Optimizing for Analytics, Machine Learning Experimentation

As artificial intelligence (AI) and machine learning (ML) are becoming part of our daily lives, we hear about the importance of data and data science almost daily, as we have for over a decade now. Data is often called “fuel”— for economic growth, innovation and even AI itself. And while data may be the fuel, it still takes science and engineering to build the rocket ship.

One of the keys to that science and engineering is optimization. Specifically, optimizing processes and methods for analytics and ML: experimentation and modeling. So, while data powers your rocket ship, optimizing analytics ensures its design and performance are state-of-the-art. And while working with machine learning isn’t rocket science (anymore), it still requires a bit of paradigm shift – one that realizes the importance of data and a data-driven enterprise.

Plumber, Engineer, or Scientist?

In both data science and plumbing, functional pipelines are essential to optimal performance and results. Both disciplines also often require a person to roll up their sleeves. For many data scientists, most of their time is still spent accessing, integrating and wrangling data to clean it and transform it for their day-to-day needs. What if there was a way to automate this “plumbing?” Data engineers should be up for that task!

By leveraging modern data infrastructure to speed up the exploration and data preparation phases, we can enable quick access to data across multiple systems as needed to explore and experiment. Data engineers can do their work focusing on the infrastructure and data pipelines and keep all consumers – including the data scientists – happy and productive.

Doing it All Over Again

Furthermore, in science, to get to the right answer, a team needs to run various explorations and experiments, including multiple iterations. These experiments exist to find relevant “features” –essentially the relevant data fields or their transformed values directly impacting the quality of the ML model – and to select the right methods, fine-tuning all parameters and hyperparameters required. Speed of experimentation is also critical. (No wonder hardware accelerators are so popular.)

To be even more effective, teams should track their experiments to make the results and models reproducible. Parameters and result metrics are key, to be captured and made available for comparison to understand how the team got to a specific model or conclusion.

Finally, these experiments and the follow-up model training might require managing various artifacts – sometimes very large data sets. To work seamlessly between data and code, these teams could utilize data versioning to capture a point-in-time version of those large data sets and make them easily available. 

Pushing the Limits

As more and more successful AI/ML use cases (or rocket ships) are built, we will keep pushing the frontiers and applicability of machine learning use cases. Whether to target huge or low-latency models, deploying in true edge locations, using large language models or Generative AI, the real starting point of innovation is to realize the importance of data and work toward a data-driven enterprise. This will help analysts and data scientists focus on what they are good at: effectively iterating to resolve complex data science problems and helping drive those to production. When done correctly, this type of optimization creates real business value and can take your productivity and use cases (rocket ship) to new heights.

Let Dell Data Management Lead the Way

Learn more about the data management journey with our interactive infographic here. And stay on the lookout for our blog on step four of the data management journey, coming next month. You can also learn more about Dell Data Management solutions on our Enterprise Data Management page.

Source: dell.com