Machine Learning at Reasonable Scale
Build and operationalize machine learning with rigid budgetary and technological constraints in mind in this live coure with Ciro Greco and Jacopo Tagliabue.
Access to: session recordings, curated resources and exclusive events
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 ML engineers or ML managers in early to mid-stage companies
who want to reduce maintenance costs, technical debt and unnecessary hirings
- Design and manage reliable end-to-end ML systems with rigid business and organizational constraints
- Reduce maintenance costs, technical debt and unnecessary hirings
- Have a basic understanding of ML best practices: train/test split, feature engineering, regression vs. classification, supervised vs. unsupervised, etc.Â
- 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
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.Â
- What are the essential components of an ML stack?Â
- What should you buy versus build?Â
- How should the budget be distributed across Data and ML?Â
- How do they impact hiring and retention strategy?Â
- 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.
In traditional software development, CI/CD automates many tasks, including testing, building and deploying software. But CI/CD for ML is a different beast. Testing and deployment of ML can be triggered by many event types, and observability and logging requirements are materially different for ML.Today, no single tool can facilitate end-to-end CI/CD for ML. The process of testing, building and deploying ML requires a symphony of tools and glue code to create an integrated CI/CD system. To offer an entry point that many data scientists and engineers are familiar with, we’ll teach you how to integrate GitHub with other ML tools to build custom CI/CD automations for ML that will increase your engineering efficiency and prevent errors from being released to production.
- 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)
- Leverage data warehouses for data transformation in ML data pipelines
- Design a data platform that supports data versioning and stateless transformation in SQL
- Design a NoOps ML pipeline for training and serving
- Use Metaflow as a declarative framework to design the ML pipeline
- 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)
- Choose between different team design patterns
- Identify the key KPIs to assess the effectiveness of your choice in the future
- 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
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Team?
Sphere offers a range of subscription packages that provide discounts on all courses in our library. We help upskill employees at some of the world’s best companies. Learn more about pricing options here or book a time to talk to one of our staff below.
Book a free consultation
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.Â
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.Â
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.
Guest Lectures by
Industry Experts
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.
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.
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!
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