AI & Machine Learning in the Enterprise

Companies have to be as flexible as their changing macro-economic environments. Their data and their data teams need to mirror those conditions and built to operate with the constant change of goals and measurables. The right mix of people, processes and systems need to be organized to properly setup companies for success, especially within their data science toolbox. Building the best designed data teams to operate this climate is critical to the company’s and people’s success.

Launching a global and entirely virtual Data Science Hackathon during a pandemic wasn’t easy, but it sparked fantastic innovation and comradery for Visa’s data scientists. If you aspire to launch your own hackathon or any significant event to engage analytic teams, Arya will walk you through all the major milestones you’ll need to plan and execute to make it happen. This presentation will cover topics such as 1) securing executive buy-in, 2) building a budget proposal, 3) proper infrastructure, and 4) what successes you can highlight at the end of the event, among others.

Transfer learning and deep learning unblocked new levels of accuracy for many medical natural language processing tasks. The session shares the current state-of-the-art accuracy on the most widely used healthcare NLP tasks: clinical & biomedical named entity recognition, relation extraction, assertion status (negation) detection, entity resolution (terminologies mapping), and de-identification. You’ll see examples of how Spark NLP enables delivering this level of accuracy in real-world, production systems – adding privacy, tuning, scalability, and the ease of use of getting it all done with a single line of Python code.

Starting up and successfully establishing a data science function or team in organizations of any size is no easy task. From the inherent scientific complexity to differences in how data science teams operate compared to classic data analytics teams to difficulty in finding the right talent, it’s not a journey for the faint of heart. Yet many organizations and leaders have done this and built highly successful teams even amid a once in a lifetime pandemic. This talk will share a set of easy-to-follow Dos & Don’ts and lessons learnt from building up and establishing successful data science teams across multiple industries and companies.

As transformation is pursued, it generally occurs with some amount of friction or resistance, and therefore evolves in highly heterogeneous and disconnected ways across business functions. This presentation will elaborate on the issues faced by traditional firms as they attempt digital transformation in a data-driven world, and describe means to overcome them to achieve outsized and continuous business performance improvement.

Video object tracking is the process of detecting, following, predicting and understanding entities of interest across a video, both spatially and temporally. Conventional video tracking methods are effective in detecting and tracking objects, but typically require an external classification algorithm to identify and discriminate such objects. Similarly, traditional CV methods for image recognition can be too computationally expensive or insufficient for video tracking systems, which need to be lightweight enough to perform at frame rate and robust to random variations in scale, lighting, pose, and occlusion. Current state of the art deep learning methodologies are explored to solve the task of real time video object tracking with contextual understanding. Topics discussed include: DeepSORT, ROLO, Siamese Networks, JDE, GOTURN.

Modern deep learning models are gaining incredible capabilities. As they scale to more complex tasks, they inevitably are becoming larger beasts to tame and train. Training models on larger amounts of data and information contributes to high electricity bills and cloud compute costs, and if we aren’t careful the AI revolution might end up becoming one of the largest carbon emitters today. In this talk, we’ll first learn about bottlenecks slowing down training and inference, and then walk through features in PyTorch that can alleviate these inefficiencies with (mostly) drop-in code replacements.

Mark Moyou – Senior Data Scientist / Solutions Architect at NVIDIA
Connie Yee – Senior Data Scientist at Bloomberg
Jaime Russ – Principal Data Scientist at Ryder Systems
Dr. Man Chon (Kevin) U – VP, Head of Enterprise Data Technology & A.I. at AMEX
Charles Irizarry (Moderation) – Principal at Brim & Co, CEO at Phygital

Best practices in data visualization and dashboard design are numerous and sometimes contradictory, but a straightforward method to apply design thinking to creating dashboards is effective and universally applicable. This session will cover the details of design thinking and how it can be applied to dashboard development to create impactful dashboards that meet user needs and provide valuable insights.

Natural Language Processing is a contemporary topic in a variety of different fields. In this talk, we will provide a framework for assessing different machine learning applications generally and specifically on sentiment analysis. We will then go over a couple of case studies on NLP apps for financial and risk management applications, one in deep learning and the other leveraging on-line learning. We will cover their design and architecture then conclude with future extensions, further research and open it up for Q&A.

