The finance sector is among the keenest adopters of machine learning (ML) and artificial intelligence (AI), the predictive powers of which have been demonstrated everywhere from back-office process automation to customer-facing applications. AI models excel in domains requiring pattern recognition based on well-labeled data, like fraud detection models trained on past behavior. ML can support employees as well as enhance customer experience, for example through conversational AI chatbots to assist consumers or decision-support tools for employees. Financial services companies have used ML for scenario modeling and to help traders respond quickly to fast-moving and turbulent financial markets. As a leader in AI, the finance industry is spearheading these and dozens more uses of AI.

The risk landscape of AI is broad and evolving. For instance, ML models, which are often developed using vast, complex, and continuously updated datasets, require a high level of digitization and connectivity in software and engineering pipelines. Yet the eradication of IT silos, both within the enterprise and potentially with external partners, increases the attack surface for cyber criminals and hackers. Cyber security and resilience is an essential component of the digital transformation agenda on which AI depends. A second established risk is bias. Because historical social inequities are baked into raw data, they can be codified--and magnified--in automated decisions leading, for instance, to unfair credit, loan, and insurance decisions.

The'smart skin' is like a fabric that contains sensors that recognize crucial indications of a human as well as airborne chemicals. Envisioned for a range of applications from baby monitoring to warfare, smart skin applications are expected to grow exponentially in the future. After nearly six years of research at the Technical University of Graz, Italian-born Anna Maria Coclite has developed the'smart skin' for the next generation of artificial intelligence materials. It senses pressure, humidity, and temperature all the while and produces electronic signs. More delicate robots or more AI prostheses will be consequently possible.

When the standard error of mean drops the red threshold we have determined, an alert would be sent, which would require us to look at the model performance and take necessary action like retraining. Retraining can be done in two different methods, either manual retraining or automatic retraining; manual retraining is far more common, as most teams are apprehensive about retraining their models without human interference. Next, we would look at a deployment done by me in Heroku using flask and python. I worked on a case study project, to provide a demo of the same, I deployed the machine learning model as a web application. The case study was to predict the abuse category based on the description provided by the victim.

As a beginner, it's hard to realize how the end product of your project should look. You start with a basic machine learning pipeline, and as the project evolves, you adjust and enhance the components to meet your golden metric. To communicate your work with the world, you'd like to have a way for people to interact with the model and evaluate its performance. In this blog, we will be learning how to build a Streamlit application using only python and deploy it to a remote Heroku server. . We will use the Pneumonia-Classification project and showcase how to deploy its Streamlit app to the cloud.

We faced some difficulties with Streamlit.io You can see our SageMaker implementation here. The purpose of this article is to provide a tutorial with examples showing how to deploy ML models to AWS SageMaker. This tutorial covers only deploying ML models that are not trained in SageMaker. It is more complicated to deploy your ML models that are trained outside of AWS SageMaker than training the models and deploy end-to-end within SageMaker.

The real value of machine learning models lies in their usability. If the model is not properly deployed, used, and continuously updated through cycles of customer feedback, it is doomed to stay in a GitHub repository, never reaching its actual potential. In this article, we will learn how to deploy a Spotify Recommendation Model in Flask in a few simple steps. The application we will deploy is stored in a recommendation_app folder. In the root directory, we have the wsgi.py

In the previous part, we have designed the app.py our main interface and in this part, we are gonna focus on the functionality of our application. Before creating the functionality of the application we will make sure that our model is ready for this I found drive as the best platform for storing our model since on GitHub we can store models just up to 20 MB. We load the model using Keras load_model function and return it for making predictions on that model. Finally, make sure you commit and push to your GitHub repository. If you want to learn how to push files on GitHub refer to Jayesh Jain's blog

After building a machine learning model on some problem you might want to share it with other friends to let them see what it can do and for projects. For deploying our models the Streamlit platform…

The Dataset we are going to use is the Loan prediction dataset. The loan prediction dataset is a unique dataset that contains 12 columns. The data was gathered to predict if a customer is eligible for a loan. The Dataset is publicly available on Kaggle and can be accessed using this link. Let's Start with the bottom-up approach and build a simple Machine learning model.