Watch Live: May 24th @ 9am PT / 12 pm ET / 5pm GMT
Organizations that can actively harness data are 2.8 times more likely to post double-digit revenue growth. But given that the average enterprise analytics program wrestles with more than 1,000 different data sources, that’s easier said than done.
The inability of enterprises to extract and load data from databases, APIs, and other sources at speed and scale blocks them from using all of their data to make confident decisions faster. Most data integration solutions today are either fragmented and disconnected, or expensive and not built for the cloud.
Join us on May 24 for an exclusive launch event. Hear Matillion CEO Matthew Scullion, CPO Ciaran Dynes, Matillion product leaders, and an IDC analyst share why unifying data integration is critical to unblock and unlock your data to quickly make it useful. They will be joined by Matillion customer, Mulesoft, who will share how they use Matillion Data Loader to streamline their data integration.
Give your team the tools and techniques they need to unlock the potential in their data today.
CEO at Matillion
CPO at Matillion
RVP, Sales at Matillion
Senior Manager, Data Engineering at Mulesoft
Watch Live: May 25th @ 2PM EST
In this Webinar, Juan Martinez from John Snow Labs and Ken Puffer from ePlus will share lessons learned from recent AI, ML, and NLP projects that have been successfully built & deployed in US hospital systems:
- Improving patient flow forecasting at Kaiser Permanente
- A real-time clinical decision support platform for Psychiatry and Oncology at Mount Sinai
- Automated de-identification of 700 million patient notes at Providence Health
Then they will showcase a live demo of the recently launched AI Workflow Accelerator Bundle for Healthcare, which provides a complete data science platform including supporting the full AI lifecycle:
- Data analysis: Enable data analysts to query, visualize & build dashboards without coding
- Data science: Enable data scientists to train models, share & scale experiments
- Model deployment options
- Operations: Enable DevOps & DataOps engineers to monitor, secure, and scale
The bundle is a turnkey solution composed of GPU-accelerated hardware from NVIDIA, proprietary software from John Snow Labs, and implementation services from ePlus. It is unique in providing all of the following healthcare-specific capabilities out of the box:
- 2,300+ current, clean, and enriched healthcare datasets – from ontologies to benchmarks
- Spark NLP for Healthcare – the most widely used NLP library in the healthcare industry – along with 250+ pre-trained clinical & biomedical NLP models for analyzing unstructured data
- Spark OCR – including the ability to read, de-identify, and extract information from DICOM images
- Security controls implemented within the platform, to enable a team of data scientists to effectively work & collaborate in air-gap, high-compliance environments
We will share speed & accuracy benchmarks measuring the optimization of John Snow Labs’ software and models on the GPU-accelerated Nvidia hardware – and how this translates to enabling your AI team to deliver bigger projects faster.
Juan Martinez, Master’s Degree in Computer Engineering, works as Sr. Data Scientist at John Snow Labs as part of the Healthcare Team. He has accumulated experience applying different NLP frameworks in many different areas of activity, including Healthcare, Finance and Legal.
Ken Puffer is the Chief Technology Officer for Healthcare solutions at ePlus. In this role, Ken consults with a broad range of healthcare leaders and technology partners to help ePlus develop, deploy, optimize, and maintain solutions that help solve the unique challenges facing healthcare.
As Chief Technology Officer and Chief Information Security Officer for 20 years at a major healthcare system in the New York Metro area, Ken brings a comprehensive understanding of addressing the business challenges present when delivering healthcare in a highly competitive environment. Among Ken’s accomplishments includes the creation and leadership of the organization’s Help Desk, Desktop Management, Network Operations, Security Operations, Technology Selection and Biomedical Engineering Teams. Ken also led the technology review team for the hospital’s EMR selection committee. The team successfully accomplished the goal of designing a highly-available system with a focus on accessibility for the care delivery team and supportability by existing operational resources.
Watch Live: WEDNESDAY, JUNE 1 @ 2PM ET
As teams look to build and deploy models into production, they need tools that can adequately scale with them. In particular, the tools they need must allow them to quickly monitor, segment, retrain, and experiment on the data.
Join us as the team at Pachyderm and Superwise discuss:
- What is MLOps and why is data critical for it
- How to architect a scalable and automated platform
- Why your team should adopt a Production-First data approach.
Oryan Omer - Lead Software Engineer at Superwise
Oryan Omer is on the lead software engineering team at Superwise with 7 years experience developing ML products. Oryan has also led an elite unit of engineers for Israel Defense Forces. In recent years, the focus has been on MLOps solutions to simplify the ML Life cycle. In his spare time, he surfs on every board there is (Wave surfing, snowboarding, carving, etc..)
Harpreet Sahotar - Developer Relations at Pachyderm
Harpreet is part of the Developer Relations team at Pachyderm, where he works across marketing, content, evangelism, and product teams. He’s also host of The Artists of Data Science podcast, father, avid reader, perpetual learner, craft beer snob.
Watch Live: June 29 @ 2PM EST
Broken data got you down? You are not alone. We’ve met hundreds of data teams that experience broken dashboards, poorly trained ML models, and inaccurate analytics — and we’ve been there ourselves. We call this problem data downtime, and we found it leads to sleepless nights, lost revenue, and wasted time.
Learn how to automatically detect data problems in minutes. Learn how to know when data breaks using ML-generated and custom rules, instantly conduct root cause analysis on common data issues and scale your data team and make incident management easier.
During this session, we’ll show how Data Observability can help you to:
- Know when data breaks using ML-generated and custom rules
- Instantly conduct root cause analysis on common data issues
- Scale your data team and make incident management easier
Experienced Solutions Architect with a demonstrated history of success working in SAAS, specifically the Analytics space. Skilled in Python, JQL, Analytics, and bridging the gap between customer feedback and Product. Master’s degree in Product Development Engineering from University of Southern California.