Machine learning for the enterprise (sponsored by IBM)
Who is this presentation for?
- You're a business decision maker looking to place bets on machine learning.
- You're a technical leader within an organization looking for the right machine learning framework.
- You're a CTO, CIO, or other IT executive looking to understand machine learning’s impact on the enterprise.
- You're a data scientist, machine learning engineer, or AI developer.
Description
Note: This free workshop, courtesy of IBM, is open to the first 50 registrants.
You’ll take a fascinating deep dive into the power and applications of machine learning in the enterprise. Beginning with the fundamentals of machine learning, how it works, and how enterprises are taking advantage of the benefits of working with machine learning applications, you’ll get a thorough introduction to three fascinating, business-ready use cases where machine learning leads to greater actionable insights, reduced costs, and increased revenue and profitability. You’ll be able to apply what you’ve learned with a tour of IBM Watson machine learning solutions, and you’ll walk away with concrete examples of how ML in action can benefit your large organization.
Outline
I. Introduction, welcome
- About speaker
- About IBM
II. A bit about the course
- Vision for the course
- Structure of the course
- Background and context
- Demonstrable use cases
- How IBM’s tools can help
- What you’ll learn, objectives, and by the end, you’ll be able to
- Details on breaks, lunch, restroom locations
III. About machine learning
- Methods
- Supervised
- Unsupervised
- High-level math concepts
- Statistics
- Pattern exploration
- Benefits
- Who’s using machine learning?
- Social media
- Medical and healthcare
- Online retail
- Finance
- Marketing
- Data security and defense
- Discussion of the chosen use cases
- Machine learning on the AI ladder and what isn’t machine learning?
- AI: What is AI, why is it confused, with ML and what’s the difference
- Optimization: What is optimization, why is it confused with ML, and what’s the difference
- Machine learning in the enterprise
- Business needs
- Security
- Resilience
- Other considerations
- Q&A
IV. Use cases
- Predicting customer churn [IBM Cloud]
- Relevance to the enterprise
- Prep
- Modeling and evaluation
- Deployment and test
- Scoring
- Q&A
- Visualizing data science [Watson Studio local]
- Relevance to the enterprise
- Load and access
- Analyze
- Deploy
- Best practices
- Q&A
- Decision optimization [Watson Studio local]
- Relevance to the enterprise
- Select data
- Run models
- Compare and create
- Modeling assistant
- Share and collaborate
- Q&A
V. The IBM ML ecosystem
- Relevance to the enterprise
- You can choose where to run, train, and deploy
- IBM Cloud benefits and strategies
- Private cloud/local support
- Watson Studio overview and why these tools make business sense
- Cloud
- Desktop
- Local
- Freebies
- Final Q&A and wrap-up, thank you
What you'll learn
- Learn what machine learning is and how it fits in with the AI picture and why your cloud and IBM’s cloud are both great places for machine learning workloads
- Discover the capabilities and profile of a machine learning-powered enterprise and how Watson Studio makes machine learning accessible in a variety of environments
- Explore popular machine learning use cases for enterprises and how they work
Matt Kirk
Your Chief Scientist
Matt Kirk is the founder of Your Chief Scientist, a firm devoted to training small cohorts of highly motivated engineers to become data scientist practitioners. He pulls from his experience writing Thoughtful Machine Learning with Python as well as his clients like ClickFunnels, Garver, SheerID, SupaDupa, and Madrona Venture. To find out more, check out yourchiefscientist.com.
Miguel Maldonado
IBM
Miguel Maldonado leads the Machine Learning curriculum development for IBM’s Data & AI Learning practice. He has 10+ years of experience in Machine Learning, Data Science, and AI. He has worked in R&D developing data mining products and served leadership roles as Director of Data Science and VP of Analytics across several industries including Retail, Banking, and Fintech. He holds a B.Sc. in Physics from Monterrey Tech and a M.S. in Analytics from NC State’s Institute for Advanced Analytics. In his free time he enjoys hiking and bouldering. He is passionate about democratizing Data Science and actively participates in datathons, local meetups, and events that support NGOs.
Presented by
Elite Sponsors
Strategic Sponsors
Zettabyte Sponsors
Contributing Sponsors
Exabyte Sponsors
Content Sponsor
Impact Sponsors
Supporting Sponsor
Non Profit
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