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Managing AI and ML in the Enterprise
The AI and ML deployments are well underway, but for CXOs the biggest issue will be managing these initiatives, and figuring out where the data science team fits in and what algorithms to buy versus build.
IBM is aiming to use artificial intelligence inside of its Watson Studio data science platform to automate data prep and some of the drudgery needed before rolling out enterprise AI.
The data prep and governance tools are included in AutoAI. AutoAI will be core to Watson Studio and its efforts to free up data scientists to focus on models. According to Forrester, 60% of respondents cited data quality as a big hurdle to deploying AI.
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How to win with prescriptive analytics
Analytics has evolved from the basics — visualizations, historicals and dashboards — to the more complex with recommendations and predictions of outcomes. Now it’s time to step it up and get prescriptive.
Watson Studio is combining AutoAI along with its Watson Machine Learning to automate core steps in deploying AI. AutoAI is available in Watson Studio on IBM Cloud. Key features include:
- Automation of data preparation and preprocessing.
- Model development and feature engineering.
- Hyperparameter optimization to build data science and AI models.
- A suite of model types for data science including gradient boosted trees and processes to experiment with machine learning.
- IBM Neural Networks Synthesis (NeuNeuS), which is in open beta. NeuNueS allows data scientists to optimize speed or accuracy and track model training.
For IBM, AutoAI is part of a Watson Studio build out as well as a broader data science portfolio that includes IBM Watson Assistant and Discovery and Watson Machine Learning.
Recent Watson moves include:
Data science: Data analytics: A guide for business leaders (free PDF) (TechRepublic) | 10 signs you may not be cut out for a data scientist job | How to choose the right data analytics tools: 5 steps | Enterprise AI in 2019: What you need to know
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