At Colgate, we have hundreds of models deployed at scale to help our scientists create better products, our marketers stay ahead of trends, and our sales teams optimize their portfolios. These business-facing models play a critical role in supporting decisions and uncovering new opportunities. Choosing the right model for the job is a crucial part of gaining business-acceptance and driving real value. Do we need Inference? Prediction? Prediction with Explainability? Inference with Prediction? This talk will go through some real-world case studies at Colgate where we balance parsimony, prediction, and explainability to deliver models that have tangible impact.

One of the major challenges faced in different enterprises is to generate the value out of the ML models in a short period of time and sustain the investment in the technology to grow it further. The bottlenecks are around the technology and governance behind the data science use cases. The scalability of the solution needs to thought through. The solution is to have right platforms to facilitate high volume feature engineering which uses timely data from different internal and external data sources. Setting up a suitable data ecosystem which is a coherent combination of Data Engineering, Data Science and Visualization technologies can produce really useful data products within an enterprise.

While drafting semitruck is never an option on the highway for obvious safety reasons, the concept of drafting as an analogy for corporate finance does have benefits for the organization. The concept of drafting involves linking vehicles at high speeds to reduce resistance and gain efficiency. If we think of drafting in the corporate finance realm the vehicles become Traditional Financial Models, Advanced Analytic tools and Data Science Machine Learning models. Take a trip with me through discussing drafting these vehicles together to create comprehensive Corporate Finance Fleet Management Solutions.

Joe Salvatore, Chief Risk Officer at Idea Financial
Noelle Silver, Global Partner AI and Analytics at IBM
Sha Edathumparampil, Chief Data Officer at Baptist Health South Florida
David Smith, VP, Data & Analytics at TheVentureCity
Charles Irizarry (Moderation), Principal at Brim & Co, CEO at Phygital

Over the last 10 years, Data Science has evolved in many ways, however only few organizations have gained the full competitive advantage from their advanced analytics, despite spending significant amounts of money in their Data Science and Machine Learning (ML) workforce and infrastructure. What could be the reasons? Basically the reasons are two-fold- firstly, most of the companies were rushing to apply data science before they were ready, and the second is the lack of well- skilled data science talents. This talk covers the second part, helping and guiding the Data Scientists to evolve their skill sets with increasing demand of the market. The traditional skill sets of data science are still valuable to build the foundational blocks, but it is important to evolve your skills along with the emerging technologies. As the advancement in technology and tools is trying to bridge the gap of complexity to democratizing data science, one important question that every data scientist should ask themselves- Are you riding the wave in the same direction? If not, you might already be falling behind.

Data teams are an integral part of many organizations nowadays and work not only to generate insights but also to develop tools and products. Many data teams follow agile guidelines, but the exploratory nature of data science creates a hinderance to use agile tools in its traditional form. In this talk I’ll explore aspects of agile and tweak some traditional methods to fit the data science development environment.

Machine Learning and Artificial Intelligence are pushing the boundaries across all the sectors. Health sector is no different. Using ML/AI, computer vision is being revolutionised. This presentation discusses the primary use cases of AI in computer vision for health care, the tool/technologies used, some case studies and major challenges faced.

As NLP has exploded within the world of Data Science and Machine Learning, it is now everywhere in finance as well. Many of us have heard about using NLP to try to outperform the market and predict stock prices. However, NLP is much more versatile than that, and has many other uses as well throughout the finance world. In this talk, I will explore a number of methodologies within NLP, explaining how they are being used in finance to help professionals do their job more efficiently and effectively.

It is  very common for data scientists today to be confronted with the need of processing and developing ML models with very large datasets.

In this chat we will share practical tips that can be applied during the machine learning project lifecycle that  will facilitate architecting  solutions to process large-scale data.

How MLOps is helping REEF to accelerate data science capabilities? In this presentation we will discuss REEF’s Machine Learning Operations Platform and the way the Data Science team work improved as a result of its implementation.

Moody Hadi – Group Manager – New Product Development & Financial Engineering at S&P Global Market Intelligence
Nirmal Budhathoki – Senior Data Scientist at VMware Carbon Black
Charles Irizarry (Moderation) – Principal at Brim & Co, CEO at Phygital