In the last 10 years, the way tech companies use distributed computing, statistical / machine learning software, and real-time infrastructure has changed in unfathomable ways.
How do you keep up, when data science moves ever faster?
It’s more than just having systems themselves now - teams must almost reverse engineer the tools and ideas and possibilities to build, ship, and deploy data products proactively and - more importantly - competitively.
In a tech world where you almost can’t pivot fast enough, we rely more than ever on analytics to help us predict where to go next.
What we need is good architecture that predicts the future before we get started.
We’ll show you how you can adopt - and adapt to - the latest data and machine learning infrastructure, examples of data engineering infrastructure and pipelines, analytics platforms, backend software development lifecycle, and documentation that enables you to democratize analytics and get your products out the door with fewer issues:
Incorporating data science into the SDLC.
Documenting your work: capturing the ultimate data to create a 360-degree view of each stage.
Choosing a tech stack that maximizes ability to build, ship and deploy infrastructure and products faster.
11 дек 2021
