The DataRobot AI Platform is presented as a solution that accelerates and democratizes data science by automating the end-to-end journey from data to value and allows users to deploy AI applications at scale. DataRobot provides a centrally governed platform that gives users AI to drive business outcomes, that is available on the user's cloud platform-of-choice, on-premise, or as a fully-managed service. The solutions include tools providing data preparation enabling users to explore and…
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Pecan.ai
Score 7.9 out of 10
N/A
Pecan is an automated AI-based predictive analytics platform that simplifies and speeds the process of building and deploying predictive models in various customer-related and operational use-cases, such as LTV, churn, NBO, risk, and segmentation. Pecan does not require any data preparation, engineering, or prepossessing - it connects directly toraw data, and uses neural networks to automate the entire predictive process. With Pecan, organizations can obtain and deploy AI models in days, without…
DataRobot can be used for risk assessment, such as predicting the likelihood of loan default. It can handle both classification and regression tasks effectively. It relies on historical data for model training. If you have limited historical data or the data quality is poor, it may not be the best choice as it requires a sufficient amount of high-quality data for accurate model building.
Pecan is something that few know and that I feel can represent a great utility for an entire company, to focus and prioritize all its products and services in favor of the right path, avoiding making mistakes and jumping directly to the solution of future problems before they happen. Pecan will allow you to always be one step ahead and improve your trading system quickly.
DataRobot helps, with algorithms, to analyze and decipher numerous machine-learning techniques in order to provide models to assist in company-wide decision making.
Our DataRobot program puts on an "even playing field" the strength of auto-machine learning and allows us to make decisions in an extremely timely manner. The speed is consistent without being offset by errors or false-negatives.
It encompasses many desired techniques that help companies in general, to reconfigure in to artificial intelligence driven firms, with little to no inconvenience.
One of the main important characteristics is its ease of use and the intuitive nature of the platform. It is possible to carry out analyzes quickly and efficiently without requiring user experience. This positive point really gives us what we need for our work: optimization and automation.
The creation of reports and statistics allows us to fully visualize the analysis carried out, in order to develop our work and carry out the pertinent actions.
Segmentation allows us to prioritize potential customers, more focused marketing campaigns, highlight our services with what our public is really interested in.
The platform itself is very complicated. It probably can't function well without being complicated, but there is a big training curve to get over before you can effectively use it. Even I'm not sure if I'm effectively using it now.
The suggested model DataRobot deploys often not the best model for our purposes. We've had to do a lot of testing to make sure what model is the best. For regressive models, DataRobot does give you a MASE score but, for some reason, often doesn't suggest the best MASE score model.
The software will give you errors if output files are not entered correctly but will not exactly tell you how to fix them. Perhaps that is complicated, but being able to download a template with your data for an output file in the correct format would be nice.
DataRobot presents a machine-learning platform designed by data scientists from an array of backgrounds, to construct and develop precise predictive modeling in a fraction of the time previously taken. The tech invloved addresses the critical shortage of data scientists by changing the speed and economics of predictive analytics. DataRobot utilizes parallel processing to evaluate models in R, Python, Spark MLlib, H2O and other open source databases. It searches for possible permutations and algorithms, features, transformation, processes, steps and tuning to yield the best models for the dataset and predictive goal.
As I am writing this report I am participating with Datarobot Engineers in an complex environment and we have their whole support. We are in Mexico and is not common to have this commitment from companies without expensive contract services. Installing is on premise and the client does not want us to take control and they, the client, is also limited because of internal IT regulations ,,, soo we are just doing magic and everybody is committed.
I've done machine learning through python before, however having to code and test each model individually was very time consuming and required a lot of expertise. The data Robot approach, is an excellent way of getting to a well placed starting point. You can then pick up the model from there and fine tune further if you need.
I particularly believe that CrossEngage has some features that Pecan does not offer, such as A/B Testing, however, we were looking for a good predictor and analyst and the truth is that Pecan does its job very well.
