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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

The motivation and basics of A/B testing (e.g., causality, surprising examples, metrics, interpreting results, trust and pitfalls, Twyman’s law, A/A tests)
Cultural challenges, humbling results (e.g., failing often, pivoting, iterating), experimentation platform, institutional memory and meta-analysis, ethics
Hierarchy of evidence, Expected Value of Information (EVI), complementary techniques, risks in observational causal studies

Engineering Leaders

Engineering managers, directors, VPs, and CTOs who want to make their organizations data-driven with metrics and A/B tests

Safe deployments
Triggering, especially in evaluating machine learning models
The benefits of agile product development

Engineering Leaders, Data Scientists and ML Engineers

who want to prioritize, release and scale ML projects that solve business use cases

You will:
  • Prioritize ML projects that solve business use cases
  • Design an ML system and invest in infrastructure necessary to solve a business use case
  • Structure your team so that different stakeholders can work productively together to achieve the business objectives
You should:
  • Understand the basic theory behind ML models such as clustering, logistic regression, decision trees, and neural network architectures
  • Understand how metrics such as accuracy, F1, precision, recall, ROC, mean squared error, or log-likelihood to are used evaluate model performance

Learn live from a world-class

Instructor

Learn live from a world-class

Instructor

I’m Chip Huyen, a co-founder of Claypot AI, a platform for real-time machine learning. Previously, I built machine learning tools at NVIDIA, Snorkel AI, and Netflix.

I graduated from Stanford University, where I currently teach CS 329S: Machine Learning Systems Design. My O’Reilly book Designing Machine Learning Systems is an Amazon #1 bestseller in Artificial Intelligence (very proud)!

LinkedIn included me among Top Voices in Software Development (2019) and Top Voices in Data Science & AI (2020). I’ve also published 4 non-technical books in Vietnamese.

Coming from a writing background, I love storytelling. I hope to incorporate a lot of stories and examples into my course to make the materials easier to understand.

Learn live from world-class

Instructors

I’m Chip Huyen, a co-founder of Claypot AI, a platform for real-time machine learning. Previously, I built machine learning tools at NVIDIA, Snorkel AI, and Netflix.

I graduated from Stanford University, where I currently teach CS 329S: Machine Learning Systems Design. My O’Reilly book Designing Machine Learning Systems is an Amazon #1 bestseller in Artificial Intelligence (very proud)!

LinkedIn included me among Top Voices in Software Development (2019) and Top Voices in Data Science & AI (2020). I’ve also published 4 non-technical books in Vietnamese.

Coming from a writing background, I love storytelling. I hope to incorporate a lot of stories and examples into my course to make the materials easier to understand.

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Industry Experts

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Expense
the cost

90% of our learners expense the full cost of our courses to their employer. This includes leading startups and enterprises alike.

Check Expense Approval At Your Company

Exclusive Content

to advance your business

Get access to exclusive content through live sessions, meetups and our Student Portal (even after you finish the cohort). Ask questions and get personal feedback directly from your instructors and others taking the course.

Join a diverse and experienced

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.

About Chip's

Live Cohort

ML systems are complex. They consist of many different components: business requirements, data, ML algorithms, underlying infrastructure, etc. They involve many different stakeholders: data scientists, engineers, business leaders, users, even society at large.

This course aims to give you a holistic understanding of ML systems. It takes into account different components of the system and the objectives of different stakeholders involved. Throughout the course, the course is illustrated with examples, case studies, and exercises.

Session 1: Prioritizing ML Projects to Solve Business Use Cases

Tuesday, February 14, 2023
1-3 pm PST
  • Determine when and when not to use machine learning, and use this knowledge to identify potential use cases at different maturity stages of a company
  • Convert business use cases into measurable ML objectives
  • Anticipate the challenges of delivering an ML project and allocate resources to address these challenges

Session 2: Data Flow Through an End-to-End ML System

Thursday, February 16, 2023
1-3 pm PST
  • Understand different layers of a data system needed to support an ML system
  • Compare different techniques to sample and create training data to train your model
  • Understand feature engineering and how to avoid data leakage

Session 3: Model Serving

Tuesday, February 21, 2023
1-3 pm PST
  • Evaluate different ways to serve a model’s predictions to users: batch prediction, online prediction with batch features, online predictions with real-time and near real-time features
  • Design your infrastructure to support different types of predictions
  • Understand how ML systems’ characteristics can negatively affect user experience, and how to minimize the effect with techniques such as smooth failing and involving human-in-the-loop

Session 4: Model Evaluation, Monitoring, and Continual Learning

Thursday, February 23, 2023
1-3 pm PST
  • Design a pipeline to effectively evaluate your models, both during development (offline evaluation) and in production (test-in-production)
  • Continue monitoring models while they are in production for distribution shifts
  • Set up your system so that your models can be continually updated to adapt to changing environments and business requirements

Session 5: Infrastructure and Organization Structure for Productive MLOps

Tuesday, February 28, 2023
1-3 pm PST
  • Evaluate key components of an ML platform including model management, prediction service, feature store, feature engineering framework, and monitoring
  • Structure your team so that different stakeholders can work productively together to achieve the business objectives

Still have questions?

We’re here to help!

Do I have to attend all of the sessions live in real-time?

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.

Will I receive a certificate upon completion?

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.

Is there homework?

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!

Can I get the course fee reimbursed by my company?

While we cannot guarantee that your company will cover the cost of the cohort, we are accredited by the Continuing Professional Development (CPD) Standards Office, meaning many of our learners are able to expense the course via their company or team’s L&D budget. We even provide an email template you can use to request approval.

I have more questions, how can I get in touch?

Please reach out to us via our Contact Form with any questions. We’re here to help!

Book a time to talk with the Sphere team

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Learning Experience