Practitioner's Perspective : How do you accelerate innovation and deliver faster time-to-value for your AI initiative

schedule Aug 8th 02:45 - 03:05 PM place Grand Ball Room 1 people 136 Interested

Machine Learning (ML) offers innovation for every business and with the advancement in the ML technology we are solving ambitious problems using Machine Learning. In this session we will learn how Amazon Sagemaker helped Zoomcar in their ML journey by providing a scalable platform for doing exploratory analysis on the vast amount of data they have and running multiple ML model before finalizing on the best fit model for solving business-critical problem of car damage analysis assessment

 
 

Learning Outcome

How Amazon Sagemaker helped Zoomcar in their ML journey by providing a scalable platform for doing exploratory analysis on the vast amount of data?

Target Audience

Data Scientists, Data Engineers, Data Specialists, Machine Learning Engineers, Data Science Enthusiasts

schedule Submitted 2 months ago

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    Keynote
    Intermediate

    Since we originally proposed the need for a first-class language, compiler and ecosystem for machine learning (ML) - a view that is increasingly shared by many, there have been plenty of interesting developments in the field. Not only have the tradeoffs in existing systems, such as TensorFlow and PyTorch, not been resolved, but they are clearer than ever now that both frameworks contain distinct "static graph" and "eager execution" interfaces. Meanwhile, the idea of ML models fundamentally being differentiable algorithms – often called differentiable programming – has caught on.

    Where current frameworks fall short, several exciting new projects have sprung up that dispense with graphs entirely, to bring differentiable programming to the mainstream. Myia, by the Theano team, differentiates and compiles a subset of Python to high-performance GPU code. Swift for TensorFlow extends Swift so that compatible functions can be compiled to TensorFlow graphs. And finally, the Flux ecosystem is extending Julia’s compiler with a number of ML-focused tools, including first-class gradients, just-in-time CUDA kernel compilation, automatic batching and support for new hardware such as TPUs.

    This talk will demonstrate how Julia is increasingly becoming a natural language for machine learning, the kind of libraries and applications the Julia community is building, the contributions from India (there are many!), and our plans going forward.

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    We will train the audience how to develop advanced image segmentation with FCN/DeepLab algorithms which can help visualize the driving scenarios accurately, so as to allow the autonomous driving system to take appropriate action considering the obstacle views.

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    Kathrin Melcher / Paolo Tamagnini - The Magic of Many-To-Many LSTMs: Codeless Product Name Generation and Neural Machine Translation

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    Case Study
    Intermediate

    What do product name generation and neural machine translation have in common?

    Both involve sequence analysis which can be implemented via recurrent neural networks (RNN) with LSTM layers.

    LSTM Neural Networks are the state of the art technique for sequence analysis. In this presentation, we find out what LSTM layers are, learn about the difference between many-to-one, many-to-many, and one-to many-structures, and train many-to-many LSTM networks for both use cases.

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    Paolo Tamagnini / Kathrin Melcher - Guided Analytics - Building Applications for Automated Machine Learning

    90 Mins
    Tutorial
    Beginner

    In recent years, a wealth of tools has appeared that automate the machine learning cycle inside a black box. We take a different stance. Automation should not result in black boxes, hiding the interesting pieces from everyone. Modern data science should allow automation and interaction to be combined flexibly into a more transparent solution.

    In some specific cases, if the analysis scenario is well defined, then full automation might make sense. However, more often than not, these scenarios are not that well defined and not that easy to control. In these cases, a certain amount of interaction with the user is highly desirable.

    By mixing and matching interaction with automation, we can use Guided Analytics to develop predictive models on the fly. More interestingly, by leveraging automated machine learning and interactive dashboard components, custom Guided Analytics Applications, tailored to your business needs, can be created in a few minutes.

    We'll build an application for automated machine learning using KNIME Software. It will have an input user interface to control the settings for data preparation, model training (e.g. using deep learning, random forest, etc.), hyperparameter optimization, and feature engineering. We'll also create an interactive dashboard to visualize the results with model interpretability techniques. At the conclusion of the workshop, the application will be deployed and run from a web browser.

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    Aditya Singh Tomar - Building Your Own Data Visualization Platform

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    45 Mins
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    Beginner

    Ever thought about having a mini interactive visualization tool that caters to your specific requirements. That is the product I created when I started independent consulting. 2 years since, and I have now decided to make it public – even the source code.

