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

5 Minutes - Why experimentation, Deployment, and reproducibility are need of hour and define 'DVC'

5 Minutes - Setup, Features and WorkFlow of DVC tool

10 Minutes - What all are the Use Cases followed by Demo

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 1 year ago

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    • Akshay Bahadur
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      Akshay Bahadur - Indian Sign Language Recognition (ISLAR)

      Akshay Bahadur
      Akshay Bahadur
      SDE-I
      Symantec Softwares
      schedule 11 months ago
      Sold Out!
      20 Mins
      Demonstration
      Beginner

      Sample this – two cities in India; Mumbai and Pune, though only 80kms apart have a distinctly varied spoken dialect. Even stranger is the fact that their sign languages are also distinct, having some very varied signs for the same objects/expressions/phrases. While regional diversification in spoken languages and scripts are well known and widely documented, apparently, this has percolated in sign language as well, essentially resulting in multiple sign languages across the country. To help overcome these inconsistencies and to standardize sign language in India, I am collaborating with the Centre for Research and Development of Deaf & Mute (an NGO in Pune) and Google. Adopting a two-pronged approach: a) I have developed an Indian Sign Language Recognition System (ISLAR) which utilizes Artificial Intelligence to accurately identify signs and translate them into text/vocals in real-time, and b) have proposed standardization of sign languages across India to the Government of India and the Indian Sign Language Research and Training Centre.

      As previously mentioned, the initiative aims to develop a lightweight machine-learning model, for 14 million speech/hearing impaired Indians, that is suitable for Indian conditions along with the flexibility to incorporate multiple signs for the same gesture. More importantly, unlike other implementations, which utilize additional external hardware, this approach, which utilizes a common surgical glove and a ubiquitous camera smartphone, has the potential of hardware-related savings at an all-India level. ISLAR received great attention from the open-source community with Google inviting me to their India and global headquarters in Bangalore and California, respectively, to interact with and share my work with the TensorFlow team.

    • Kriti Doneria
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      Kriti Doneria - Trust Building in AI systems: A critical thinking perspective

      Kriti Doneria
      Kriti Doneria
      Data Science
      Practitioner
      schedule 1 year ago
      Sold Out!
      20 Mins
      Talk
      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.

    • Kuldeep Singh
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      Kuldeep Singh - Leverage Docker, Kubernetes and Kubeflow for DS, ML and AI Workflow and Workload

      20 Mins
      Demonstration
      Intermediate

      DS, ML, and AI have moved very far from just running the models only at your local machine. Nowadays models are running in production and helping the business at decision making, which in turn increased the expectations for continuously running the models and making the changes online, but remember running this at a large scale is no easy task.
      During this session, we will learn about one such approach with Docker, Kubernetes and Kubeflow which can help us not only in developing but also deploying models at scale, allow us to use distributed setup and Hyperparameter tuning

    help