AI & Machine Learning
Wednesday, November 17, 2021
Artificial intelligence, machine learning, and deep learning are intertwined capabilities that attempt to solve problems that defy traditional computational solutions — problems include fraud detection, voice recognition, and search result recommendations. While they defy simple computation, they are computationally expensive, involving computation of perhaps millions of probabilities and weights. While these computations can be done outside of the database, there are specific advantages of doing machine learning inside the database, close to where the data is stored. This presentation explains how to do machine learning inside the Postgres database.
The Agile Metrics are important to track the health of your projects. They help in tracking the project progress. There are other advanced metrics equally important, like Customer Satisfaction, Employee Satisfaction, and Innovation. Tracking these statistics many times is not easy and straightforward.
Did you ever think of applying AI (Artificial Intelligence) to measure these and come up with actionable evidence? The AI-powered with NLP (Natural language Processing) and statistical models not just help in getting a good project insight, it can also help in course corrections, and increase the rate of project success. It can help companies to understand their core strengths, weaknesses, and how to position themselves in the market.
Rohit will talk and demonstrate how you can digitally transform your Agile Program Management with AI and NLP. How it enables organizations to take proactive measures that not only make projects successful but also help companies stay competitive and thrive in the market.
There are many ways to tell when your application breaks. But figuring out what caused it to break is slow and tedious as engineers hunt through logs and dashboards, piecing together the details of what happened.
Fortunately, unsupervised machine learning can speed-up the process. It works by automatically finding the log events and metrics that describe the root cause, and it uses GPT-3 to provide a plain language summary of the problem.
Thursday, November 18, 2021
In this talk I will present a technique for deploying machine learning models to provide real-time predictions using Apache Pulsar Functions. In order to provide a prediction in real-time, the model usually receives a single data point from the caller, and is expected to provide an accurate prediction within a few milliseconds.
Throughout this talk, I will demonstrate the steps required to deploy a fully-trained ML that predicts the delivery time for a food delivery service based upon real-time traffic information, the customer's location, and the restaurant that will be fulfilling the order.
Over the past decade, graph databases have become an indispensable asset in dealing with networked and non-relational data. However, as the amount of data ingested into graph databases has exploded, performance has become a key criterion when determining which one to use. In addition, it is critical for organizations to understand the type of use cases in which graph databases can add business value relative to more traditional SQL or other types of NoSQL databases. During the workshop, we will dig into the fundamentals of Labeled Property Graphs and highlight use cases, ranging from fraud detection, money laundering, and complex manufacturing to real-time analytics for Customer 360. We will show where graph databases are not simply an option but perhaps the only choice available. Lastly, we will explore how to use Python to interact with the graph databases to extract features to be used in ML models. This hands-on workshop will cover: - Graph Fundamentals - Graph Use-Cases - Introduction to TigerGraph Cloud - Integrating Python with TigerGraph Cloud - Feature Generation for Supervised Machine Learning
Today, API software solutions are usually designed first for the cloud, and often a particular cloud services provider. This was the case for rev.ai, our speech to text API. However, many use cases still require on-premise or at least private cloud deployments - whether due to privacy, latency or cost considerations.
This session will describe how we adapted our cloud-based speech-to-text API for on-premise deployment and the challenges we faced in doing so. We will discuss maintaining consistency between cloud and on-premise APIs, maximizing code reuse, and enabling platform-agnostic scalability.
Artificial Intelligence and Machine Learning are gradually making in-roads on every aspect of business and technology, so it’s no surprise that it appears in the DevOps tool chain, but what does it really do? This session discusses the state-of-the-art of applied AI technologies, and provides detailed examples of practical use-cases, including Machine Learning, Natural Language Processing and Neural Networks.