AI is increasingly driving change in enterprise. Here you will learn about the pre-AI infrastructure and pre-AI software development paradigms. Then you will learn about how consistent liquefaction of technology stack has happened. First it was infrastructure which led to the birth of cloud computing. This was the software 1.0 era. Now AI is driving the Software 2.0 era where low-code and no-code applications are becoming popular with citizen developers.

MLOps is all the engineering pieces that come together and often help to deploy, run, and train AI models. With that, you will invariably encounter (atleast) the following tightly interwoven components of MLOps: DataOps – Data Engineering, Model Ops – Machine – & Deep Learning and DevOps – Software engineering required to develop elegant apps and solutions.

You will learn about the MLOps concepts, Benefits such as Benefits such as rapid innovation through robust machine learning lifecycle management, creating reproducible workflow and models, easy deployment of high precision models in any location and finally management and monitoring of the same for sustained value creation.

• Available tools in the market today – COTS (commercial off the shelf) as well as Open Source.
• MLOPs Design Elements: You will understand about each dimension of the MLOPs domain
• Featuritis*: The curse of more features
• App Engines – What are they?
• What is an App Engine Framework – Standard vs Flexible?
• Public Cloud App Engines: Google App Engine – How it works
• AWS App Framework – How it works?
• Azure App Framework – How it works?
• On-Premise App Engine: Build Your Own
• A comparative presentation of on-premise vs BYO approach and how to benefit from best of both worlds approach.

• Model Serving: What is it and when do you need it?
• Techniques Used: Stateful vs Stateless Serving
• Batch Serving: How to do it?
• Continuous Model Evaluation: How is it done? More?
• Software Tools and Architectures: Apache Spark, Kafka, Beam, DLHub & more.
• Summary: Familiarize with some use-cases from the industry that focus on lessons learned and the strategies used to overcome them.

• Model Inferencing: What is it and when do you need it?
• Online Inferencing: Designing for Cloud Based environments, Implementing Online Inferencing, Optimization – Do we have a fix for featuritis*?,
• Releasing Value inside your Organization: CI/CD(Deployment, Rollout), CM (Continuous monitoring)
• Batch Inferencing: Implementation, Pros and Cons of Batch Inferencing, Software Tools for Batch inferencing
• Lessons Learnt – Some Do’s and Don’ts

• Model Registries – What is it?
• Implementing your own Model Registry
• Model Registry API and its benefits: East of use, repeatability and ease of testing.
• Designing & Implementing your own
• Model Registry API
• Reliable CI/CD: Serving Reliably Trained Models in Production Continuously (CI/CD)
• ModelOps: Model Train/ Model
• Iterate / Model Retrain
• Promoting to Production
• Retrieving for Inferencing
• Software Tools for Model Registries

• Advantages of deploying microservices architecture: scalability, speed, fault isolation
• RESTful architecture for ML/AI
• Advantages for using RESTful architectures
• Use cases Explained
• FARO – Learn with us. We will use our own stack – a modified FARM approach.
• Other approaches and tools

• Model Testing – What is it?
• Performance Testing (before Rollout)
• Dataset Representation – A Moving Target
• Combating Data Leakage
• Fighting Hidden Feedback loops
• Performance Issues in SubPopulations
• Example Uses cases from Industry Verticals
• Software Tools for Performance Testing