Dataiku - Detailed Review
Data Tools
Dataiku - Detailed Review Contents
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Dataiku - Product Overview
Dataiku Overview
Dataiku is a comprehensive platform that falls squarely within the Data Tools AI-driven product category, designed to facilitate the entire lifecycle of analytics and AI projects.Primary Function
Dataiku’s primary function is to enable organizations to build, deploy, and manage data, analytics, and AI projects. It integrates technology, people, and processes into a single platform, making it possible for various roles, from data scientists to business leaders, to work with data effectively.Target Audience
The target audience for Dataiku includes large enterprises, mid-sized businesses, and even startups across diverse industries such as finance, healthcare, retail, and manufacturing. The platform is particularly suited for organizations looking to leverage data for decision-making and innovation.Key Features
Data Preparation
Dataiku accelerates data preparation by connecting to various data sources, including cloud storage, business applications, and on-premises databases. It offers visual and coding interfaces, allowing users to cleanse, prepare, and transform data efficiently, even without extensive technical skills.Machine Learning
The platform supports machine learning through AutoML, which guides users in building and evaluating models. It also allows for full-code development, ensuring flexibility for both beginners and experienced data scientists. Dataiku supports a range of ML tasks, including prediction, clustering, forecasting, and more.Generative AI
Dataiku enables the safe deployment of generative AI applications at enterprise scale. It includes a secure large language model (LLM) gateway, no-code to full-code development tools, and AI-powered assistants. Features like LLM Guard Services help manage costs, detect toxicity, and prevent forbidden terms in model queries and responses.AI Governance
The platform enforces AI governance standards across all data work, ensuring visibility and reducing risk as the AI portfolio scales. This includes oversight over data preparation, self-service analytics, machine learning, and generative AI applications.XOps and Model Management
Dataiku manages all dimensions of AI portfolio operations through a unified platform. It automates data pipelines, deploys and manages machine learning models and generative AI applications, and provides a monitoring dashboard for tracking model performance across different environments.Data Insights and Visualization
The platform enhances business intelligence and self-service analytics with features like visualization, dashboards, and GenAI-powered storytelling. This allows everyone in the organization to make better, faster decisions based on trusted data.Conclusion
In summary, Dataiku is an all-encompassing platform that supports every aspect of data analytics and AI, from data preparation and model development to deployment and ongoing monitoring, making it a valuable tool for a wide range of organizations. Dataiku - User Interface and Experience
User-Friendly Design
Dataiku boasts a user-friendly design that makes it accessible to teams with different levels of technical expertise. This design ensures that both non-technical and technical users can work efficiently within the platform. For instance, non-technical users, such as marketers, can transition from tools like Excel to Dataiku with a manageable learning curve, as the platform offers familiar functionalities and an intuitive interface.
Visual and Coding Interfaces
Dataiku provides both visual point-and-click interfaces and coding options, allowing users to choose their preferred method of working. The visual recipes, for example, enable users to perform common data transformations like joins, splits, and windows without writing any code. This feature is particularly useful for those who prefer a no-code approach or need to quickly generate data transformations.
Dataiku Flow
The heart of Dataiku is the Dataiku Flow, a visual representation of the entire data pipeline. This flow illustrates how data moves through various stages of transformation, from raw inputs to final outputs. It provides a comprehensive overview of the workflow, making it easier to manage, understand, and troubleshoot complex data processes.
Integration and Flexibility
Dataiku is tech-agnostic, allowing users to seamlessly integrate and leverage a variety of open-source frameworks and tools. Users can work with their preferred technologies, such as Python, R, and SQL, and use popular machine learning libraries like TensorFlow, scikit-learn, and PyTorch. This flexibility ensures that data professionals can work without being constrained by the platform, and it supports the use of Jupyter notebooks natively.
Collaboration
The platform is designed to facilitate collaboration among various stakeholders, including business SMEs and data engineers. Everyone can use the same tool to create a shared understanding of the data pipeline. Features like the data catalog allow trusted datasets to be easily discovered and shared across the organization, promoting reuse and reducing the burden on IT teams.
Ease of Use
Dataiku simplifies many aspects of data science workflows, such as connecting to data sources, configuring compute resources, and managing model lifecycles. For example, data scientists can execute their code in containerized, distributed environments using Spark or Kubernetes clusters with minimal setup required. This reduces the time spent on troubleshooting resource and environment issues, allowing users to focus more on their core tasks.
