AI EXPO & DEMO HALL

Demo Talk | In-person

In the Python open-source ecosystem, many packages are available that cater to:
– the building of great algorithms
– the visualization of data

Despite this, over 85% of Data Science Pilots remain pilots and do not make it to the production stage.

With Taipy, a new open-source Python framework, Data Scientists/Python Developers are able to build great pilots as well as stunning production-ready applications for end-users.
Taipy provides two independent modules: Taipy GUI and Taipy Core.

In this talk, we will demonstrate how:
Taipy-GUI goes way beyond the capabilities of the standard graphical stack: Gradio, Streamlit, Dash, etc.
Taipy Core fills a void in the standard Python back-end stack.

Demo Talk | In-person

Integrating and unifying data from diverse sources is foundational to AI and ML workflows. This workshop will demonstrate how Anzo’s knowledge graph platform can create an enterprise scale knowledge graph from several sources – setting organizations up for sustainable success with collective intelligence. During this workshop, users will:

Create a sample knowledge graph from several sources.
Demonstrate flexible data preparation for training datasets.
Analyze the knowledge graph with native visualizations and graph algorithms
Connect to the knowledge graph for additional data science operations

From its hyper agile in-memory MPP graph engine to its point-and-click user experience and open flexible architecture, Anzo transcends the limitations of traditional knowledge graphs and gives you all the capabilities and flexibilities that complex, enterprise-scale solutions need.

Join this demo to see why Anzo might be the solution you need.

Demo Talk | Virtual

Experienced machine learning engineers and data scientists care about ways to easily get their models up and running quickly and share ML assets across teams for collaboration. Collaborate and streamline the management of thousands of models across teams with new, innovative features in Azure Machine Learning. Come and join us in this interactive session with our product experts and get your questions answered on the latest capabilities in Azure Machine Learning!

Demo Talk | Virtual

Modeling time series data is difficult due to its large quantities and constantly evolving nature. Existing techniques have limitations in scalability, agility, explainability, and accuracy. Despite 50 years of research, current techniques often fall short when applied to time series data. The Tangent Information Modeler (TIM) offers a game-changing approach with efficient and effective feature engineering based on Information Geometry. This multivariate modeling co-pilot can handle a wider range of time series use cases with award-winning results and incredible performance.

During this demo session we will showcase how best-in-class and very transparent time series models can be built with just one iteration through the data. We will cover several concrete use cases for advanced time series forecasting, anomaly detection and root cause analysis.

Demo Talk | In-person

Join this demo to find how to centralize your ML pipeline and cut down operational complexities at each stage along the way. Qwak’s platform supports multiple use cases across any business vertical and allows data teams to productionize their models more efficiently and without depending on engineering resources.

Join us to watch how <presenter name> uses Qwak to create features from data and build, train and deploy models into production. All under a single platform and with unprecedented simplicity.

Demo Talk | In-person

Modeling time series data is difficult due to its large quantities and constantly evolving nature. Existing techniques have limitations in scalability, agility, explainability, and accuracy. Despite 50 years of research, current techniques often fall short when applied to time series data. The Tangent Information Modeler (TIM) offers a game-changing approach with efficient and effective feature engineering based on Information Geometry. This multivariate modeling co-pilot can handle a wider range of time series use cases with award-winning results and incredible performance.

During this demo session we will showcase how best-in-class and very transparent time series models can be built with just one iteration through the data. We will cover several concrete use cases for advanced time series forecasting, anomaly detection and root cause analysis.

Demo Talk | Virtual

Integrating and unifying data from diverse sources is foundational to AI and ML workflows. This workshop will demonstrate how Anzo’s knowledge graph platform can create an enterprise scale knowledge graph from several sources – setting organizations up for sustainable success with collective intelligence. During this workshop, users will:

Create a sample knowledge graph from several sources.
Demonstrate flexible data preparation for training datasets.
Analyze the knowledge graph with native visualizations and graph algorithms
Connect to the knowledge graph for additional data science operations

From its hyper agile in-memory MPP graph engine to its point-and-click user experience and open flexible architecture, Anzo transcends the limitations of traditional knowledge graphs and gives you all the capabilities and flexibilities that complex, enterprise-scale solutions need.

Join this demo to see why Anzo might be the solution you need.

Demo Talk | In-person

Learn why the truly open source HPCC Systems platform is better at Big Data and offers an end-to-end solution for Developers and Data Scientists. Learn how ECL can empower you to build powerful data queries with ease. HPCC Systems, a comprehensive and dedicated data lake platform makes combining different types of data easier and faster than competing platforms — even data stored in massive, mixed schema data lakes — and it scales very quickly as your data needs grow. Topics include HPCC Architecture, Embedded Languages and external datastores, Machine Learning Library, Visualization, Application Security and more.

Demo Talk | In-person

When putting models into production it’s critical to know how they’re performing over time. As the last mile of the data pipeline, models can be impacted by a variety of issues, often outside the control of the data science team. “Observability” promises to help teams detect and prevent issues that could impact their models—but what is observability vs. data observability vs. ML observability? Get practical answers and recommendations from Kyle Kirwan, former product leader for Uber’s metadata tools, and founder of data observability company, Bigeye.

Demo Talk | In-person

In this 25-minute demo, we will explore the top 5 cool tricks of Delta for data scientists and discuss why your data lake should be a Delta Lake. Delta Lake is an open-source storage layer that brings reliability to data lakes by providing ACID transactions, scalable metadata handling, and data versioning. We will first introduce the concept of Delta Lake and explain how it helps data scientists to manage their data pipelines with ease. We will then dive into the top 5 cool tricks of Delta Lake, which include performance optimizations, time travel, schema enforcement, automatic data merging, and data validation. We will demonstrate these tricks using real-world examples and show how they can simplify your data pipeline and reduce your development time. By the end of this talk, you will have a better understanding of Delta Lake’s features and how it can help you to manage your data lake efficiently. You will also have learned about the benefits of using Delta Lake and why it’s a must-have for data scientists working with large data sets.

Demo Talk | In-person

MLRun is an open-source MLOps orchestration framework. It exists to accelerate the integration of AI/ML applications into existing business workflows. MLRun introduces Data Scientists to a simple Python SDK that transforms their code into a production-quality application. It does so by abstracting the many layers involved in the MLOps pipeline. Developers can build, test, and tune their work anywhere and leverage MLRun to integrate with other components of their business workflow.

The capabilities of MLRun are extensive, and we will cover the basics to get you started. You will leave this session with enough information to:

• Get started with MLRun, on your own, in 10 minutes, so you can automate and accelerate your path to production and have your first AI app running in 20 minutes
• Run local move to Kubernetes
• Understand how your Python code can run as a Kubernetes job with no code changes
• Track your experiments
• Get an introduction to advanced MLOps topics using MLRun”

Demo Talk | In-person

Beacon Analytics helps customers transition from rigid and monolithic data solutions to flexible microservices architecture, enabling better performance and faster access to critical information. By breaking up data into smaller, independent services, customers gain greater access and modification capabilities. The team recommends using the Polars library, which is based on Apache Arrow, in combination with Dash Plotly to create easy to maintain, high-performance solutions at an excellent price-to-performance ratio. Join Danny Bharat, Senior Vice President of Analytics at Cedric Millar and co-founder of Beacon Analytics, as he shares how his team’s innovative approach to data solutions allows them to build comprehensive 360° intelligence and deliver actionable insights. Beacon Analytics empowers customers to achieve success in a rapidly changing business and technology landscape by utilizing schema-on-read approaches, unstructured data storage, and on-the-fly analysis and transformation.