Cloud & MLOps ☁️


This is a dedicated section focussed on the pivotal discipline of MLOps. Drawing from the insights presented in McKinsey’s The state of AI in 2022 Report (opens in a new tab), there's an evident surge in AI adoption, with figures more than doubling since 2017. Conversely, O'Reilly's 2022 survey on AI adoption (opens in a new tab) paints a slightly different picture for enterprises - only 26% of the surveyed population have successfully deployed models in a production environment. This underlines the central challenge:

Navigating the complexities of MLOps is no simple task, with numerous enterprises struggling to harness the full potential of their AI endeavors.

The gravitation towards AI isn't just a fleeting trend. For an increasing number of businesses, it's evolving into a strategic cornerstone. However, the journey from ML model creation to its deployment in production harbors a plethora of challenges. While the toolkit for ML grows more sophisticated, transitioning a model to production is still fraught with friction.

In this dynamic data-driven era characterized by burgeoning data volumes and soaring computational needs, conventional information processing methodologies often fall short. Enter the realm of distributed processing.

One of the standout solutions in this arena is Ray (opens in a new tab), a Python first distributed computing framework devised to scale your Python applications and machine learning workloads.

Ray is more than just another tool - it's a holistic, unified compute framework that gracefully scales your AI and Python tasks. From orchestration and scheduling to fault tolerance and autoscaling, Ray seamlessly handles the multifaceted nature of distributed execution. Its unparalleled strength? Being the inaugural unified, distributed compute framework crafted specifically for amplifying ML or Python tasks. With Ray, MLOps becomes significantly more manageable, enabling you to streamline data preprocessing, training, and tuning, all within a single script.

Moreover, Ray Serve (opens in a new tab) bridges the gap between the conception and deployment of your real-time pipelines. By offering this abstraction, it paves the way for teams to simplify their technical landscape, ensuring scalability isn't compromised, fostering greater alignment, and significantly reducing operational friction.

Ray Framework

To sum it up, Ray offers a scalable and cohesive ML platform, empowering you to rapidly prototype and deploy. By converging data preprocessing, training, and tuning into one script, Ray drastically accelerates your ML journey. Meanwhile, Ray Serve introduces a user-friendly Python API to design intricate inference pipelines and effortlessly roll them out in real-time using YAML.

Connect with the Ray community

You can learn and get more involved with the Ray community of developers and researchers: