Data Science & Predictive Models

Wednesday, October 26, 2022

- PDT
OPEN TALK (AI): Patenting Artificial Intelligence– How AI Companies Can Identify and Protect AI Inventions
Steve Bachmann
Steve Bachmann
Bachmann Law Group PC, President, Silicon Valley Patent Attorney

Artificial intelligence is becoming one of the most widespread and useful technologies in use today. From data collection to model training, language processing to predictive models, deep networks to AI frameworks, there are many categories and implementations of AI, all with protectable features and important business applications. Protecting cutting edge AI technology helps companies achieve business goals and support their AI innovation.
This presentation will identify key strategies to identify which aspects of AI are patentable and which aspects are not. The discussed strategies will be supplemented with practical real-world examples of patenting different areas of the AI process, from data collection to model training and model implementation to output applications, as well as distinct types of AI systems.
Attendees will also learn about AI patent trends and the most common use cases in which different AI companies build valuable patent portfolios around their AI technology. 

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. 

- PDT
OPEN TALK (AI): Level Up Your Data Lake - to ML and Beyond
Vinodhini SD
Vinodhini SD
Treeverse, Developer Advocate

A data lake is primarily two things: an object store and the objects being stored. Even with the most basic setup, data lakes are capable of supporting BI, Machine Learning, and operational analytics use cases. This flexibility speaks to the strength of object stores, particularly their flexibility in integrating with a diverse set of data processing engines.

As data lakes exploded in adoption, a number of improvements were made to the first architectures. The first and most obvious improvement was to file formats, which led to the development of analytics-optimized formats like parquet, and eventually modern table formats.

An even newer improvement has been the emergence of data source control tools that bring new levels of manageability across an entire lake! In this talk, we'll cover how to incorporate these technologies into your data lake, and how they simplify workflows critical to ML experimentation, deployment of datasets, and more! 

Wednesday, November 2, 2022

- PDT
[#VIRTUAL] OPEN TALK (AI): Patenting Artificial Intelligence– How AI Companies Can Identify and Protect AI Inventions
Steve Bachmann
Steve Bachmann
Bachmann Law Group PC, President, Silicon Valley Patent Attorney

Artificial intelligence is becoming one of the most widespread and useful technologies in use today. From data collection to model training, language processing to predictive models, deep networks to AI frameworks, there are many categories and implementations of AI, all with protectable features and important business applications. Protecting cutting edge AI technology helps companies achieve business goals and support their AI innovation.
This presentation will identify key strategies to identify which aspects of AI are patentable and which aspects are not. The discussed strategies will be supplemented with practical real-world examples of patenting different areas of the AI process, from data collection to model training and model implementation to output applications, as well as distinct types of AI systems.
Attendees will also learn about AI patent trends and the most common use cases in which different AI companies build valuable patent portfolios around their AI technology. 

Thursday, November 3, 2022

- PDT
[#VIRTUAL] PRO TALK (AI): Enabling AI for Developers
Orly Amsalem
Orly Amsalem
cnvrg.io, VP of AI Innovation

Gartner TalentNeuron reveals that, in the U.S. employment market, there are roughly 140,000 people who describe themselves as data scientists. The supply of data science talent falls short compared to the demand for AI in everyday applications. Meanwhile, there are about 30M software developers that are looking for ways to enhance their applications with AI capabilities. The use of AI solutions is growing every day, with recommendation engines, text detection, virtual agents and more. As demand for AI grows, the fate of AI’s maturity relies on making AI accessible to developers, engineers, and business users, and offering the tools to apply machine learning in minutes.
In this session we will talk about how developers can become the main drivers of AI transformation and give an overview of cnvrg.io AI Blueprints, a new capability designed for data scientists, and software developers to build and create AI and integrate it to their applications. In minutes, any software developer or engineer can apply object detection, text-detection, pose-detection, scene detection and more into any application or solution they are building. Developers can use their own data to train and deploy their models using customizable and open source ML pipelines for any use case or industry. We will end with a real-life example of how to build and deploy a production-quality AI Blueprint in minutes. 

- 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. 

- PDT
[#VIRTUAL] OPEN TALK (AI): Level Up Your Data Lake - to ML and Beyond
Oz Katz
Oz Katz
Treeverse, CTO & Co-Founder

A data lake is primarily two things: an object store and the objects being stored. Even with the most basic setup, data lakes are capable of supporting BI, Machine Learning, and operational analytics use cases. This flexibility speaks to the strength of object stores, particularly their flexibility in integrating with a diverse set of data processing engines.

As data lakes exploded in adoption, a number of improvements were made to the first architectures. The first and most obvious improvement was to file formats, which led to the development of analytics-optimized formats like parquet, and eventually modern table formats.

An even newer improvement has been the emergence of data source control tools that bring new levels of manageability across an entire lake! In this talk, we'll cover how to incorporate these technologies into your data lake, and how they simplify workflows critical to ML experimentation, deployment of datasets, and more!