Join IBM Developer for the very first online AI and ML Summit. The AI and ML Summit is an exciting half day of talks, workshop and virtual networking with speakers from Twilio, Weights and Biases, IBM and others. While we are not able to provide real food, we will be giving out virtual 🍕s!
Free and open to all - come and learn about AI, Machine Learning, Data Science, how to leverage AI in the enterprise, and what is the future of AI. Bring your questions and curiosity for learning.
In the next 5-10 years, we’re going to see an explosion in narrow intelligent AI applications, the decisions of which will increasingly affect our lives in domains like - loan application approval, self driving cars, predicting recidivism and many more. This gives rise to some prescient problems, including the need to understand the decisions being made by these algorithms. In this talk, Lavanya will introduce methods that help explain the predictions made by black box machine learning models, as well as the tools built by Weights & Biases that help you explain the outputs of your own models.
As the great Shakespeare once said, "2B || !2B" (or "to be or not to be"). This talk will go over you how you can improve your communication apps with ML tasks like text analysis and classification via phone calls, SMS, and chat using TensorFlow and other ML libraries.
This session will also feature a live-coding demo and look at performing sentiment analysis, entity analysis, and text classification with neural networks and other deep learning methods so you can best serve customers before they need it or when they want it.
Learn how to build and deploy your very own AI predictive models. IBM Watson Studio AutoAI automatically analyzes your data and generates customized predictive model pipelines. It categorizes data as needed, does features engineering and optimizes hyperparameters to get to the best possible model. We will show you how to integrate Watson Studio AutoAI into your projects.
Using AutoAI, you can build and deploy a machine learning model with sophisticated training features and no coding. The tool does most of the work for you. We will walk you through how to deploy your customs models to your application.
The AutoAI process follows this sequence to build candidate pipelines:
Data Pre-Processing
Automated Model Selection
Automated Feature Engineering
Hyperparameter Optimization
Whether you are counting cars on a road or people stranded on rooftops in a natural disaster, there are plenty of use cases for object detection. Often times, pre-trained object detection models do not suit our needs and we need to create our own custom models.
How can we utilize machine learning to train our own custom model without substantive computing power and time?
Answer: Watson Machine Learning.
How can we leverage our custom trained model to detect object’s, in real-time, with complete user privacy, all in the browser?
Answer: TensorFlow.js.
Lavanya is a machine learning engineer @ Weights and Biases. She began working on AI 10 years ago when she founded ACM SIGAI at Purdue University as a sophomore. In a past life, she taught herself to code at age 10, and built her first startup at 14. She's driven by a deep desire to understand the universe around us better by using machine learning. You can find her on twitter @lavanya.ai.
Eric Schles is a senior data scientist with 6 years of full time experience. During his time in industry he has worked in the anti human trafficking, cancer research, government and big tech spaces. During his time at Microsoft he worked as a consulting engineer building production systems for fortune 500 and 100 clients all over the world. During his time in government he worked for the Federal Reserve in San Francisco, the White House and the General Services administration, bringing data science into the procurement process, health systems, human resource systems and in various inter agency consulting capacities. In addition, he worked on strategic cross federal initiatives such as the white house data council and various internal research goals to bring data science to federal agencies as well as assess readiness of agencies for data science.
Jenna Ritten (@jritten) is a cloud software developer turned developer advocate for IBM Cloud. She works on expanding the reach of open source technologies to Detroit and Austin's developer communities. Her areas of interest include hackathons, design-thinking workshops, NLU/NLP, gamified learning, and Blockchain. Jenna left Detroit to attend Dev Bootcamp San Francisco to learn full-stack web development before joining IBM Austin as part of the Tech Re-Entry program. She is an advocate for non-traditional people in tech, much like herself, and she provides support by building and fostering communities for underrepresented people in tech.