Founder & CEO
Senior Business Intelligence Analyst
ML Engineer, Data & Machine Learning
Technical Product Manager
VP of Product
Senior Data Scientist
Head of Core AI
Bridging Communication Gaps | Process Standardization & Repeatability | Streamlining Automation | Enabling Technology | Governance | Agile DS/ML
|12:00 pm ET||
Verta MLOps Salon Welcome and Kickoff
Manasi Vartak - Founder & CEO at Verta
|12:10 pm ET||
Improve Machine Learning Team Productivity with Standardization and Automation - an Introduction to Skelebot
Sherry Wang - Senior Data Scientist at Cars.com
There are many huddles a machine learning engineer/data scientist has to overcome to bring a model from ideation to production, especially on the cloud. How to recreate the development environment easily at a cheap price? Is there a way to enable teams with limited knowledge of container technology to self-deploy docker images? How to version control code as well as artifacts for a ML project? How to encourage teams to continuously create tools to automate repetitive tasks involved in the ML development process? In this talk, the audience will get to see how we solved these problems at cars.com by creating the open source tool skelebot, and be inspired to apply it to solve their own problems as well.
|12:45 pm ET||
Accelerating ML Workflow with Kubeflow, ModelDB, and Feast
Jaiganesh Prabhakaran - Machine Learning Engineer, Data & Machine Learning at Zulily
A key to Zulily’s success is having a robust ML infrastructure and a wide array of ML solutions that use both structured and unstructured data. This conversation will cover Zulily's ML platform, which is built on top of Kubernetes and uses open-source tools, such as Kubeflow, ModelDB, and Feast, and how the platform helps accelerate ML workflow for ML engineers and data scientists within Zulily’s data science and analytics teams.
|1:20 pm ET||
How to transform experimental AI projects into successful products
Meeta Dash - VP of Product at Verta
Have you wondered why more than half of the AI initiatives fizzle out in the pilot state? Where are the gaps - business strategy, people, process or tools?
In this talk I will talk about how product managers can help steer the ship in the right direction and increase the success rate of AI initiatives.
|2:05 pm ET||
Automation and the need of CI/CD pipeline in machine learning development
Lukas Gubo - MLOps Project Manager at Virufy
Our strategy is to deploy robust but explainable models. To achieve this we are using several open source tools to help us with model development. Many models would remain unused and not brought up to the production stage. Therefore, we count on these tools and are able to make a streamlined process of deploying models.
|2:40 pm ET||
Building ML Platforms for Model Development and Training: Challenges and Best Practices
Meenakshi Sharma - Technical Product Manager at Wayfair
Machine learning platforms and operations are a critical part of a company’s data science strategy allowing data scientists to perform model lifecycle management starting from exploration, development, training to the final deployment and monitoring. Despite the wide range of frameworks, tools and technologies available, developing robust, scalable and efficient DSML pipelines still remains a big challenge due to the complex and dynamic nature of the ML workflows. In this session, I would like to discuss design considerations, challenges and best practices while building platforms for model development and training. In this session, I would like to deep dive into challenges associated with managing these specialized teams as they build and integrate new machine learning capabilities into the business. This would include but not be limited to - recruiting and maintaining a highly skilled team, balancing skill sets, better designed and targeted training programs, managing cross-team dynamics as a company grows and matures.
|3:15 pm ET||
Evolving your ML solutions with collaboration and technology
Orestes Castaneda - Senior Business Intelligence Analyst at CBS Interactive
As businesses and their competitive landscape evolve, more challenging business questions arise. This provides unique opportunities to develop ML applications to enable data-and-intelligence-based decision making. With that, BI and Data Science teams need to quickly adapt and mature their processes to work efficiently and innovate. In this session, I will share process, collaboration and technology components that organizations and teams can use to evolve their solutions portfolio.
|3:50 pm ET||
Automating AI development for the Edge
Brian Cruz - Head of Core AI at Samba TV
The ability to run Artificial Intelligence algorithms at the edge provides a number of benefits not just limited to better latency, privacy, and cost. In fact, the advent of edge computing has initiated a new paradigm for distributed computing that enables entirely new classes of products and services. Creating new AI-enabled products within this new ecosystem brings with it a number of challenges, one of which is the automation of the development process itself. The development processes for hardware, software, and AI often differ, and this introduces a challenging set of constraints on projects. By combining the time-tested best practices from each of these three areas we can automate many parts of the development process in order to build better products that go to market more quickly.
|4:30 pm ET||
45 min Panel Discussion
Manasi Vartak - Founder & CEO at Verta
|5:25 pm ET||