Overall User Experience
The overall user experience in Dataiku is characterized by its ease of use, flexibility, and comprehensive suite of tools. It offers a holistic approach to data processing, covering data preparation, visualization, machine learning, DataOps, MLOps, and more. This makes it an attractive option for businesses seeking a single, unified platform that minimizes the need for extensive tool integration and simplifies the data processing operation.
Dataiku - Key Features and Functionality
Dataiku Overview
Dataiku is a comprehensive platform that integrates various advanced analytics features, making it a powerful tool for data professionals and organizations. Here are the main features and how they work:Data Integration and Management
Dataiku allows users to connect to and manage data from various sources, including different file types and storage resources. It provides a data catalog where trusted datasets can be published, making them easily discoverable and shareable across the organization. This feature reduces the burden on IT teams and ensures data consistency and reuse.Data Preparation
Dataiku streamlines data preparation by enabling users to connect, cleanse, and prepare data quickly. It offers features like sampling and pre-filtering of datasets, which is crucial for exploratory data analysis on manageable subsets of data. The platform also provides real-time data previews at various stages of the workflow, helping to identify potential issues early in the data preparation process.Exploratory Data Analysis (EDA)
For EDA, Dataiku offers built-in statistical charting and the ability to generate analysis with just a few clicks or entirely in code. The platform can automatically generate analysis that best fits the data. Additionally, it provides embedded metrics to monitor data quality and statistical properties, helping to identify trends, anomalies, and outliers.Machine Learning and AutoML
Dataiku is a powerhouse for predictive modeling, supporting a wide array of ML algorithms from classical techniques to advanced methods like gradient boosting and neural networks. It features AutoML to simplify model selection and hyperparameter tuning. Users can build, train, and deploy models with ease, and the platform offers extensive model evaluation metrics and visualization tools to assess model performance. Features like What-if Analysis and explainability tools help in understanding the importance of features and their impact on results.AI Governance and Compliance
Dataiku emphasizes AI governance, providing tools to enforce standards across all data work. It includes features like Model Risk Project Views to spot and mitigate risks in AI projects, Universal Feature Importance for explaining models, and Causal Machine Learning to understand the reasoning behind AI model results. These features ensure visibility and reduce risk as the AI portfolio scales.Automation and Scenarios
Dataiku’s automation capabilities are highlighted through its Scenarios feature, which allows users to automate repetitive tasks, schedule workflows, and trigger actions based on specific conditions. This ensures smooth and efficient data processes, reducing the likelihood of human error and freeing up time for more strategic tasks.Collaboration and Central Workbench
The platform fosters collaboration with its Dataiku Flow, a visual representation of the entire data pipeline. This visual map helps manage and understand complex data processes, aids in troubleshooting, and optimizes the data pipeline. It also supports real-time collaboration, allowing multiple users to work on projects simultaneously.Generative AI and Integrations
Dataiku integrates with generative AI technologies, such as OpenAI’s GPT models, allowing users to incorporate these models into data projects using a visual interface and natural language prompts. The platform also integrates with Google Cloud Platform (GCP), enabling users to leverage GCP’s resources like BigQuery, Cloud Storage, and Vertex AI. This integration enhances the ability to build, deploy, and manage AI applications securely and efficiently.Visualization and Insights
Dataiku enhances business intelligence and self-service analytics with features like visualization, dashboards, and GenAI-powered storytelling. It allows everyone in the organization to make better, faster decisions based on trusted data. Users can create analytic dashboards and data products and share them with business users to support day-to-day decision-making.XOps and Model Deployment
The platform manages all dimensions of AI portfolio operations through a unified platform. It automates data pipelines to ensure clean, reliable, and timely data and deploys and manages machine learning models and GenAI applications in production. Dataiku supports real-time scoring and integration with operational systems, enabling seamless model deployment and operation.Conclusion
In summary, Dataiku offers a flexible and scalable platform that supports the entire data science lifecycle, from data integration and preparation to predictive modeling, collaboration, and real-time scoring, all while integrating AI technologies to enhance efficiency and decision-making. Dataiku - Performance and Accuracy
Performance and Accuracy
Dataiku is highly regarded for its ability to build, deploy, and manage data, analytics, and AI projects. Here are some strengths:Model Evaluation and Monitoring
