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About Decision Optimization and DSX Local

A brief introduction to Decision Optimization for Data Science Experience.

What is Decision Optimization?

Decision Optimization gives you access to IBM's world-leading solution engines for mathematical programming and constraint programming. People frequently use the term optimization to mean making something better. Although optimization often makes things better, it means a lot more than that: optimization means finding the most appropriate solution to a precisely defined situation. It is a sophisticated analytics technology which can explore a huge range of possible scenarios before suggesting the best way to respond to a present or future situation.

Decision optimization

  1. The situation is generally a business problem, such as planning, scheduling, pricing, inventory, or resource management.
  2. Whatever the problem is, resolving it starts with the optimization model, which is the mathematical formulation of the problem that can be interpreted and solved by an optimization engine. The optimization model specifies the relationships among the goals, limits, and choices that are involved in the decisions. But it is the input data that makes these relationships concrete. An optimization model for production planning, for example, can have the same form whether you are producing three products or a thousand. The optimization model plus the input data creates an instance of an optimization problem.
  3. Optimization engines (or solvers) apply mathematical algorithms to find a solution, a set of decisions that achieves the best values of the goals and respects limits imposed. The optimization engine implements specialized algorithms that have been developed and tuned to efficiently solve a large variety of different problems. Decision Optimization uses the IBM CPLEX and CP Optimizer engines that have been proved powerful in solving real-world applications.
  4. The solution that emerges from the solver details the recommended values for all of the decisions that are represented in the model. Equally important are the metric values that represent the targets. These values measure the quality of the solution in terms of the business goals.
  5. All of this is made available to business users via a business application. Usually, the solution and goals are summarized in tabular or graphical views that provide understanding and insight.

What is Data Science Experience Local?

The IBM Data Science Experience Local (DSX Local) is an on-premises enterprise solution for data scientists. It provides an interactive, collaborative platform that offers data scientists with a complete set of the tools. It draws from open source and IBM technologies and provides data science teams with access to a community of peers. This allows teams to collaborate and take advantage of shared resources—including data sets, notebooks and articles—using Jupyter Notebooks, RStudio and Apache Spark with a growing set of IBM innovations for data scientists. What’s more, DSX offers user scalability and a high level of security, making it highly adaptable to a broad range of applications.

Figure 1. The IBM Data Science Experience

Image showing different tools available in DSX Local

DSX Local provides the environment and tools to help solve your business problems by collaboratively analyzing your data. It integrates a full range of data science capabilities, including predictive and prescriptive technologies, within a unified environment. The DSX architecture is focused on the project—which is also how you organize your resources for solving a business problem. When you create a project for analyzing data, you associate it with a compute engine and storage and then add collaborators, data assets and analytic. You can also add bookmarks to important resources and associate other services with your project.