Over a million developers have joined DZone.
{{announcement.body}}
{{announcement.title}}

Decision Optimization and Machine Learning: Complementary Techniques for an AI-Driven Future

DZone's Guide to

Decision Optimization and Machine Learning: Complementary Techniques for an AI-Driven Future

While data science technologies like predictive analytics continue to drive innovation across enterprises, ML techniques are instrumental to scaling data science for businesses.

· AI Zone ·
Free Resource

Insight for I&O leaders on deploying AIOps platforms to enhance performance monitoring today. Read the Guide.

As companies wake up to the potential of AI applications, data science and machine learning techniques are increasingly seen as fundamental to drive this growth. While data science technologies like predictive analytics continue to drive innovation across enterprises, machine learning techniques are instrumental to scaling data science for businesses.

From the field of medical research to self-driving cars and from personal assistants to product recommendation engines, we see the impact of machine learning all around us. The role of decision optimization technology in fueling the success of machine learning deserves special mention. Decision optimization technology uses advanced mathematical and artificial intelligence techniques to solve decision-making problems that involve millions of decision variables, business constraints, and trade-offs.

Decision Optimization + ML = Innovative Solutions

The interplay between decision optimization and machine learning is best appreciated when one understands how each technique complements the other. Machine learning models bring the ability to provide accurate forecasts (demand forecasts, equipment failure predictions, etc.) by considering real-time inputs as well as historical data. While a reliable forecast is invaluable, having the ability to make analytics-driven decisions around the best course of action to take is priceless. This can be accomplished by feeding the forecasts generated by machine learning models as inputs to a decision optimization model that can then consider the various tradeoffs and constraints to recommend the optimal solution to meet business goals.

On the other hand, once an optimization model has recommended an action plan and that plan is in operation, the data on the execution of that plan can be used by machine learning models to improve forecasts, automatically make the decision models more accurate, and hedge against risks.

We'll explore real-world applications of this interplay between machine learning and decision optimization at the IBM Think 2018 conference. You can learn the differences and complementary strengths of these two techniques, learn best practices, and see examples of combining these technologies to achieve financial gains and efficiencies by attending Session 4227 (Make Better, Faster, Smarter Decisions by Combining IBM Machine Learning and IBM Decision Optimization).

Also, don't miss the demo (IBM's Decision Optimization on DSX: Predict Maintenance Needs to Keep Production Going) that describes creating innovative solutions that combine machine learning and decision optimization.

IBM decision optimization for data science will help data scientists take advantage of these technologies in a unified environment to create unique solutions to complex business problems.

Join us at the IBM Think 2018 conference (March 19-22) in Las Vegas to learn about all these exciting announcements. Visit the Think 2018 event page to learn more and register.

TrueSight is an AIOps platform, powered by machine learning and analytics, that elevates IT operations to address multi-cloud complexity and the speed of digital transformation.

Topics:
data science ,decision optimization ,machine learning ,ai ,predictive analytics

Published at DZone with permission of

Opinions expressed by DZone contributors are their own.

{{ parent.title || parent.header.title}}

{{ parent.tldr }}

{{ parent.urlSource.name }}