Data Scientist -aaS
Data Scientists are the evolution of statistics and math that were commonly referred to Statistician that fused with Computer Scientists power of coding software programs as the models became more non linear and more non parametric and things changed pretty dramatically. The last mile of this journey is understanding business outcomes and how to use data to steer an organization down the right path of success. At the moment Data Scientists are hard to come by as they are in high demand and the average tenure is about 9 months. There is a need to augment that skill set and Data Scientist -aaS can do that for you.
The secret to building really good predictive models is not being an expert in any one type of approach. The trick is to try everything, but people are cognitively bound so nobody knows a handful of techniques. DataScientist- aaS is a software program that automates the process of building and tuning predictive models so you can find the best one.
Data Scientist-aaS helps organizations monetize, optimize, automate, and provide actionable insights to their data. The AI solution inside of Data Scientist -aaS automates some task that would normally require human intelligence.
Machine Learning Models
AI Prediction Models Platform incorporates baked-in modeling techniques from top-ranked data scientists to automatically produce dozens of machine learning models with the click of a button. This enables anyone, regardless of data science expertise, to build practical machine learning models that have tangible effects on a business’s bottom line. It runs what we call “model blueprints,” meta-models that contain the machine learning algorithm in addition to a combination of data pre-processing, feature engineering, and post-processing steps. Model blueprints result in more relevant insights into your data, allowing you to make better decisions that yield tangible business value. Additionally, the platform’s built-in guardrails and baked-in best practices from the minds of some of the best data scientists ensure that your models are as accurate and practical as possible. It also has tools to increase the interpretability of machine learning models it creates, allowing users to more easily communicate insights from model outcomes to the ultimate decision-makers regarding whether or not those models are deployed into production.
What are Prediction Explanations in Machine Learning?
Traditionally, machine learning models have not included insight into why or how they arrived at an outcome. This makes it difficult to objectively explain the decisions made and actions taken based on these models. Prediction Explanations avoid the “black box” syndrome by describing which characteristics, or feature variables, have the greatest impact on a model’s outcomes.
Why are Prediction Explanations Important for Machine Learning?
When the reasons behind a model’s outcomes are as important as the outcomes themselves, Prediction Explanations can uncover the factors that most contribute to those outcomes. For example, banks using models to determine whether or not they should approve a loan can use Prediction Explanations to gain insight into why an application was accepted or rejected. With that insight they can develop models that comply with regulations, easily explain model outcomes to stakeholders, and identify high-impact factors to help focus their business strategies.
AI Prediction Explanations allow you to calculate the impact of a configurable number of features (the “reasons”) for each outcome your model generates. Once calculated, you can preview the top explanations or download the full results.
Each explanation is a feature from the dataset and its corresponding value, accompanied by a qualitative indicator of the explanation’s strength – whether it had a positive or negative influence on the final outcome.
From the example above, you could answer “Why did the model give one of the patients a 92.9% probability of being readmitted?” The explanations explain that the 8 inpatient visits, 28 medications, and the specific discharge disposition all had a strong positive effect on the (also positive) prediction.
Common Use Cases
Predicting Loan Default Rates + using Robotic Process Automation to build workflow to validate, approve or disqualify loans without need for human interaction.
Predicting Covid cases in general area.
Predicting Fraud Detection + using Robotic Process Automation to automate Security Engineering Protocols to alert IT Security.
Predicting number and types of claims that will need to be paid out per month/year/etc. Plus many more
Please fill out information below and Let’s Chat about it.