Deep Learning and Machine Learning models trained by many data professionals either end up in an
inference.ipynb notebook or an
app.pyfile 😅. Those meticulous model architectures capable of creating awe in the real world never see the light of the day. Those models just sit there in the background processing requests via an API gateway doing their job silently and making the system more intelligent.
People using those intelligent systems don’t always credit the Data Professionals who spent hours or weeks or months collecting data, cleaning the collected data, formatting the data to use it correctly, writing the model architecture, training that model architecture and validating it. And if the validation metrics are not very good, again going back to square one and repeating the cycle. Those people don’t see the struggle of a data professional not being able to use a 4 channel image on a pre-trained ResNet to get image features, they don’t appreciate the fact that it took a data professional days and nights to optimize the training parameters to make the model converge to a better accuracy or reach a lower loss value, they don’t get to see the data professional’s toil of choosing the best layer of combinations of best layers to predict the final output, or know the agonizing feeling when the validation scores are poor because the model weights weren’t loaded correctly or a different seed value was used at the start.
The Association of Data Scientists (AdaSci), the premier global professional body of data science and ML practitioners, has announced a hands-on workshop on deep learning model deployment on February 6, Saturday.
Over the last few years, the applications of deep learning models have increased exponentially, with use cases ranging from automated driving, fraud detection, healthcare, voice assistants, machine translation and text generation.
Typically, when data scientists start machine learning model development, they mostly focus on the algorithms to use, feature engineering process, and hyperparameters to make the model more accurate. However, model deployment is the most critical step in the machine learning pipeline. As a matter of fact, models can only be beneficial to a business if deployed and managed correctly. Model deployment or management is probably the most under discussed topic.
In this workshop, the attendees get to learn about ML lifecycle, from gathering data to the deployment of models. Researchers and data scientists can build a pipeline to log and deploy machine learning models. Alongside, they will be able to learn about the challenges associated with machine learning models in production and handling different toolkits to track and monitor these models once deployed.
#hands on deep learning #machine learning model deployment #machine learning models #model deployment #model deployment workshop
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#deep learning development #deep learning framework #deep learning expert #deep learning ai #deep learning services
If you are like me or any other Machine Learning enthusiast, the worst nightmare for you, obviously other than the Cuda errors, must be deploying the model elsewhere to present your work to your friends and networks on the internet. There are many ways to go around and achieve this, but in this article, I will share with you why I think Streamlit is the go-to solution for it.
To keep this article organized and easy to refer, let’s divide it into a few parts so that you can jump directly to whichever part that seems important for you.
There are a few other ways to deploy ML web apps but all of them have some drawbacks, the most noticeable one being that nothing is as easy and quick as Streamlit.
*Even when I say quickly, it still requires more work and time than what you’ll require for Streamlit!
#streamlit #aws #machine-learning #heroku #deployment #deep learning
The Deep Learning DevCon 2020, DLDC 2020, has exciting talks and sessions around the latest developments in the field of deep learning, that will not only be interesting for professionals of this field but also for the enthusiasts who are willing to make a career in the field of deep learning. The two-day conference scheduled for 29th and 30th October will host paper presentations, tech talks, workshops that will uncover some interesting developments as well as the latest research and advancement of this area. Further to this, with deep learning gaining massive traction, this conference will highlight some fascinating use cases across the world.
Here are ten interesting talks and sessions of DLDC 2020 that one should definitely attend:
By Dipanjan Sarkar
**About: **Adversarial Robustness in Deep Learning is a session presented by Dipanjan Sarkar, a Data Science Lead at Applied Materials, as well as a Google Developer Expert in Machine Learning. In this session, he will focus on the adversarial robustness in the field of deep learning, where he talks about its importance, different types of adversarial attacks, and will showcase some ways to train the neural networks with adversarial realisation. Considering abstract deep learning has brought us tremendous achievements in the fields of computer vision and natural language processing, this talk will be really interesting for people working in this area. With this session, the attendees will have a comprehensive understanding of adversarial perturbations in the field of deep learning and ways to deal with them with common recipes.
By Divye Singh
**About: **Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in technology degree in Mathematical Modeling and Simulation and has the interest to research in the field of artificial intelligence, learning-based systems, machine learning, etc. In this paper presentation, he will talk about the common problem of class imbalance in medical diagnosis and anomaly detection, and how the problem can be solved with a deep learning framework. The talk focuses on the paper, where he has proposed a synergistic over-sampling method generating informative synthetic minority class data by filtering the noise from the over-sampled examples. Further, he will also showcase the experimental results on several real-life imbalanced datasets to prove the effectiveness of the proposed method for binary classification problems.
By Dongsuk Hong
About: This is a paper presentation given by Dongsuk Hong, who is a PhD in Computer Science, and works in the big data centre of Korea Credit Information Services. This talk will introduce the attendees with machine learning and deep learning models for predicting self-employment default rates using credit information. He will talk about the study, where the DNN model is implemented for two purposes — a sub-model for the selection of credit information variables; and works for cascading to the final model that predicts default rates. Hong’s main research area is data analysis of credit information, where she is particularly interested in evaluating the performance of prediction models based on machine learning and deep learning. This talk will be interesting for the deep learning practitioners who are willing to make a career in this field.
#opinions #attend dldc 2020 #deep learning #deep learning sessions #deep learning talks #dldc 2020 #top deep learning sessions at dldc 2020 #top deep learning talks at dldc 2020
Imagine building a supervised machine learning ML model to decide whether a credit card transaction has detected fraud or not. With the model confidence level in successful applications, we can evaluate the risk-free credit cards transactions. So you have built the model, which can detect credit card frauds, now what? The deployment of such ML-model is the prime goal of the project.
Deploying an ML-model simply means the integration of the model into an existing production environment which can take in an input and return an output that can be used in making practical business decisions. Here is where Streamlit comes to play !
Streamlit is a open-source app framework__is__the easiest way for data scientists and machine learning engineers to create beautiful, performant apps in only a few hours! All in pure Python. All for free.
In the part one of this tutorial I am going to deploy a Supervised machine learning model to predict the age of a Abalone and in the next part of the tutorial we will host this web app on Heroku. An Abalone is a molluscs with a peculiar ear-shaped shell lined of mother of pearl. Abalone’s age can be obtained using their physical measurement. Let us deploy the model.
#machine-learning #supervised-learning #deploy #deployment-model #streamlit