Azure Data Science Virtual Machines (DSVM) vs. IBM ILOG CPLEX Optimization Studio

To leverage a high processing workload that can be done fast instead of in multiple days or hours.

Incentivized

In my opinon, if the problem is less than 5000 variables, one should try to solve with free available solver rather than directly going for a commercial license of IBM CPLEX Optimization Studio. In my opinion, if the priority is not in terms of solving time with higher number of variables, even then one can go for free solvers like CBC, IPOPT, SCIP. In my opinion, if priority is solving time and number of variables is also high, only in that case one should prefer going for a commercial license.

• Leveraging data.
• Computer vision.
• Data science.

Incentivized

• Linear Programming
• Mixed-Integer Linear Programming
• Non-Linear Convex-Optimization
• Visualization
• Shadow Price Analysis
• Parameter Tuning

• Azure DSVM pricing must be reduced so that an AI-based start-up can use the Azure DSVM.
• Azure must create an environment to use Azure DSVM offline as well.
• Lack of frameworks

• Data handling from different sources like Note Pad, etc.
• Large size of MILP problems.
• Various parameters to set.

It's nice to use and with good optimization.

Honestly, to say, I never contacted CPLEX but used its forum to know/clarify any issues I faced.

Azure DSVM provides [many] cost-effective solutions rather than using the Amazon SageMaker. Amazon products are a little more detailed products but this detailing is [a] little costly in comparison to the Azure. Azure DSVM is way more controlled than the Amazon SageMaker and it is very cost-effective as compared to Amazon SageMaker. We are already managing Aure services so we explored the Azure DSVM which turned out [to] be a good choice.

Compared with MATLAB, CPLEX is a more user-friendly and simpler structure for writing models. This one also has a good return on investment.

• Azure DSVM is little costly with long term support for ML based environments.
• Azure DSVM is very good for short tasking and costs us [a] little low than the on-prem server.
• [Scaling] option is very convenient.

• Better for price.
• Many model parameters/features.
• No visualization.