Simplify Experimentation, Deployment and Collaboration for ML and AI Models

Machine Learning and AI are changing or would say have changed the way how businesses used to behave. However, the Data Science community is still lacking good practices for organizing their projects and effectively collaborating and experimenting quickly to reduce “time to market”.

During this session, we will learn about one such open-source tool “DVC”
which can help you in helping ML models shareable and reproducible.
It is designed to handle large files, data sets, machine learning models, metrics as well as code

 
 

Outline/Structure of the Demonstration

Everyone is aware about Machine Learning, AI and there value addition. but how many experiments we did are running into production and how the entire ecosystem around Data (Data Engineering, Data Manager, Data Scientist. Models Consumers) is comfortable in making changes, answer is not so many and the part of problem statement is entire lifecycle is not defined well versed, there are many hands-off during the process, Business values are not defined till the final stage, Missing model and Data Versioning.

During this session, we will learn about one such open-source tool “DVC”
which can help you in helping ML models shareable and reproducible.
It is designed to handle large files, data sets, machine learning models, metrics as well as code

Learning Outcome

People will be able to learn how to leverage the software engineering principles we learned and how to apply it into the Data Science world and how we can simplify the workflow so that ML and AI models can run at large scale

Target Audience

Data Scientist, Data Engineers, Data Managers

Prerequisites for Attendees

On the concept side they should be knowing how the model development, deployment workflow works, its challenges.

On the tooling side they should be knowing Python, Git etc.

schedule Submitted 2 months ago

Public Feedback

comment Suggest improvements to the Speaker

  • Liked KRITI DONERIA
    keyboard_arrow_down

    KRITI DONERIA - Trust Building in AI systems: A critical thinking perspective

    KRITI DONERIA
    KRITI DONERIA
    ANALYST
    ADT
    schedule 2 months ago
    Sold Out!
    90 Mins
    Tutorial
    Beginner

    How do I know when to trust AI,and when not to?

    Who goes to jail if a self driving car kills someone tomorrow?

    Do you know scientists say people will believe anything,repeated enough

    Designing AI systems is also an exercise in critical thinking because an AI is only as good as its creator.This talk is for discussions like these,and more.

    With the exponential increase in computing power available, several AI algorithms that were mere papers written decades ago have become implementable. For a data scientist, it is very tempting to use the most sophisticated algorithm available. But given that its applicability has moved beyond academia and out into the business world, are numbers alone sufficient? Putting context to AI, or XAI (explainable AI) takes the black box out of AI to enhance human-computer interaction. This talk shall revolve around the interpret-ability-complexity trade-off, challenges, drivers and caveats of the XAI paradigm, and an intuitive demo of translating inner workings of an ML algorithm into human understandable formats to achieve more business buy-ins.

    Prepare to be amused and enthralled at the same time.