MLOps - What’s the deal behind it?

Why do 90% of corporate AI projects fail to reach production? Discover the engineering discipline that bridges the gap between a model in a notebook and a real-world product.

#1about 3 minutes

The challenge of applying AI research in business

AI research focuses on benchmarks and theory, creating a significant gap between academic breakthroughs and successful industry adoption.

#2about 5 minutes

Introducing MLOps and its growing market landscape

MLOps emerged to address the high failure rate of AI projects, with its market and industry interest growing significantly since 2019.

#3about 5 minutes

What MLOps is and the engineering challenges it solves

MLOps is a set of practices for reliably deploying and maintaining ML models, addressing the complex interplay between data, code, models, and infrastructure.

#4about 3 minutes

Navigating the chaotic and overwhelming MLOps landscape

The MLOps field is currently fragmented with too many tools, conflicting best practices, and a high risk of vendor lock-in, making it difficult to navigate.

#5about 2 minutes

Using data management and open source tools for MLOps

Invest in robust data, model, and experiment management, and leverage open source tools like ONNX, DVC, and Docker to build reproducible systems.

#6about 9 minutes

Why ML engineering is the key to successful AI products

Strong software and ML engineering skills are the primary bottleneck for productionizing AI, making it a critical discipline for any company serious about ML.