A Review of DataRobot.
August 17, 2018
A Review of DataRobot.
Score 9 out of 10
Overall Satisfaction with DataRobot
We are using DataRobot as a department-wide software system for analytics on predictability on the Information Technology sector of our firm. Mainly we are utilizing the program for best practices in order to assist in company-wide decision making. It has addressed various manpower issues by yielding a level of automation that eases use of machine learning tasks, basically by displaying helpful predictive models.
- 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.
- The importance of realizing that most software programming in the Predictive Analysis genre is not 100% ideal cannot be overstated. It's tough to locate a program that would fulfill every need of every business type and size. Though DataRobot is about as close as it gets, it may not be for every industry.
- Though DataRobot helps to capture six sigma techniques, knowledge, and expertise of the best data scientists in the nation, it may end up causing rifts in intercompany personnel due to fear of job loss on a long term scale.
- Though fairly priced for a firm of our size and capability, it may be out of market reach for smaller companies at this point. It's important for every firm to understand exactly what they need and can afford.
- DataRobot can run multiple experiments at the same time. This helps to minimize time spent on any given experiment.
- DataRobot helps to get rid of bottlenecks by yielding various ways to enact completed models of prediction.
- I consider the Return on Investment high. APIs for real-time scoring have saved us many dollars and time.
We started off using BigML. A positive about BigML would be that it doesn't require files on your local drive. You just need an internet connection and API allows the user to do anything he or she needs (including model deployment and prediction). We ended up with DataRobot mainly, initially because BigML doesn't provide offline support like other open-source programming does. In the long run DataRobot was much more cost effective as well.
DataRobot is equipped to serve both cloud computing and on-premise work. Its comprehensive cloud module, channeled through Amazon sustains highly flexible machine learning programs. Lower than average costs are provided via cloud networking because there is no need to install hardware and the additional pricing that comes along with that. DataRobot assists in eliminating hassle by offering various methods of deployment in regard to predictive modeling. The program may not be appropriate for smaller startups of 20 or fewer employees, mainly because the exportable prediction code, and native batch scoring may not translate as well to a smaller firm.
22 - Most of the employees here are analytics strategists. The best tuning of models along with machine learning and generation of reports is tucked in to the most advanced features. Our strategists view the data preparation package, (which has a lot of power) and integrate the predictive modeling via automation. The work-flow is easy with the DataRobot API.
13 - Support for DataRobot requires a rapid response time. Critical yet effective machine learning, for us, needs a response where we are never left in the wait with an issue. Our in house personnel typically can have a response time of thirty minutes, and he or she is uniquely attune to very specific cases. We appreciate the training via DataRobot, in regard to our own response team.
- Automation on a machine-learning platform eases the most mundane tasks that an analyst may have to do in order to make decisions.
- DataRobot has a platform that is designed to be less error-prone and faster than other programming, thereby addressing the shortage of data scientists available in the market.
- Predicative modeling is utilized in presentation of new data in order to make probability-based predictions in the market. These use cases are based on the given patterns.
- Self-service machine learning is one way we gauge success. We have found that even non-analysts in our firm are more than capable of undertaking intense artificial intelligence projects. We did not expect this.
- We were pleasantly surprised by how accessible, the Customer Facing Data Scientists were/are with DataRobot. Very present for email or phone conversation.
- Scope of work and framing issues have been innovated for us by data science projects as well as great, above-standard communication.
- Increased organizational complexity and pronounced artificial intelligence in the form of predictive modeling.
- We are interested in the acceleration packages, as well, in order to better educate most of our staff.
- Our end game here is to encompass this very pervasive software and fundamentally transform our current models, in a way that positively impacts earnings.
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.