Many business problems can be solved using predictive analytics. Creating a predictive model usually includes data ingestion, feature engineering, and data wrangling before going on to model training, deployment, and monitoring. Data pipelines in large organizations tend to be distributed across various machines, storage, and tools. This can be messy and hard to assemble all in one place. This is where tools like the AI & Analytics Engine and Dataiku come into play. AutoML and ML tools enable users to implement end-to-end ML pipelines on a single platform to make predictions.
Dataiku describes itself as the platform that systemizes the use of data and AI, and one where everyone can create and consume AI. Its mission is to make AI and data so intertwined with an organization’s data-to-day, that it becomes a fundamental part of the everyday business workings. Dataiku is an all-in-one product, and one of its strengths lies in being a great tool for collaborative working. That being said, with Dataiku, some technical knowledge may be required for users to successfully navigate and use the platform in its entirety. Their target audiences are:
Tech experts, such as data scientists, engineers, and architects;
Business Experts, such as analysts; and
Enterprises.
PI.EXCHANGE’s end-to-end AutoML platform, the AI & Analytics Engine, enables users to build predictive models from raw data, without writing a single line of code. The Engine simplifies and automates the manual and time-consuming processes involved in the creation and maintenance of predictive analytics pipelines, such as data ingestion, data wrangling, model training, and model monitoring.
With the Engine, what normally took hours and days, can take just minutes. The target audiences are similar to Dataiku’s. Alongside the above audiences, the Engine also provides a viable ML solution for:
Entrepreneurs,
Solopreneurs,
Students;
And researchers.
The differentiating factor is that users with minimal to no data science or coding experience can be successfully guided to build and begin using machine learning with the Engine.
For other comparison articles, check out:
Dataiku offers 4 tiers of subscription plans, tiered according to the number of users, as well as features. All plans come with a 14-day free trial for new users. Dataiku also offers a free edition of their platform. Naturally, the free edition comes with limited feature access. However, it is free forever, making it cost-friendly for users who only need it for basic tasks.
When it comes to pricing, however, there is no mention of the subscription costs found on the website. We did come across the Discover plan’s pricing on the Microsoft Azure marketplace. The Discover plan, catered to small teams, costs $80,000/year, amounting to approximately $6,667/month. With limited features and a maximum of 5 users, that is one hefty price tag. This essentially rules out entrepreneurs, solopreneurs, and smaller companies from being able to adopt Dataiku for their ML needs.
The AI & Analytics Engine’s pricing for individual and group users is fixed, just varying in data usage and storage quotas, as well as the maximum number of users. Larger enterprises and corporations will have custom pricing depending on the data storage limit or usage quotas configured based on their needs.
Individual subscription - $129USD /month
Team subscription (up to 4 users) - $499USD /month
Business subscription (up to 10 users) - $1,999USD/month
The AI & Analytics Engine was built to enable everyone access to AI/ML. That means affordable prices with a wide range of readily-available features.
When comparing pricing, the AI & Analytics Engine is significantly more affordable - enabling organizations without a data science team to still reap the benefits of AI/ML.
When it comes to technical documentation, Dataiku does a stellar job of being thorough and informative. Their technical documentation is organized and all-encompassing, taking users through the various aspects of the platform. They also have a knowledge base that allows users to be self-sufficient in their usage of the platform. Included in the knowledge base, are getting started articles, how-to articles, and articles to help them successfully navigate the platform.
Dataiku houses their community on their website. The community is akin to discussion boards, categorized according to topics (for example, product ideas, getting started, general discussion). Here, users can pose questions to get help from each other within the community.
Another great feature as part of their educational resource is the Academy. Users can take up courses and tutorials to increase their proficiency in the platform, and use it to its full potential.
The AI & Analytics Engine's Knowledge Hub comes with a wealth of articles. These articles guide users through the Engine’s workflow, with how-to guides, concept articles, and video tutorials. The concept articles are for users with minimal to no ML or data science expertise. They allow such users to become more familiar with the ML terms used within the Engine, and to understand why the Engine automates and recommends actions for them.
Next up, PI.EXCHANGE has a slack community for users (and non-users) of the Engine. Members of the Slack community channel come together to discuss ideas, suggest potential features, and get direct help from the team. When it comes to customer success support, all trial users are given the option to set up a complimentary personalized onboarding session with an expert data scientist to walk them through the platform and help them reach their goals with the Engine.
Come join our Slack community to find out more about the Engine!
The AI & Analytics Engine offers fast, streamlined, and repeatable data wrangling as part of the machine learning pipeline. The Engine’s smart data preparation feature lets users easily carry out data wrangling tasks with its intuitive user interface. Users can upload their raw data and use this feature to prepare their data. This allows users to complete the end-to-end machine learning workflow entirely within the Engine, without requiring the use of another tool for data wrangling. Furthermore, data wrangling recipes can be saved and reused in other datasets, speeding up and automating future processes.
Dataiku has a similar data-wrangling feature within its platform. Users can wrangle their data without writing a line of code on the platform. That said, Dataiku also offers users the ability to wrangle their data using code. The flexibility offered by Dataiku in its data-wrangling feature proves useful to both users unfamiliar with data science and coding, as well as highly-technical users like Data Scientists. Similar to the Engine, the data preparation steps can be saved as a reproducible recipe.
One of the AI & Analytics Engine’s highly useful features is the Model Recommender. The AI-driven Model Recommender helps users select the optimal ML algorithm given their data and what they are trying to do. It does so by predicting an ML algorithm’s (model) performance on the dataset before you select and train the model.
The Engine ranks the selected algorithms (models) according to their estimated performance, across predictive performance, prediction time, and training time. This powerful feature helps users make an informed decision and saves a significant amount of time, so you need only train the models that will perform optimally given your criteria of optimal (speed or performance or both).
Currently, Dataiku does not offer a Model Recommender feature on their platform.
Dataiku has an interesting and highly useful feature - Dashboards. Users can easily create dashboards with charts. Dashboards allow users to share elements of your project with other teammates, even those who do not have full project access. Their data visualization feature is highly sophisticated and comes with a multitude of visualization options.
The AI & Analytics Engine offers data visualizations for datasets. Users can view the data analysis through various visualization types (like bar charts, graphs, etc.).
Both the AI & Analytics Engine and Dataiku are tools intended to speed up and streamline the machine learning process.
Dataiku comes with many advanced features, including an advanced data visualization feature. This would appeal to larger corporations with teams full of technical staff that would fully utilize this added utility. However, Dataiku comes at a significant cost. As such, it may not be an affordable option for individual users or SMBs.
The AI & Analytics Engine is a cost-friendly alternative. The competitive pricing point makes the Engine a great tool for companies large and small, to empower more users to build value with ML in a highly managed and scalable way.
So, if you are building out an ML capability for the first time, lack the skill set internally, are struggling with the well-publicized data science skills shortage, or simply have a team that would benefit from a simplified and streamlined end-to-end ML workflow, the Engine might be the one for you.
Not sure where to start with machine learning? Reach out to us with your business problem, and we’ll get in touch with how the Engine can help you specifically.
How does the H2O Driverless AI add up when compared to the AI & Analytics Engine? Let’s find out in this comparison blog looking at both AutoML...
In this comparison article, we look at two powerful Machine Learning tools, Obviously.AI and PI.EXCHANGE'S AI & Analytics Engine.
What are no-code AutoML solutions? We dive deep into machine learning platforms and take you through how they can help you pursue your ML-driven...