Machine Learning capable to Machine Learning driven organizations

Dominick Amalraj - Consultant

The desire to use machine learning solutions is at an all-time high in the data analytics industry. Organizations want to understand what the future holds and with this knowledge they hope to make better business decisions. Companies are heavily investing into this analytics trend by upgrading their data environment, hiring data scientists, and leveraging tools for more efficient modelling. Even with this, data science teams have a backlog of incomplete projects and are struggling to keep up with the demand of the business today. So, we came up with some steps that can help elevate your team from machine learning capable to machine learning driven.

  1. Create goals and ranked deliverables for your data science team 
  2. Begin your data preparation ASAP
  3. Leverage Auto-ML tools to streamline the modelling process
  4. Put it in front of business users and get their feedback

1) Create Goals and Deliverables: 

The data science team often works for various groups throughout an entire enterprise. This can cause the number of projects to add up, leaving it almost impossible to achieve them all. It is important that the data science team’s work is aligned to the strategy of the entire company and not just for individual teams. By deriving useful and realistic data science projects that complements the overall goals of an organization, the data science team will be able to devote their time in a more efficient manner. On top of deriving business critical use cases, it is important to rank them by both feasibility and impact to the business. Every facet of the business is more successful with an action plan and machine learning is no different. For every potential use case it is important to identify the data elements that would be significant to the model, determine if that data is accessible, and evaluate the health of the data. Without the right data a predictive model will not be able to perform well, and it is important to understand if the right data is available. Additionally, it is important to understand the impact the model would have for the organization. Recognize the business decisions that could be made from a model, how business users would derive these decisions, and who these business users are. This will give more information on whether a use case is worth the effort to produce and how to create the final showing of a machine learning model. There is no correct way to ultimately rank your machine learning projects, but by understanding the feasibility and benefits of a use case, organizations will have a better idea of the projects to take on.

2) Began Data Preparation ASAP: 

The backbone of any machine learning project is the data preparation. Anyone that has made machine learning models can attest that the data preparation can be very complex. There are usually multiple tables needed to create an optimal dataset and numerous of transformations needed. It can get even more complicated with feature engineering which is using domain expertise to create new features from the raw data. Since the data preparation is such a major and time constraining stage of the machine learning process, it is beneficial to start this right away. Some organizations will have similar datasets throughout their use cases while others have distinct unique datasets for each of their use cases. Either way if the datasets are prepared from the start, this allows data scientists to continue to build, alter, and modify datasets to make it more capable in delivering high performing machine learning models. Not only will it help deliver the best models, but it will drive machine learning projects to be delivered in a timelier manner. Although, complimenting this idea with an automated machine learning tool would deliver the best results in terms of speed and efficiency.

3) Auto-ML: 

Automated machine learning platforms are grabbing the attention of many organizations with the power to automate many of the hard points in machine learning. Auto-ML is able to take in a final dataset ready for model building and create highly accurate models. These platforms are able to take on some of the data preparation and feature engineering process, as well as iterating through different model types to find the optimal model for your use case. Examples of these platforms include DataRobot, Azure ML, Kortical, and H20.AI. If a data science team has a planned order of relevant and realistic use cases with datasets made before the model building stage, these Auto-ML tools can work on multiple use cases simultaneously with the ability to iterate through varying datasets more easily. Auto-ML tools will allow data scientists to finish data science projects quicker without sacrificing accuracy. Additionally, Auto-ML gives the ability to citizen data scientist, who may not have all of the technical capabilities of regular data scientists, to build out machine learning models and contribute to the organization’s machine learning agenda.

4) Put it in Front of your Business Users: 

Another crucial, but often overlooked aspect is putting final models into production so that business users can understand them and make better business decisions with them. Many data science teams spend very little time on this stage because of the amount of effort needed in the phases above. Regardless, of how good a model is, without publishing it in a sufficient manner business users will not be able to gain the right value from it. It is important to consult the necessary business users in how they would derive business decisions from a model’s prediction. As well as assessing their level of experience in processing business intelligence solutions. This will give your team more insight on the best way to present the final model results. Along with that, since machine learning is an iterative process, having the results available to the business users allows the subject matter experts to input suggestions on areas that the model could be improved and where there is missing data elements in the model.

These are just a few of the major tips and tricks to keep in mind while elevating your data science capabilities within your organization. These steps can help your business conquer more use-cases faster and continue growth of your completed projects.

If you have any question on how Pomerol Partners can help advance your machine learning capabilities please reach out to