Machine Learning (ML) leverages data to improve performance of certain tasks. ML algorithms use training data to build models that make predictions or decisions without being explicitly programmed to do so.

Machine learning models are beneficial to businesses in many ways, including quickly analyzing vast amounts of data, identifying anomalies, and discovering patterns that would be challenging or time-consuming for a human to perform alone. However, like any other project, investment into a project has to be planned meticulously with clear business value established, in order to be justified. This part is difficult when it comes to Machine learning projects, as each project is unique and no two projects use the exact same workflows. The scop and time variables also vary according to the availability of data, infrastructure, the complexity of the project as well as number of resources required.

There are so many unknown and moving factors, that there is no simple way to estimate the time and cost for ML projects. The only way to move forward is to expect and accept a degree of uncertainty and let your business problem guide you towards the solution.

However, based on our experience working on various ML projects, we’ve developed a 5-step process that seems to work for most clients. With clear objectives and deliverables at each stage, it is possible to estimate the scope, time and cost of machine learning projects. In this article, we’ll share the same with you to give you an understanding of the considerations for a successful ML Project:

Lifecycle Of A Machine Learning Project

The machine learning life cycle is important as it gives a high-level perspective of how the entire project should be structured in order to obtain real, practical business value. The Datalens AI Machine Learning team defines the project estimates based on the following stages:

  1. Define Project Objectives: Without having a clear definition of the business problem, it is not possible to find the best way forward. That’s why, we conduct a comprehensive Discovery session which allows us to answer many questions that will determine our next steps. This step usually involves defining the business value, specific tasks & requirements, risks and success criteria.
    Deliverable – A Problem Statement which would define if a project is trivial or or would be complex.
  2. Acquire & Prep Data: Data is the primal currency of a Machine Learning project. The next step is to start planning the data requirements and data sets that are needed for the task. What type of data is required? Is the data available in-house or does it need to be acquired? Is the available data in a usable format or does it need to prepared? Depending on the answers to these questions, you could start developing project timelines.Deliverable: Data Pipeline in a format suitable for analysis, most likely into a flat file format such as a .csv.
  3. Model Exploration: The next step is to determine the target variable, which is the factor on which you wish to gain deeper understanding. During this phase, the model is trained using the training data and model performance is evaluated which includes model selection, hyperparameter tuning and model fitting the data.Deliverable: A proof-of-concept
  4. Model Deployment: Once the best model is determined and the proof-of-concept has been developed, the next stage is where the model is developed and the team works iteratively till they reach a production-ready answer. By now, many of the variables are set and the estimation gets quite precise by this stage.Deliverable: A production-ready ML Solution
  5. Test and Improve: Once the model is deployed, the next step is monitor and evolve. This is a continuous phase. Machine Learning projects require time for achieving satisfying outcomes and constant monitoring is required to ensure that there is no degradation of data.

Conclusion

Although the above steps are broadly followed in every project, there are factors that affect the overall cost of ML Projects. Data costs, Research costs, Infrastructure costs and Maintenance costs vary from project to project. Even though the goals might be well-defined, there is no guarantee of whether a model would achieve the desired outcome or not. It is not usually possible to lower the scope and then run the project in a time-boxed setting through a predefined delivery date. However, when you work with a Machine Learning experts like Datalens, they generally know how to foresee and mitigate delays and risks.

Kickstart Your First Machine Learning Project

If you’re considering a machine learning project, why not start today. At Datalens AI, we’ve helped many companies explore machine learning and AI for the first time. We work tirelessly not only to deliver accurate estimates — but to ensure a stellar project experience, backed by:

1. A Professional Development Approach
2. A Dedicated Project Manager + Testers
3. Clean Code That’s Easy To Maintain
4. A Quality Guarantee

 

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