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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

Designed for ML engineers or ML managers in early to mid-stage companies

who want to reduce maintenance costs, technical debt and unnecessary hirings

You will:
  • Design and manage reliable end-to-end ML systems with rigid business and organizational constraints
  • Reduce maintenance costs, technical debt and unnecessary hirings
You should:
  • Have a basic understanding of ML best practices: train/test split, feature engineering, regression vs. classification, supervised vs. unsupervised, etc. 
  • Have familiarity with Python and basic SQL (e.g., experience working with basic data science packages like Numpy, Scikit Learn, Pandas; and some familiarity with ML packages like Tensorflow and Pytorch)
  • Have at least three years of experience in working in a software organization

Learn live from a world-class

Instructor

Learn live from a world-class

Instructor

Ciro holds a Ph.D. in Neuroscience and has worked as a researcher at the University of Milan-Bicocca, MIT, and Ghent University. He published extensively in Cognitive Sciences, Linguistics, and Machine Learning. In 2017, he co-founded Tooso, a San Francisco-based startup in NLP and information retrieval. Ciro served as the CEO of Tooso, leading the company from its inception to its acquisition in 2019 by Coveo. At Coveo, Ciro worked in the executive team and led the AI team from the scale-up stage to IPO in 2021 (TSX:CVO). In addition, he worked on data and ML strategy, as well as on data governance and M&A. 

Learn live from a world-class

Instructor

Learn live from a world-class

Instructor

Educated in several acronyms across the globe (UNISR, SFI, MIT), Jacopo was CTO and founder of Tooso, an AI company acquired by Coveo (TSX:CVO) in 2019. At Coveo, he led the AI and MLOps roadmap from scale-up to IPO, and built out Coveo Labs, an applied R&D practice rooted in word-class collaborations (Stanford, NVIDIA, Microsoft, Uber, Oxford), open source and open science: Coveo Labs work appeared at NAACL, RecSys, ACL, and WWW, among other venues. He's also a frequent speaker on dataOps, ML, and Reasonable Scale strategies in industry and academia. He is currently an Adj. Professor of ML Systems at NYU.

Learn live from world-class

Instructors

Ciro holds a Ph.D. in Neuroscience and has worked as a researcher at the University of Milan-Bicocca, MIT, and Ghent University. He published extensively in Cognitive Sciences, Linguistics, and Machine Learning. In 2017, he co-founded Tooso, a San Francisco-based startup in NLP and information retrieval. Ciro served as the CEO of Tooso, leading the company from its inception to its acquisition in 2019 by Coveo. At Coveo, Ciro worked in the executive team and led the AI team from the scale-up stage to IPO in 2021 (TSX:CVO). In addition, he worked on data and ML strategy, as well as on data governance and M&A. 

Educated in several acronyms across the globe (UNISR, SFI, MIT), Jacopo was CTO and founder of Tooso, an AI company acquired by Coveo (TSX:CVO) in 2019. At Coveo, he led the AI and MLOps roadmap from scale-up to IPO, and built out Coveo Labs, an applied R&D practice rooted in word-class collaborations (Stanford, NVIDIA, Microsoft, Uber, Oxford), open source and open science: Coveo Labs work appeared at NAACL, RecSys, ACL, and WWW, among other venues. He's also a frequent speaker on dataOps, ML, and Reasonable Scale strategies in industry and academia. He is currently an Adj. Professor of ML Systems at NYU.

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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 Ciro & Jacopo's

Live Cohort

This course will teach a framework for machine learning at Reasonable Scale (RS). At its essence, RS means that ML systems are designed and deployed with four explicit constraints in mind: financial impact, team size, data volume, and computing resources.

For RS companies, the ideal ML stack requires the minimal amount of Ops possible by maximizing the investments in the components that bring the highest marginal gain. Choices that appear at first glance exquisitely technical can play a profound role in the larger context of organizations’ business strategy and constraints. 

  • What are the essential components of an ML stack? 
  • What should you buy versus build? 
  • How should the budget be distributed across Data and ML? 
  • How do they impact hiring and retention strategy? 
  • What are the implications for COGS and a company’s economics?

Leave this course able to operationalize ML with unmovable budgetary and technological constraints in mind.

Session 1: ML at Reasonable Scale Framework

Monday, March 6, 2023
1-3 pm PST
  • Use the key factors of this framework to identify and rank the relevant constraints of your organization to measure future ROI (e.g., developer experience, speed of innovation, costs, retention, etc)

Session 2: Applying the Framework to DataOps

Wednesday, March 8, 2023
1-3 pm PST
  • Leverage data warehouses for data transformation in ML data pipelines
  • Design a data platform that supports data versioning and stateless transformation in SQL

Session 3: Applying the Framework to MLOps at Reasonable Scale

Monday, March 13, 2023
1-3 pm PST
  • Design a NoOps ML pipeline for training and serving
  • Use Metaflow as a declarative framework to design the ML pipeline

Session 4: Choose Vendors and Technology

Wednesday, March 15, 2023
1-3 pm PST
  • Identify what vendors/technologies play an essential role in your strategic design (i.e., changing them in the future will be hard) vs. what vendors/technologies are peripheral (i.e., you can choose what you like better and change it in the future)

Session 5: Choose the Right Team Structure

Monday, March 20, 2023
1-3 pm PST
  • Choose between different team design patterns
  • Identify the key KPIs to assess the effectiveness of your choice in the future

Session 6: Factor in Hidden Costs Before You Build

Wednesday, March 22, 2023
1-3 pm PST
  • Estimate different types of costs: opportunity costs, operational costs, hard COGS
  • Design a strategy to address them
  • Communicate the strategy to stakeholders with different backgrounds

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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