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5 Foundations of a Machine Learning Capability

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An introductory talk to a panel session on how to build a machine learning capability in enterprises

Publicado en: Datos y análisis
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5 Foundations of a Machine Learning Capability

  1. 1. 5 Foundations of a Machine Learning Capability LESSONS LEARNED FROM A 2 YEAR JOURNEY ENDA RIDGE Copyright Enda Ridge 2018#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge
  2. 2. What I’ve Learned PhD ‘Design of Experiments for Tuning Algorithms’ Data mining Software pre-sales Forensic Data Analytics Senior Manager Professional Services Head of Data Science & Algorithms Copyright Enda Ridge 2018#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 2004 2008 2010 2012 2015 Main challenge to applying machine learning successfully: • Organisations underestimate the dependencies and flexibility required 2014 1
  3. 3. About my organisation (and yours)  2nd largest grocery retailer in the UK  Almost ~150 years old  Employ almost 200,000 colleagues (~0.5% of UK workforce)  ~1,500 stores, > 90,000 products  ~250,000 Online orders per week Copyright Enda Ridge 2018#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 2  Large established enterprise  Legacy systems  Traditional approaches to operations  Many functional areas  Not all pristine digital data  warehouses, depots, even fields!
  4. 4. Foundation #1: strong leader in a central data team  Central Hub  A Senior Advocate  Clear Engagement Model  Clear Pipeline and Priorities Copyright Enda Ridge 2018#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge Projec Projec Projec Hub  Avoid these pitfalls:  Irrelevant projects  Expecting magic  Operational distractions  Overwhelming demand 3
  5. 5. Foundation #2: tactical environment ‘Lab’ Data store App Server Dev tools Copyright Enda Ridge 2018#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 4  Avoid IT dependency pitfalls  Scale  Permission groups  Proxy access  Local admin rights  Licencing  Tech Support  Data feeds
  6. 6. Foundation #3: build a track record of successes  Focus on the easy but high value opportunities  Prefer projects where you can insert machine learning in a light-weight way  Communicate progress (Perfect is the enemy of done)  ‘Marketing collateral’ when the job is completed Copyright Enda Ridge 2018#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 5 Pitfalls  Delivery black hole  Not building a fan base  Not securing funding for year 2
  7. 7. Foundation #4: People  1 or 2 data scientists who can do science and communicate  Begin the HR conversations early  Interview processes  Head count, salary budget  Training and progression  Break down silos for mixed teams Copyright Enda Ridge 2018#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 6 Pitfalls  Cannot productionise results  Churn  Results not understood
  8. 8. Foundation #5: Change in culture Copyright Enda Ridge 2018#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 7  Willingness to experiment (with customers)  Acceptance of failure  Humans in the loop  Increased data literacy  Mixed teams Pitfalls  Tissue rejection  Lack of engagement  Unable to quantify success
  9. 9. 5 Foundations of a machine learning capability Leadership with clear objectives Technology flexibility Track record of success People Change in culture Copyright Enda Ridge 2018#GuerrillaAnalytics http://guerrilla-analytics.net @enda_ridge 8 1 2 3 4 5

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