AI & Research,And Industrial Tracks - all videos, Also inviting registrations for Signal In Bangalore This update is for participants only
There are three phases in the lifecycle of an application - research, application and aftermath of the application.
- Assess capabilities, determining the new frontiers for AI.
- Find a use for the application.
- Learn how to run it, monitor it and update it with time.
The three tracks at the 2023 Monsoon edition of The Fifth Elephant will cover this lifecycle.
The 2023 Monsoon edition is curated by:
- Nischal HP, Vice President of Data Engineering and Data Science at Scoutbee. Nischal curated the MLOps conference which was held online between 23 and 27 July 2021.
- Sumod Mohan, Founder and CEO at AutoInfer. Sumod curated Anthill Inside 2019 edition, held in Bangalore on 23 November.
- AI and Research - covers research, findings, and solutions for challenges on building models in various areas such as fraud detection, forecasting, and analytics. This track delves into the latest methodologies for handling challenges such as large-scale data processing, distributed computing, and optimizing model performance.
- Industrial applications of ML - covers implementation of AI in the industry, with more focus on the AI models, the issues in training, gathering data so, and so forth. ML is being used at scale in industries such as automotive, mechanical, manufacturing, agriculture, and such domains. This track focuses on the challenges in this space, as we see innovation coming out of these industries in the pursuit of using ML on a second-to-second basis.
- AI and Product - covers strategies for building AI products to scale and mitigating challenges. This track provides insights on incorporating AI tools and forecasting techniques to improve model training, developing a working model architecture, and using data in the business context.
The Fifth Elephant 2023 Monsoon edition will be held in-person. Attendance is open to The Fifth Elephant members only. Purchase a membership to attend the conference in-person. If you have questions about participation, post a comment here.
- Data/MLOps engineers who want to learn about state-of-the-art tools and techniques, especially from domains such as automobile, agri-tech and mechanical industries.
- Data scientists who want a deeper understanding of model deployment/governance.
- Architects who are building ML workflows that scale.
- Tech founders who are building products that require AI or ML.
- Product managers, who want to learn about the process of building AI/ML products.
- Directors, VPs and senior tech leadership who are building AI/ML teams.
Sponsorship slots are open for:
- Infrastructure (GPU, CPU and cloud providers) and developer productivity tool makers who want to evangelise their offering to developers and decision-makers.
- Companies seeking tech branding among AI and ML developers.
- Venture Capital (VC) firms and investors who want to scan the landscape of innovations and innovators in AI and who want to source leads for investment in the AI and ML space.
Ensemble Techniques for Object Detection Models
DNN (Deep Neural Network) models are nonlinear and have a high variance, which can be frustrating when preparing a final model for making predictions. In order to get good results with any model, there are certain criteria (data, hyperparameters) that need to be fulfilled. But in the real-world scenario, you might either end up with bad training data or might have a hard time figuring out appropriate hyperparameters.
A successful approach to reducing the variance of neural network models is to train multiple models instead of a single model and to combine the predictions from these models. Ensemble learning has been proven to improve the generalization ability effectively in both theory and practice. An ensemble system may be more efficient at improving overall accuracy for the same increase in compute, storage, or communication resources by using that increase on two or more methods than would have been improved by increasing resource use for a single method.
Using ensemble techniques for machine learning models has become a fairly common practice that combines multiple learners on a single-learning task but when you look at object detection (classification + localization) models it’s not so straightforward to apply them. One of the bigger problems with ensemble techniques is getting appropriate results without a significant drop in model inference time. Each technique comes with its own advantages and disadvantages and also depends on the models that are being used. To verify and demonstrate which methods work better, we compiled results for different ensemble techniques (including state-of-the-art techniques) on our own models trained for logo detection.