Decision Optimization Using ML Models
Machine learning models make predictions, but predictions don't tell you what action to take. Learn how to optimize decision rules and measure the impact of your work, live with Dan Becker, an industry leader with experience at Google and Data Robot.
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Designed to help make organizations
Data Driven
Product Leaders
Program managers and product managers that are focused on metrics like growth and revenue, and prioritization decisions
Data Scientists
Data Scientists and Data Science managers who help map strategic decisions to actionable experimental designs and then interpret the results in a trustworthy manner
Engineering Leaders
Engineering managers, directors, VPs, and CTOs who want to make their organizations data-driven with metrics and A/B tests
Designed for Data Scientists and ML Engineers
who want to better prioritization of ML efforts by estimating business impacts of models before building them
- Optimize business rules for programmatically translating ML predictions into actions
- Measure the impact of deployed ML models on business metrics like costs, revenue, customer retention, etc.
- Identify and fix the common problems that prevent ML systems from leading to better business outcomes
- Be proficient at building machine learning models with tabular data and comparing standard validation measures (eg confusion matrix, AUC and RMSE scores)
- Have experience building deep learning models with Keras / TensorFlow
- Be comfortable with statistical ideas like density functions
Live Cohort
- Apply profit curves for optimizing decision thresholds with binary classification models
- Predict the business value of alternative ML projects before you start them, and estimate the dollar value of potential improvements in model quality
- Identify limitations of profit curves through a case study of insurance carrier prioritizing which medical claims to inspect
- Case Study: optimizing retail inventory using a demand forecasting model
- Build a deep learning model to predict statistical distributions rather than just point predictions
- Evaluate how alternative loss metrics apply to different real-world decision-making processes
- Case Study in financial services
- Design joint strategy for model validation and decision rule validation
- Integrate multiple ML models for more realistic simulations
- Explain the sim2real problem and alternatives to make decision optimization more robust
- Use domain knowledge to proactively correct for how training data may misrepresent future relationships
- Define strategy for testing a decision rule in real world with minimal risk
- Develop a strategy for bringing decision optimization to your business
- Evaluate and improve proposals for applying decision optimization
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Learn live from a world-class
Instructor
Learn live from a world-class
Instructor
Dan Becker is the VP of ML Development Tools for DataRobot. He finished in 2nd place (out of 1350+ teams) in a machine learning competition with a $500,000 grand prize. He's led AI consulting projects for 6 companies in the Fortunate 100, and over 500,000 people have taken his applied AI courses on Kaggle. Dan is a contributor to TensorFlow and Keras. He worked as a data scientist at Google before founding Decision.AI to help data scientists optimize the decision rules they use for translating ML predictions into business decisions. Dan has a PhD in economics.
Learn live from world-class
Instructors
Dan Becker is the VP of ML Development Tools for DataRobot. He finished in 2nd place (out of 1350+ teams) in a machine learning competition with a $500,000 grand prize. He's led AI consulting projects for 6 companies in the Fortunate 100, and over 500,000 people have taken his applied AI courses on Kaggle. Dan is a contributor to TensorFlow and Keras. He worked as a data scientist at Google before founding Decision.AI to help data scientists optimize the decision rules they use for translating ML predictions into business decisions. Dan has a PhD in economics.
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Community
This cohort gives you access to a rich community of like-minded professionals from some of the best businesses in the world. Even after the course ends, you will continue to learn and build with each other.
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You don’t! We record every live session in the cohort and make each recording and the session slides available on our portal for you to access anytime.
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Each learner receives a certificate of completion, which is sent to you upon completion of the cohort (along with access to our Alumni portal!). Additionally, Sphere is listed as a school on LinkedIn so you can display your certificate in the Education section of your profile.
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Throughout the cohort, there may be take-home questions that pertain to subsequent sessions. These are optional, but allow you to engage more with the instructor and other cohort members!
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