Machine Learning In Production
People spend most of their time and efforts in building really good generalized models which could give satisfactory numbers on different metrics. Most of the tutorials, blogs and articles focus on explaining various concepts such as feature engineering, model selection, hyperparameter tuning etc. and using model in production is a topic that is often overlooked.
After you have built your perfect model, then comes the next part how do I serve this to people so they can also use it. This is the question that I faced a while ago when I wanted to use my model on a webapp and display results based on the users inputs. This is when I started exploring the topic in AI which is equally important but very less talked about.
The real value of machine learning models lies in production but most of the models end up being in a Jupyter Notebook (in the form of Python code) or inside a folder on the local computer. If you have also faced the same problems then this talk is for you !
In this talk I'll tell you about the working of the systems in production, how to plan their architecture, the various ways you can deploy your model to production, their pros and cons, and when to use what. All these topics will be supported by code snippets and demos including real life examples. The talk will also revolve around making the best use of the available open source tools and frameworks to build a reliable and scalable pipeline for you machine learning system.
Outline/Structure of the Talk
I plan to cover the following topics during my talk:
- Development vs Production
- Why should you care about your Production Environment
- How to plan your Production Architecture
- Serve your model using Flask API
- How to build APIs using Flask and connect it with your model
- Ways to deploy it on Cloud Platforms
- Containerize your Models
- Introduction to Docker
- Building Docker images for your model
- Deploying Docker Images on cloud
- Deploying Models on the Edge devices
- What are Edge devices (with Use Cases)
- Model Pruning, Quantization
All the topics mentioned will be explained in a interactive way along with code snippets and demos.
After attending this session, the attendees will:
- have an extensive and practical knowledge about deploying their models in production
- will have deep insights of which method to choose when
- take the model deployment problem more seriously
Data Scientists, Machine Learning/Deep Learning Engineers, Data Engineers, People working in DevOps
Prerequisites for Attendees
Since this is a beginner level talk so having familiarity with basic topics of machine learning and deep learning should be fine.
schedule Submitted 1 year ago
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Tushar Mittal - Train your GANs Faster and Achive Better ResultsTushar MittalStudentKanpur Institute of Technology
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GANs have been in trend since they were introduced, back in 2014 and have also produced some very amazing results in every domain ranging from images to videos and even audios.
When reading and understanding about the working of GANs, they seem very intuitive and not that hard to train. It is when you get into training them you realize that its quite hard to train and achieve good results with your GAN architecture, and I learnt this the hard way.
I trained my first ever GAN as part of a contest on Kaggle, wherein the task was to generate new unseen images of dogs using the given 20,000 images. I gladly entered the competition thinking how hard it could be. But as I trained my first model and analyzed the results I realized that its not as simple as it looks, and as I progressed through the competition, I participated in various discussions, read the kernels submitted by others and tried out various approaches. This taught me a lot about training GANs the right ways. I have trained various GANs on several datasets since then.
So in this talk I want to share the tips and tricks that worked for me in achieving good results so you can directly use them and not have to learn them the hard way as I did.