognition. Watson’s AI capabilities can be integrated into various applications and workflows.
• Use Case: Great for businesses looking to incorporate advanced AI features into their existing systems, such as customer service automation and data analysis.
• Why Try It: IBM Watson’s suite of AI tools is well-regarded for its robustness and ability to handle complex data tasks, providing reliable and scalable AI solutions. I always assume IBM is too pricey & therefore haven’t really tried it, but I don’t know if this is actually true.

7. Google Cloud AutoML

• Overview: Google Cloud AutoML allows developers with limited machine learning expertise to train high-quality models specific to their business needs. It offers tools for vision, language, translation, and structured data.
• Use Case: Suitable for businesses looking to leverage Google’s AI capabilities without needing extensive machine learning knowledge.
• Why Try It: The platform’s user-friendly interface and integration with Google Cloud services make it a versatile and accessible option for small to medium-sized enterprises. Obvs. works better with google suite / GCP

8. BigML

• Overview: BigML provides an easy-to-use machine learning platform for creating and deploying predictive models. It offers features like classification, regression, clustering, anomaly detection, and association discovery.
• Use Case: Ideal for small businesses and educational institutions that want to explore machine learning without significant investment in infrastructure.
• Why Try It: BigML’s simplicity and affordability make it a great starting point for those new to machine learning.

9. KNIME

• Overview: KNIME Analytics Platform is an open-source software for data analytics, reporting, and integration. It allows users to create data workflows using a visual programming interface.
• Use Case: Suitable for data scientists and analysts who prefer a visual, no-code approach to data analytics and machine learning.
• Why Try It: KNIME’s flexibility and community support make it a powerful tool for various data analysis tasks.

10. Orange

• Overview: Orange is an open-source data visualization and analysis tool, featuring a visual programming front-end for explorative data analysis and interactive data visualization.
• Use Case: Great for educators, students, and researchers who need a free, easy-to-use platform for data analysis and visualization.
• Why Try It: Orange’s user-friendly interface and wide range of add-ons make it an excellent choice for those new to data analysis.

 

An AI data app can significantly enhance your data analysis capabilities, allowing you to derive deeper insights and make more informed decisions. At High Digital, we’re committed to helping you navigate the data and AI landscape and integrate the best tools to meet your business needs. Whether you’re looking for a visualisation application, predictive models, analyse text analysis, or all of it…  these AI applications are worth checking out.

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