Best practices for Databricks PoC (Proof of Concept)

The AI race is on, and organizations all over the world are rushing to implement AI-driven technologies into their workflows, but most aren’t getting the most out of it. [1] Databricks stands as one of the most efficient platforms for deploying data analytics and machine learning projects.

Unfortunately, even with a unified platform for developing, deploying, and maintaining models, organizations still have to deal with the cost implications of taking these models back to the drawing board when they don’t work as intended.

Therefore, in a bid to curb these challenges, organizations are now using Databricks (Proof of Concept) PoC to validate their model’s capabilities and potential benefits to the organization.

This article will delve into Databricks PoC, giving you all the information you need to set effective objectives and design a Databricks PoC.

Understanding the benefits of Databricks PoC

Databricks proof of concept is a project undertaken to verify whether specific theories or concepts in a data science, deep learning, or machine learning project can be applied in real-life business processes and whether they justify the development, implementation, and maintenance costs.

Therefore, Databricks PoC is essentially meant to determine the viability of a data science or machine learning project in terms of cost and effectiveness.

Some of the most notable benefits of Databricks proof of concept include:

Minimizing business risk

Implementing a proof-of-concept project enables organizations to verify the core elements of their data science or ML project and ensure that the project is headed in the right direction without committing too many resources in the initial stages of development.

This way, an organization can test an initial model with the data it has on hand, enabling it to check the model’s viability and know whether it has all the data it needs to build and deploy an effective model. If an organization finds that it doesn’t have enough data or its data is compromised, it can source more data or enrich the data it has on hand.

Getting stakeholders on board with the project

Implementing ML models is expensive, and these costs can significantly dig into an organization’s funding. Therefore, stakeholders have to approve the project before it’s allowed to take off.

In this regard, data scientists must first develop an effective PoC to convince stakeholders of its viability. If the model is found satisfactory, it can be further fine-tuned and approved to enter the full development stage.

Improving data collection practices

Data quality is vital in creating an effective machine-learning project. [2] Ensuring data quality starts at the data collection stage. Essentially, if your data isn’t representative of the model’s intended purpose, it won’t perform well.

Performing a proof of concept enables you to determine the effectiveness of your data collection and refining methods, thus streamlining future projects.

Read more about Databricks for Business: Use cases

How to set effective objectives for your Databricks PoC

The effectiveness of your Databricks proof of concept comes down to how well you define your objectives when creating it. In that regard, here are a few factors to consider when building a Databricks proof of concept. [3]

Understand your objectives

As scientists, data engineers are often plagued by the impulse to innovate first and justify later. However, the nature of ML projects doesn’t fit this notion since they are costly, time-consuming, and meant to serve a specific purpose. Therefore, it is important to define your project’s objectives before developing your proof of concept. This way, your PoC will align with the intended project’s objectives.

Define specific use cases

The goal of any Databricks Proof of Concept is to demonstrate the viability and feasibility of a specific project or solution. Therefore, when building your PoC, you need to identify core use cases of the project that align with the organization’s goals.

The use cases you consider should reflect real-world scenarios where the project could potentially make a difference.

Determine key performance indicators (KPIs)

KPIs enable you to determine whether a specific objective has been reached. By employing relevant KPIs in your PoC, you can better manage the project more effectively at every step in the process. [4]

Make objectives SMART

The SMART criteria involve making your objectives:
● Smart
● Measurable
● Achievable
● Relevant, and
● Time-bound

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Key steps in designing your Databricks PoC

There are four key steps in designing a Databricks proof of concept. They include:

Define your objectives

The first step in designing a Databricks PoC is defining your objectives and the business use cases in which they are embedded. This way, you are better able to set metrics or success criteria to help guide you through the process.

Gather relevant data

The next step is to gather enough data on the project’s specific use cases. While you’re at it, you may also have to refine the data to make your proof of concept more effective. Data also helps you to translate the project’s use cases into a detailed description of your PoC solution.

Build the PoC

Once you have all the necessary data in place, employ the objectives set in the first step to build the PoC in the Databricks environment. Here, you’ll also have to determine the best tools and statistical languages to employ in your PoC project.

Test the Proof of Concept

Once your PoC project is ready, you can test it in a day-to-day simulated business environment and collaborate with different teams within the organization to assess the project’s user-friendliness, relevance, and performance.

Essentially, the model needs to make sense for the end-users and be intuitive to use. Therefore, the testing process should be tangible and measurable to present accurate outcomes and possible impacts on management and overall business processes.

It might be also interesting for you: Mastering Databricks Deployment: A Step-by-Step Guide

The bottom line

Building a Databricks PoC is not only necessary but vital to ensuring the success of any AI-driven project, including machine learning, Data science, and other AI projects. It provides a measurable description of the project’s viability and feasibility, thus helping to mitigate potential losses and provide a reliable framework for completing the rest of the project.

References

[1] Helpnetsecurity.com. Generative AI Strategy. URL: https://www.helpnetsecurity.com/2023/07/25/generative-ai-strategy/. Accessed August 24, 2023
[2] Kdnuggets.com. The Significance of Data Quality in Making a Successful Machine Learning Model. URL: https://www.kdnuggets.com/2022/03/significance-data-quality-making-successful-machine-learning-model.html. Accessed August 24, 2023
[3] Tech.gsa.gov. Agile Investment – Proof of Concept Phase Checklist. URL: https://tech.gsa.gov/guides/agile_investment_proof-of-concept_phase_checklist/. Accessed August 24, 2023
[4] Coopervision.ca. The Importance of Key Performance Indicators. URL: https://coopervision.ca/practitioner/practice-building/key-performance-indicators-kpis. Accessed August 24, 2023