1. Background introduction

”87% of projects don’t get past the experiment phase and therefore, never make it into production..”
”Companies that are holding on from deploying algorithms and models in production, stand a lot to lose.

Think about it: If an AI model is to save a company $10M a month, then not deploying and maintaining it will mean the company loses $10M a month!

Unlike static resources such as physical machines and even some databases and software, machine learning models are a constantly moving target. They require continuous monitoring and continuous deployment. On top of this, as the clients ecosystem changes in the real-world, so does the machine learning model.

MLOps provides an opportunity for companies to actively guard and combat data-shift and subsequent data-decay of their model performance by constantly feeding new data and features to it. This active intervention allows for superior model performance, meaning greater data security and accuracy, and that allows businesses to develop and deploy models at a faster rate.”

2. Brief introduction to Tarry’s next training in 28th April:
MLOPS Training – Running successful AI projects in Production

Different modules

We will start with gentle introduction and take a deep dive into full ML Life Cycle. This will involve a review of AI software and App stack, Model Serving / Deployment techniques, Model Registry, Emerging AI microservice architectures and finally performance strategies.
– What you will learn

You will learn the art of careful planning and framing your business question and how you will solve it with MLOPs.

You will learn to build an end-to-end resilient MLOPS pipeline

You will learn the art of ModelOPs (Training, Debugging, Fine-Tuning) that is  required before you publish your models in production.

You will learn to Deploy and Monitor confidently in Production: Finally, Tips and tricks to deploy and monitor and update your model(s), build safeguards for a resilient CI/CD operations.

The real benefits

You will learn to develop, run, deploy and manage your ML applications successfully in production environments.

Note: At its core, MLOps is the standardization and streamlining of machine learning lifecycle management .MLOps touches all level of IT operations and therefore this course is for domain / business subject matter experts, data scientists, ML/ data architects, data engineers, software engineers, model / risk managers, data marketers and much more.

3. Time for questions and answers

Tarry Singh is world’s leading expert and is regularly chosen as in the list of top AI experts.
Recently for 2021 nominations:
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