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.
Related jobs
Jobs that call for the skills explored in this talk.
Matching moments
03:08 MIN
Understanding the role and challenges of MLOps
The Road to MLOps: How Verivox Transitioned to AWS
01:33 MIN
The convergence of ML and DevOps in MLOps
AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment
02:06 MIN
The rise of MLOps and AI security considerations
MLOps and AI Driven Development
04:20 MIN
Defining MLOps and its role in production ML
DevOps for Machine Learning
02:50 MIN
Understanding the core principles and lifecycle of MLOps
MLOps on Kubernetes: Exploring Argo Workflows
03:27 MIN
Understanding the new AI developer stack and MLOps workflow
Developer Experience, Platform Engineering and AI powered Apps
10:42 MIN
Understanding the machine learning workflow and MLOps
Machine Learning in ML.NET
02:36 MIN
Applying DevOps principles to machine learning operations
DevOps for AI: running LLMs in production with Kubernetes and KubeFlow
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