The flood of data now available to enterprises is fueling a revolution in data science.
What was once relegated to spreadsheets can now involve advanced technologies like artificial intelligence (AI), machine learning (ML), and deep learning—powerful tools for unlocking insights, surfacing efficiencies, and informing the creation of new products.
As companies look to build out their data science capabilities, however, many of them encounter the same question: How do you actually get the most bang for your data science buck?
As you know, storing and analyzing vast amounts of data can be an expensive proposition. This is especially true when you’re leveraging tools like AI and ML. And when you take into account the reality that the majority of advanced analytics models rarely make it to production, the idea of investing in data science can become even less appetizing.
The good news, though, is that there are steps your business can take to successfully increase your data science capabilities without blowing holes in your budget.
Step 1. Focus on your data
Any data science project is only as good as the data it has access to, which is why your first step toward increasing your data science capabilities should be to focus on the data you have.
In general, there are three areas you should focus on:
- Assessing your data to understand what it is and where it’s coming from. This means combing through all your current analytics workloads and data sources, examining the completeness and accuracy of that data, and identifying which of your teams needs access to specific data sets.
- Simplifying your data, which requires overhauling the way your data is distributed and your queries are handled. To do this, you need to spread your data in such a way that valuable data is not buried beneath a mountain of useless information.
- Warehousing data so your data scientists have a substantial playground in which to run models. The benefits of data warehouses include ease of access to critical data for your teams, an accelerated turnaround on analysis and reporting, and ensured governance and security across the board.
Step 2: Democratize your data
For years, data was under the control of IT gatekeepers.
Now, as more and more enterprises look to leverage data science and advanced analytics, there is a greater push to democratize data so it is accessible to everyone from analysts and executives to marketing departments.
In other words, when it comes to data science, the actual scientists are only one spoke in the wheel.
While assessing, simplifying, and warehousing your data are critical components to democratization, there are additional tools available to make information available throughout your organization. Tools like:
- Data virtualization software for manipulating data regardless of inconsistencies, file formats, or data location
- Data federation software that leverages metadata to aggregate information into a single virtual database
- Data lakes to centrally locate information and partition it out for access
Step 3: Break down silos
The chasm between those data science models that are being created and those that actually reach production can be attributed to a number of things. These include the relative newness of advanced analytics technologies and a lack of skills with specialized software.
The most common culprit, however, is a disconnect between data science teams and IT teams within an organization. And driving this disconnect is the fact that most models built by data scientists are done so on dedicated workstations or cloud instances that IT teams don’t actively manage.
This means that when it comes to actually moving a model from the workstation or cloud instance into production, IT teams are routinely in the dark about how to deploy that model at scale.
The aforementioned data democratization can go a long way toward bridging this gap. But if your organization is just getting started on building out your data science capabilities, our ML Accelerator program can help take you from zero to production ready with ML models quickly.
Included in the ML Accelerator program is a ready-to-use platform and infrastructure featuring:
- Kubernetes based on Rancher
- Hardware to support ML and deep learning (DL) workloads, including a base model with 4xA100 GPUs
- Workflow management with Kubeflow and ready-built containers
- Self-service Jupyter notebooks for data exploration
- Integration with Nvidia RAPIDS and Spark
- IT monitoring and alerting with Prometheus and Grafana
Beyond the platform, the program also includes engineering assistance to help you build and deploy your first model, a workshop to kickstart your data science projects, and best practices for building out models successfully.
To learn more about our ML Accelerator program, contact one of our experts.
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