AI for the Enterprise

Wednesday, October 26, 2022

- PDT
PRO TALK (AI): ML Drift Monitoring : What to Observe, How to Analyze & When to Act
Kumaran Ponnambalam
Kumaran Ponnambalam
Cisco, Principal Engineer

Deploying a new ML model in production successfully is a great achievement, but also is the beginning of a persistent challenge to keep them performing at expected levels. Models in product will drift and decay, and the value provided by them to the business will drop. ML drift monitoring is a challenging tasks, from identifying the right data to collect, the right metrics to compute, the right trends to analyze and the right actions to take. This session will explore the process of model drift monitoring, from model instrumentation to determining the next-best-action. Real life challenges will be explored and best practices and recommendations will be discussed. 

- PDT
PRO TALK (AI): Data Ecosystem a Stepping Stone for Decarbonization of Operation Industry

Climate change is possibly one of the most complex and challenging issues on earth. On the other hand, manufacturing companies often find themselves in the crosswind of it. Oil and gas, mining, chemical, cement, energy, and utility sectors are responsible for more than 50% of the industrial GHG emissions. The changes they are bringing into their operations are not enough to address the issue. New initiatives for carbon abetment are not showing any visible improvement in reducing GHG levels in the environment.

In this session, we will analyze how data ecosystems such as LiDAR, remote-sensing data, IT, and OT data pertinent to these manufacturing companies can help them to track/measure, trace and mitigate excess emission issues for their operations. We will also explore how advanced AI techniques such as deep learning, and reinforcement learning techniques can be used effectively to find an optimal solution for the above-mentioned problem/s with real-life examples. 

Thursday, October 27, 2022

- PDT
OPEN TALK (AI): Operationalizing AI with a Shift from Research to Product Orientation
Yotam Oren
Yotam Oren
Mona, CEO & Cofounder

Many AI programs fail to deliver sustained value despite great research, due to insufficient operational tools, processes and practices. These days, more and more data science teams are going through a major shift, from research orientation, to product orientation. Key factors to successfully transition to a product-oriented approach to AI include empowering data scientists to take end to end accountability for model performance, and going beyond the model - gaining a granular understanding of the behavior of the entire AI-driven process. In this talk, Yotam will discuss the importance of empowering data science teams to successfully make the transition from research oriented to product oriented. 

Wednesday, November 2, 2022

- PDT
[#VIRTUAL] PRO TALK (AI): ML Drift Monitoring : What to Observe, How to Analyze & When to Act
Kumaran Ponnambalam
Kumaran Ponnambalam
Cisco, Principal Engineer

Deploying a new ML model in production successfully is a great achievement, but also is the beginning of a persistent challenge to keep them performing at expected levels. Models in product will drift and decay, and the value provided by them to the business will drop. ML drift monitoring is a challenging tasks, from identifying the right data to collect, the right metrics to compute, the right trends to analyze and the right actions to take. This session will explore the process of model drift monitoring, from model instrumentation to determining the next-best-action. Real life challenges will be explored and best practices and recommendations will be discussed. 

- PDT
[#VIRTUAL] PRO TALK (AI): Data Ecosystem a Stepping Stone for Decarbonization of Operation Industry

Climate change is possibly one of the most complex and challenging issues on earth. On the other hand, manufacturing companies often find themselves in the crosswind of it. Oil and gas, mining, chemical, cement, energy, and utility sectors are responsible for more than 50% of the industrial GHG emissions. The changes they are bringing into their operations are not enough to address the issue. New initiatives for carbon abetment are not showing any visible improvement in reducing GHG levels in the environment.

In this session, we will analyze how data ecosystems such as LiDAR, remote-sensing data, IT, and OT data pertinent to these manufacturing companies can help them to track/measure, trace and mitigate excess emission issues for their operations. We will also explore how advanced AI techniques such as deep learning, and reinforcement learning techniques can be used effectively to find an optimal solution for the above-mentioned problem/s with real-life examples. 

- PDT
[#VIRTUAL] OPEN TALK (AI): It’s an AI Product Manager’s Job to Help an Organization Succeed with Predictive Machine Learning
Paul Ortchanian
Paul Ortchanian
Bain Public, Founder, CEO, Head of Product, Data and Strategy

In short, AI is a lifecycle that requires the integration of data, machine learning models, and the software around it. It covers everything from scoping and designing to building and testing all the way through to deployment — and eventually requires frequent monitoring. Product managers need to ensure that data scientists are delivering results in efficient ways so business counterparts can understand, interpret, and use it to learn from. This includes everything from the definition of the problem, the coverage and quality of the data set and its analysis, to the presentation of results and the follow-up. 

- PDT
[#VIRTUAL] OPEN TALK (AI): Building Enterprise Grade Accessible Applications Using Microsoft Azure AI
Prashant G Bhoyar
Prashant G Bhoyar
AIS, Cloud Solution Architect

Microsoft's CEO Satya Nadella has said: "Human Language is the new UI layer, bots are like new application". As more and more bots are getting popular in homes and enterprises, the demand for custom bots is increasing at rapid space.

Azure Communication Services is a cloud-based communications service that lets you add voice, video, chat, and telephony to your apps.

The Microsoft Bot framework is a comprehensive open-source offering that we can use to build and deploy high-quality bots.

Microsoft Cognitive Services let you build apps with powerful algorithms to see, hear, speak, understand and interpret our needs using natural methods of communication, with just a few lines of code. Easily add intelligent features – such as emotion and sentiment detection, vision and speech recognition, language understanding, knowledge, and search – into your app, across devices and platforms such as iOS, Android, and Windows, keep improving and are easy to set up.

In this demo-driven session, we will cover how to build the enterprise-grade intelligent bots in using Microsoft Bot Framework, Cognitive Services, and Azure Communication Services and deploy in Microsoft Teams and other platforms like SharePoint, Public-Facing Web Sites, etc 

Thursday, November 3, 2022

- PDT
[#VIRTUAL] PRO TALK (AI): Leveraging Automated Machine Learning to Enable Anyone to Develop Machine Learning Solutions
Hugo Barona
Hugo Barona
Ergo, Cloud Solution Architect

Nowadays, several business owners know that leveraging Artificial Intelligence capabilities, on their systems and applications, can enable their businesses to achieve better results. But building Artificial Intelligence solutions may be a time-consuming and complex process, so consequently, some of these people give up of building such solutions, since they or their team do not have the expertise and capacity required, or sometimes they end-up paying to third-party companies to build these solutions and as a consequence, they end-up doing a significant investment on building these solutions. Azure Automated Machile Learning is the solution to enable anyone to build the Artificial Intelligence and Machine Learning solutions at low cost and with the best quality possible. 

- PDT
[#VIRTUAL] OPEN TALK (AI): Operationalizing AI with a Shift from Research to Product Orientation
Yotam Oren
Yotam Oren
Mona, CEO & Cofounder

Many AI programs fail to deliver sustained value despite great research, due to insufficient operational tools, processes and practices. These days, more and more data science teams are going through a major shift, from research orientation, to product orientation. Key factors to successfully transition to a product-oriented approach to AI include empowering data scientists to take end to end accountability for model performance, and going beyond the model - gaining a granular understanding of the behavior of the entire AI-driven process. In this talk, Yotam will discuss the importance of empowering data science teams to successfully make the transition from research oriented to product oriented.