Dataiku allows users to evaluate model performance using an Evaluation Recipe, which provides detailed metrics and comparison between train and test sets. This helps in identifying overfitting and ensuring the model’s accuracy on unseen data.AutoML and Custom Models
The platform offers both automated machine learning (AutoML) and the option to build custom models using code in languages like Python, R, or Scala. This flexibility is beneficial for data scientists with varying levels of expertise.Data Preparation and Integration
Dataiku facilitates connecting to, cleansing, and preparing data at scale, with both visual and coding interfaces available. This makes data preparation more efficient and accessible.Limitations and Areas for Improvement
Despite its strengths, there are several areas where Dataiku could improve:Class Imbalance
Addressing class imbalance is crucial, and while Dataiku provides some resources on how to handle this, users often need to refer to community posts for detailed steps. Integrating more comprehensive tools or guides directly within the platform could be beneficial.Collaboration and Version Control
Integrating with version control systems like GitHub can be challenging. Users have reported difficulties in managing code effectively between Dataiku and GitHub, which hampers collaboration among developers.Data Type Support
Dataiku is strong with structured data but struggles with more complex data types such as text and image data. Enhancing support for these data types would make the platform more versatile.User Interface and Accessibility
While the interface is user-friendly for those familiar with IT and data science, it can be daunting for users without a strong technical background. Simplifying the interface and providing more training resources could make it more accessible.Performance Speed
Some users have reported that Dataiku can be slow during use, particularly when running scripts to transfer data. Improving the performance speed would enhance the overall user experience.BigQuery Integration
There are specific limitations when working with BigQuery, such as the lack of support for Python code recipes and poor support for certain BigQuery functions. These limitations add unnecessary complexity and steps to the workflow.Conclusion
Dataiku is a powerful platform for data science and AI, offering strong capabilities in model evaluation, data preparation, and integration. However, it faces challenges in areas such as class imbalance handling, collaboration tools, support for diverse data types, user interface accessibility, performance speed, and integration with specific databases like BigQuery. Addressing these limitations could further enhance the platform’s performance and accuracy. Dataiku - Pricing and Plans
Dataiku Pricing Overview
Dataiku, a leading platform for Everyday AI, offers a structured pricing structure with several plans to cater to different user needs and organizational sizes. Here’s a breakdown of their pricing and the features associated with each plan:
Free Plan
- This plan is ideal for individuals or small teams just starting with data analytics.
- It supports up to 3 users.
- Key features include visual data preparation capabilities and access to files or open-source databases.
- The Free plan is installed on your own infrastructure, giving you full control over your data.
- It is free forever, making it a great option for those with limited budgets or who are testing the waters.
Discover Plan
- The Discover plan offers more features than the Free plan and is suitable for teams that need to explore more advanced capabilities.
- It includes a broader range of features not available in the Free plan, such as additional database connectors and more extensive data preparation tools.
- This plan is part of the 14-day free trial, which allows up to 5 users to collaborate on AI projects. During the trial, you get access to more features compared to the Free plan.
Business Plan
- This plan is geared towards mid-sized teams that require more advanced features such as automation and scalable computations.
- It provides a balance of power and affordability, making it suitable for growing teams.
- The Business plan includes features like workflow automation, scalable computations, and more comprehensive data analytics tools compared to the Discover plan.
Enterprise Plan
- The Enterprise plan is the most comprehensive and is designed for larger organizations with complex data analytics needs.
- It includes all the features and capabilities of Dataiku, such as all database connectors, full deployment capabilities, an isolation framework for enhanced security, and unlimited instances with resource governance.
- This plan is perfect for organizations that need a robust and scalable data analytics platform to support their data-driven initiatives.
Additional Information
- Pricing: The median annual price for Dataiku, specifically the Paid Edition with 5 users, is around $26,000. This translates to $450 per user per month.
- Trial: Dataiku offers a 14-day free trial with a Discover Online license, which includes more features than the Free plan and allows up to 5 users to collaborate.
By choosing the appropriate plan, users can ensure they have the right set of features and capabilities to meet their specific data analytics and AI needs.
