With mathematical optimization, companies can capture the key features of their business problems in an optimization model and can generate optimal solutions (which are used as the basis to make optimal decisions). Data scientists with some basic mathematical programming skills can easily learn how to build, implement, and maintain mathematical optimization applications. The Gurobi Python API borrows ideas from modeling languages, enabling users to deploy and solve mathematical optimization models with scripts that are easy to write, read, and maintain. Such modules can even be embedded in decision support systems for production-ready applications.

In this latest Data Science Central webinar, we will introduce and demonstrate how you can perform common time-series Machine Learning tasks such as Forecasting and Anomaly Detection, directly within the Influx platform without the need to use external tools, languages and services During this webinar, you will learn: How to initiate Machine Learning tasks directly… Read More »DSC Webinar Series: No-code ML for Forecasting and Anomaly Detection

Mathematical optimization (MO) technologies are being utilized today by leading global companies across industries – including aviation, energy, finance, logistics, telecommunications, manufacturing, media, and many more – to solve a wide range of complex, real-world problems, make optimal, data-driven decisions, and achieve greater operational efficiency. An increasing number of data scientists are adding MO into their analytics toolbox and developing applications that combine MO and machine learning (ML) technologies. In this series of webinars, we will show you how – with MO techniques – you can build interpretable models to tackle your prediction and classification problems. How to formulate an MO model. How to build an MO model using the Gurobi Python API.

A common challenge for teams working on video machine learning applications is how to scale and automate their ML lifecycle when working with these types of large unstructured datasets. In this latest Data Science Central webinar, Vincent Koops, Senior Data Scientist at RTL Netherlands, will walk through their Video AI platform at RTL and how they've addressed these challenges. Their platform is built on top of Pachyderm and Kubernetes to enable a wide range of ML applications such as automatic thumbnail picking and mid-roll marking.

Natural language processing has garnered interest in helping people interact with computer systems to make sense and meaning of the world. In the area of visual analytics, natural language has been shown to help improve the overall cognition of visualization tasks. In this latest Data Science Central webinar, Vidya will discuss how natural language can be leveraged in various aspects of the analytical workflow ranging from smarter data transformations, visual encodings, autocompletion to supporting analytical intent. More recently, chatbot systems have garnered interest as conversational interfaces for a variety of tasks. Machine learning approaches have proven to be promising for approximating the heuristics and conversational cues for continuous learning in a chatbot interface.

In this latest Data Science Central webinar, Dan Chaney, (VP of Enterprise AI & Data Science Solutions, Future Tech Enterprise, Inc.) and Lenny Isler, (Business Development, Advanced Computing Solutions, for HPi), discuss the key internal challenges that companies face when trying to manage their AI programs and align various stakeholders - business leadership, IT, and data science team members - around common objectives and business goals.

This special 30-minute interactive Data Science Central webinar includes a series of audience games and experiments that explore the relationship between people and data through the neuroscience of human perception, memory, decision making, and narrative. Watch to gain a clear understanding of how people make decisions and experience their world to help uncover the best ways to guide analytics functions and set an agenda for a data-driven culture within your own organization, through a series of practical, real-world examples.

With mathematical optimization, companies can capture the key features of their business problems in an optimization model and can generate optimal solutions (which are used as the basis to make optimal decisions). Data scientists with some basic mathematical programming skills can easily learn how to build, implement, and maintain mathematical optimization applications. The Gurobi Python API borrows ideas from modeling languages, enabling users to deploy and solve mathematical optimization models with scripts that are easy to write, read, and maintain. Such modules can even be embedded in decision support systems for production-ready applications.

In this latest Data Science Central webinar, we will introduce and demonstrate how you can perform common time-series Machine Learning tasks such as Forecastin…

Massive investments in data science teams and machine learning platforms have yet to yield results for most companies. The last mile for AI project success is the deployment and management of models in production requiring new technology and practices. This new area is called Machine Learning Operations or MLOps.