    This session will give you an overview about creating a custom, personalized version of a visualization platform built on R and Shiny. We will focus on a mix of structure and flexibility to address the varying requirements. We will look at the code itself and the various components involved while exploring the customization options available to ensure that the outcome is truly a personal product.

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    Anuj Gupta - Continuous Learning Systems: Building ML systems that keep learning from their mistakes

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    Anuj Gupta
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    45 Mins
    Talk
    Beginner

    Won't it be great to have ML models that can update their “learning” as and when they make mistake and correction is provided in real time? In this talk we look at a concrete business use case which warrants such a system. We will take a deep dive to understand the use case and how we went about building a continuously learning system for text classification. The approaches we took, the results we got.

    For most machine learning systems, “train once, just predict thereafter” paradigm works well. However, there are scenarios when this paradigm does not suffice. The model needs to be updated often enough. Two of the most common cases are:

    1. When the distribution is non-stationary i.e. the distribution of the data changes. This implies that with time the test data will have very different distribution from the training data.
    2. The model needs to learn from its mistakes.

    While (1) is often addressed by retraining the model, (2) is often addressed using batch update. Batch updation requires collecting a sizeable number of feedback points. What if you have much fewer feedback points? You need model that can learn continuously - as and when model makes a mistake and feedback is provided. To best of our knowledge there is a very limited literature on this.

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    Deepak Mukunthu - Automated Machine Learning

    45 Mins
    Talk
    Beginner

    Intelligent experiences powered by AI can seem like magic to users. Developing them, however, is pretty cumbersome involving a series of sequential and interconnected decisions along the way that is pretty time-consuming. What if there was an automated service that identifies the best machine learning pipelines for a given problem/data? Automated Machine Learning does exactly that!

    With the goal of accelerating AI for data scientists by improving their productivity and democratizing AI for other data personas who want to get into machine learning, Automated ML comes in many different flavors and experiences. Automated ML is one of the top 5 AI trends this year. This session will cover concepts of Automated ML, how it works, different variations of it and how you can use it for your scenarios.

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    Govind Chada - Using 3D Convolutional Neural Networks with Visual Insights for Classification of Lung Nodules and Early Detection of Lung Cancer

    Govind Chada
    Govind Chada
    AI/ML Researcher
    Cy Woods
    schedule 5 months ago
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    45 Mins
    Case Study
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    Lung cancer is the leading cause of cancer death among both men and women in the U.S., with more than a hundred thousand deaths every year. The five-year survival rate is only 17%; however, early detection of malignant lung nodules significantly improves the chances of survival and prognosis.

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    Madhan Rajasekkharan
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    Beginner

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    Dr. Om Deshmukh - Key Principles to Succeed in Data Science

    90 Mins
    Tutorial
    Beginner

    Building a successful career in the field of data science needs a lot more than just a thorough understanding of the various machine learning models. One has to also undergo a paradigm shift with regards to how s/he would typically approach any technical problems. In particular, patterns and insights unearthed from the data analysis have to be the guiding North Star for the next best action rather than the path of action implied by the data scientist's or his/her superior's intuition alone. One of the things that makes this shift tricker, in reality, is the 'confirmation bias': Confirmation bias is defined as a cognitive bias to interpret information in such a way that it further’s our pre-existing notions.

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    Videos account for about 75% of Internet traffic today. Enterprises are creating more and more videos and using them for various informational purposes, including marketing, training of customers, partners & employees and internal communications. However, videos are considered as the blackholes of the Internet because it is very hard to see what’s inside them. The opaque nature of videos equally impacts end users who spend a lot of time navigating to their point of interest, leading to severe underutilization of videos as a powerful medium of information.

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    I've used Federated learning to build anomaly detection models that monitor data quality and cybersecurity – while preserving data privacy.

    Federated learning enables Edge devices to collaboratively learn deep learning models but keeping all of the data on the device itself. Instead of moving data to the cloud, the models are trained on the device and only the updates of the model are shared across the network.

    Using federated learning gave me the following advantages:

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    • Low latency during inference
    • Privacy-preserving
    • Improved energy efficiency of the devices

    I built deep learning models using tensorflow and deployed using uTensor. uTensor is a light-weight ML inference framework built on Mbed and Tensorflow.

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    Rahul Agarwal - Continuous Data Integrity Tracking

    Rahul Agarwal
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    20 Mins
    Experience Report
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    "In God we trust; all others must bring data." - W. E. Deming, Author & Professor

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    Amit Doshi - Integrating Digital Twin and AI for Smarter Engineering Decisions

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    Anuj Gupta
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    Sold Out!
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