Dataiku - Integration and Compatibility
Integration with Other Tools
Dataiku is highly integrative, allowing users to work seamlessly with a wide range of tools and technologies. Here are some key integration points:BI Tools
Dataiku can integrate with popular business intelligence (BI) tools like Power BI, Tableau, and Looker. Users can export data directly from Dataiku to these tools or pull data through APIs, enabling smooth transitions between platforms.Open-Source Frameworks
The platform supports various open-source frameworks and tools, including Python, R, SQL, TensorFlow, scikit-learn, PyTorch, Apache Spark, and Hadoop. This allows data professionals to leverage their preferred technologies without being constrained by the platform.Jupyter Notebooks
Dataiku natively supports Jupyter notebooks, enabling users to code in their preferred environment directly within the platform.Data Catalog
Dataiku allows users to publish trusted datasets in a data catalog, making it easy to search, discover, and share datasets across the organization. This feature promotes data reuse and reduces the burden on IT teams.Compatibility Across Platforms
Dataiku ensures compatibility across different platforms and devices through several features:Operating Systems
Dataiku can run on various operating systems, including custom installations on Linux, as well as single-user/test instances on MacOS. Detailed installation requirements and options are available in the official documentation.Release Compatibility
Dataiku supports backward compatibility, allowing projects exported from older versions of the Data Science Studio (DSS) to be imported into newer versions. However, importing a newer project into an older instance is not supported.Tech-Agnostic Approach
The platform’s technology-agnostic approach ensures that users can integrate and leverage a variety of tools and frameworks without compatibility issues. This flexibility is crucial for maintaining compatibility with existing technology stacks.Deployment and Operations
Dataiku facilitates smooth deployment and management of machine learning models and generative AI applications across different environments:Model Deployment
Models built in Dataiku can be seamlessly deployed into production, with capabilities for real-time scoring and integration with operational systems. This ensures that models are accurately evaluated and deployed efficiently.XOps
Dataiku’s XOps (eXtended Operations) feature manages all dimensions of AI portfolio operations through a single, unified platform. This includes automating data pipelines, deploying models, and managing GenAI applications in production. Overall, Dataiku’s integration and compatibility features make it a versatile and adaptable platform that can fit into a wide range of data science and analytics workflows. Dataiku - Customer Support and Resources
Customer Support
For users encountering issues, Dataiku provides several avenues for support:
Integrated Support Window
For Dataiku Cloud users, the best way to get help is through the support window integrated into the platform. This ensures your inquiry is routed to the appropriate team quickly.
Dataiku Support Team
If you cannot resolve an issue on your own, you can reach out to Dataiku’s support team. It is recommended to be as specific as possible about the issue, include relevant screenshots, and send the appropriate log files or diagnosis. This helps the support team provide the quickest and most effective assistance.
Additional Resources
Dataiku offers a wealth of resources to help users troubleshoot and learn:
Dataiku Knowledge Base
This is a comprehensive resource that includes concepts, tutorials, how-tos, tips, and FAQs organized by themes and topics. It is a valuable tool for learning and troubleshooting various aspects of Dataiku.
Troubleshooting Documentation
Dataiku provides high-level troubleshooting steps and an overview of the logging structure. This helps users identify and resolve issues by checking logs for different types of problems, such as job or scenario failures.
Community and Forums
Users can engage with other Dataiku users, share best practices, and get support through the community forums. This is a great place to discuss issues and learn from others who may have encountered similar problems.
Guided Learning Paths
Dataiku offers guided learning activities and paths that help users upskill and gain certifications on Dataiku DSS. These resources are particularly useful for new users or those looking to deepen their knowledge.
Developer Resources
For developers, Dataiku provides detailed documentation on how to code in Dataiku, create applications, and operate Dataiku through its APIs. This includes numerous code samples and examples, as well as reference API documentation.
By leveraging these support options and resources, users can efficiently troubleshoot issues, enhance their skills, and maximize the benefits of using Dataiku’s AI-driven data tools.
Dataiku - Pros and Cons
Advantages of Dataiku
Dataiku offers several significant advantages that make it a valuable tool in the data tools and AI-driven product category:User-Friendly Interface
Dataiku is praised for its user-friendly interface, which is particularly appealing to both IT professionals and data scientists. The GUI-based features make it easy to use, even for those who are not proficient in coding.Data Preparation and Automation
The platform excels in data preparation, allowing users to connect, cleanse, and prepare data quickly. It features a visual flow for building data pipelines, and automated scenarios can handle repetitive tasks, schedule workflows, and trigger actions based on specific conditions. This automation enhances efficiency and reduces the likelihood of human error.Collaboration and Traceability
Dataiku supports strong collaboration features, including the use of Jupyter notebooks and a visual flow that represents the DataOps pipeline. This allows teams to work together seamlessly and maintain traceability of all changes made to the data and models.Machine Learning and Predictive Modeling
The platform is a powerhouse for predictive modeling, offering a comprehensive suite of tools from AutoML to advanced machine learning algorithms. It simplifies model selection and hyperparameter tuning and includes explainability features like “What if?” analysis to better understand the importance of features.Data Insights and Business Intelligence
Dataiku enhances business intelligence and self-service analytics with features like visualization, dashboards, and GenAI-powered storytelling. It enables users to make better, faster decisions based on trusted data.AI Governance and XOps
The platform enforces AI governance standards across all data work, ensuring visibility and reducing risk as the AI portfolio scales. It also manages all dimensions of AI portfolio operations through a unified platform, including automating data pipelines and deploying machine learning models.Disadvantages of Dataiku
While Dataiku has many strengths, there are also some notable challenges:Server Stability and Performance
One of the main issues is the need for improvement in server uptime and stability, particularly with query engines like Spark and Hive. Additionally, processing large data sets can be notably slow.Licensing Costs
Dataiku is criticized for its high licensing costs, which can be a significant barrier for some organizations.Integration Issues
There have been reports of issues with GitHub integration, which can hinder the workflow for teams that rely heavily on version control.Deep Learning Integration
While Dataiku offers strong machine learning capabilities, there is a need for improvement in deep learning integration to fully realize its potential in data prediction and handling.Additional Challenges
Some users have noted that while the platform is user-friendly, it may still require at least one team member with programming skills and an intuition for feature engineering and model building. Additionally, reliance on Dataiku’s consultants might be necessary in some cases. Overall, Dataiku offers a comprehensive set of features that enhance data preparation, machine learning, and collaboration, but it also comes with some challenges related to performance, cost, and integration. Dataiku - Comparison with Competitors
Competitive Landscape
Dataiku operates in a competitive market with several notable competitors, including Alteryx, Databricks, RapidMiner, Knime, and Domino Data Lab.Alteryx
Alteryx is a well-established player, offering a platform for data blending and advanced analytics. It caters to a wide range of industries and has a strong customer base. However, Dataiku differentiates itself with its more user-friendly interface and comprehensive platform that covers the entire data science workflow.Databricks
Databricks provides a unified analytics platform powered by Apache Spark, focusing on big data processing and machine learning. While Databricks is strong in handling large-scale data analytics, Dataiku stands out with its collaborative environment, scalability, and seamless integration capabilities.RapidMiner
RapidMiner offers a data science platform for building predictive models and deploying machine learning solutions. Like Dataiku, it provides advanced analytics capabilities, but Dataiku’s user-friendly interface and end-to-end workflow support give it an edge in usability and efficiency.Knime
Knime is an open-source data analytics platform known for its data preparation and visualization capabilities. While Knime is a strong competitor in these areas, Dataiku’s comprehensive platform and collaborative features make it more appealing for enterprises needing a unified solution.Domino Data Lab
Domino Data Lab focuses on enabling data science teams to collaborate and deploy models at scale. Although it offers similar features to Dataiku, Dataiku’s scalability, machine learning capabilities, and user-friendly interface set it apart.Unique Features of Dataiku
- Comprehensive Platform: Dataiku covers the entire data science workflow, from data preparation to deployment, making it a one-stop solution for data scientists and analysts.
- Collaborative Environment: Dataiku fosters teamwork by enabling different roles within an organization to work together on a centralized platform, enhancing communication and productivity.
- Scalability: The platform is highly scalable, accommodating both small and large datasets, which is crucial for businesses of all sizes.
- Integration Capabilities: Dataiku offers seamless integration with various data sources, tools, and technologies, making it easy to incorporate into existing workflows.
- User-Friendly Interface: The platform is intuitive and easy to navigate, making it accessible to users of all skill levels, from data scientists to business analysts.
Potential Alternatives
Microsoft Azure Machine Learning
Azure Machine Learning is a strong alternative, offering a visual drag-and-drop authoring environment that requires no coding. It is easier to customize and implement compared to Dataiku but may lack in support and transparency. Azure Machine Learning is particularly useful for those already integrated with the Microsoft ecosystem.Google Cloud Vertex AI
Google Cloud Vertex AI is another competitor, providing a managed service for building and running machine learning models. It is easier to customize and implement but may be harder to use and less transparent than Dataiku. Vertex AI is suitable for enterprises solving complex problems like image recognition and customer email response optimization.Tableau
Tableau, now part of Salesforce, is a leading business intelligence platform that uses AI to enhance data analysis and visualization. It offers advanced visualizations and integrates seamlessly with Salesforce data. However, it can be challenging for new users and business users without extensive data experience.IBM Cognos Analytics
IBM Cognos Analytics is an integrated self-service solution that leverages AI-powered automation and insights. It supports natural language queries and automated pattern detection but has a complex interface with a steep learning curve, making it less accessible to non-technical users. In summary, while Dataiku faces strong competition from various data analytics and machine learning platforms, its comprehensive platform, collaborative environment, scalability, and user-friendly interface make it a standout choice for enterprises seeking a unified and efficient data science solution. Dataiku - Frequently Asked Questions
Frequently Asked Questions about Dataiku
What is Dataiku and what does it offer?
Dataiku is a universal AI platform that covers the full lifecycle of analytics and AI projects. It supports everyone from data experts to domain experts, regardless of their technical skill set, by providing both visual and coder tooling. The platform includes features for data preparation, model development, solution deployment, and ongoing monitoring, all while ensuring governance and oversight.How does Dataiku support different user skill levels?
Dataiku is designed to meet users where they are, offering no-code, low-code, and full-code features. This allows both technical and non-technical users to work with data effectively. For example, visual components powered by large language models (LLMs) enable everyone to leverage the latest Generative AI technologies without needing to write code.What are the key features of Dataiku’s AutoML?
Dataiku’s AutoML supports users in building models using the latest machine learning technologies. It offers a guided visual framework for tasks such as prediction, clustering, forecasting, causal ML, and computer vision. AutoML streamlines model design and evaluation with easy-to-use templates, making it accessible for both beginners and experienced users.How does Dataiku handle Generative AI applications?
Dataiku provides several features to safely build and manage Generative AI applications. The Dataiku LLM Mesh acts as an intermediary between LLM service providers and end-user applications, reducing risk and allowing for the selection of the most cost-effective and performant models. Additionally, LLM Guard Services help manage costs, detect toxicity, and prevent forbidden terms in model queries and responses.What data preparation capabilities does Dataiku offer?
Dataiku accelerates data preparation significantly, with 40 data connectors to various data sources such as cloud storage, business applications, and on-premises databases. The platform provides 100 visual data transformers, AI-enhanced data prep, and easy automation, allowing teams to efficiently wrangle, cleanse, and prepare data with or without code.How does Dataiku facilitate collaboration among team members?
Dataiku offers a collaborative environment called Dataiku Flow, where coders and non-coders can work together on data projects. The platform includes central hubs like the catalog, plugin store, and project homepage, making it easy for teams to discover and reuse existing projects and data products. This ensures transparency and smooth collaboration among team members.What kind of automation and monitoring does Dataiku provide?
Dataiku automates repetitive workflows through scenarios that can schedule tasks like loading and processing data, refreshing charts and dashboards, and updating project documentation. The platform also includes automated monitoring that checks data for quality issues and alerts teams via email or messaging apps if any issues arise. A unified monitoring dashboard provides status and health metrics across different environments.How does Dataiku support deployment and model maintenance?
Dataiku’s Deployer manages project and model versions along with infrastructure dependencies for batch and API service deployments. The platform allows for the periodic update of production models based on newer data or shifting conditions. It also analyzes drift and evaluates model performance over time to keep models running at peak performance.What is the pricing structure for Dataiku?
The median annual price for Dataiku is around $26,000, which typically includes a plan for 5 users. The cost breaks down to about $450 per user per month. Dataiku pricing can vary depending on the plan and the number of seats included, with annual contracts that auto-renew.Are there pre-built solutions available in Dataiku?
Yes, Dataiku offers a library of pre-built projects and plug-and-play templates for common use cases such as demand forecasting, customer segmentation, process mining, and fraud detection. These solutions help extend the team’s reach and speed up the delivery of AI and analytics solutions.How does Dataiku ensure governance and oversight?
Dataiku underpins all processes with the necessary governance and oversight to leverage AI at scale. This includes features like LLM Guard Services to manage costs, detect toxicity, and prevent forbidden terms. The platform also ensures clear oversight over LLM costs and applies various usage controls to maintain compliance and risk management.