Industrializing Machine LearningHow to integrate ML in existing businesses

Erik Schmiegelow, CEO, Hivemind TechnologiesTwitter: @eschmiegelow

HIVEMIND

Agenda

I.  Status Quo in Machine Learning

II.  Getting Started

III.  Implementation

I. Status Quo“Artificial Intelligence” is all the rage, popping up everywhere: in driverless cars, object and speech recognition, Alpha Go, Robot automation.

The data incumbents (Google, Facebook, AWS and others) invest enormous resources to develop such tools, whilenew businesses threaten older, established companies in their own markets with data ‒ driven approaches.

I. Status QuoMachine Learning vs. Deep Learning - None of this is frightfully new, it’s been around since the 1950’sMachine Learning is AI in its basic form, parsing data, applying statistical algorithms to learn from it and predict outcomes.

Deep Learning is machine learning on steroids by attempting to mimic the neurons in a brain, each connected in intertwined layer of recognition and processing.

I. Status QuoGoing Mainstream

If it’s so old, why is it going through the roof now?

Abundance of data ‒ it has never been so easy and cheap to collect large data sets.

Tooling ‒ the combination of open source and the efforts of G.A.F.A and others have given us a wide array of freely available tools (Pandas, SparkML,TensorFlow, etc..).

I. Status QuoWhy is this relevant for me?

Leaving the lab – applying Machine Learning isn’t the sole domain of research scientists anymore. (Even though they still push the envelope)

Start ups and existing companies alike are redefining their spaces, fuelled bymassive investment in AI.

I. Status QuoA storm is coming

Literally every industry is affected – with deployment in obvious areas such as FinTech and Commerce, but also in:•  Health care•  Food and agriculture•  Manufacturing•  Energy

…with early movers rewarded with a significant competitive advantage.

II. Getting StartedSo now what?

Despite the existence of tools, introducing Machine Learning isn’t like installing a magic box and switching it on.

You will need:•  initial datasets•  an organisation that supports it•  a team that builds it•  a product that integrates it

II. Getting Started ‒ Data ProcessThe crucial first step is to get the data flywheel running.

s1

s2

t2 s3

t1

More DataBetter products

Smarter Algorithms

More users

Identify the initial use case ‒ define the problems you want to address with Machine Learning

Secure the data pipeline ‒ automated data ingest is key. Your predictions can only be as accurate as your data is complete.

Redefine the products ‒ assess the implications and effects on your products

Understand and assist your users ‒ in adopting and welcoming the changes in your product and UX

II. Getting Started - Challenges

•  Getting it right involves iterating multiple times•  Start with small use cases with controllable outcomes

and gradually increase scope•  Manage expectations ‒ AI is not unicorn fairy dust which

magically transforms things overnight.•  Help users and management understand what problems

ML can solve (and which it cannot)

III. Implementation - DataEssentially, four steps:

1. CollectionThe first step is to widen the net on collectible data. Funnel every possible source into one repository and add as many relevant external sources as possible.

2. Measure and ExploreEstablish and explore relationships between data sets. Analyze occurrences and score attributes of data records.

III. Implementation - Data3. Classify and trainClassify metricized records according to their business value (e.g. customers according to conversion, basket sizes, etc..) and connect that to activity. Train models using established classifications.

4. Apply the models to your business flow and productsRun your predictions along existing products and validate outcomes, apply a/b testing

III. Implementation - OrganizationOrganizational support factors

•  Identify the users •  Integrate the data suppliers•  Determine satisfaction levels with existing systems and

processes•  Inform and win over the stake holders•  Check for privacy and legal issues

… and create a dedicated data team to implement change

III. Implementation ‒ Data TeamAn effective data team consists of two roles: engineers and data scientists:1.  Engineers will setup

data ingestion and automate processing

2.  Data scientists explore data and apply algorithms and statistical models

III. Implementation – Data TeamSetting up an effective data team requires the following steps:1.  Have the organisation relinquish data ownership to the

data team2.  Setup a data ingestions role responsible for data

collection and conversion3.  Setup a data science role to explore relationships4.  Setup a data engineering role to automate data

aggregation and model training, application and reporting

III. Implementation ‒ Evolve ProductsWith your data process in place, the next steps are:1.  Hook up your data team with every step of your value

chain2.  Validate every process with real time metrics 3.  Challenge every process step with metrics4.  Engage users and incorporate feedback

III. Implementation - OpportunitiesBy integrating the data process in your value chain, your business can:•  Validate market assumptions on real metrics•  React to new trends and detect burgeoning ones•  Roll out new product features based on predicted

customer behaviour •  Accurately measure the individual performance of

products•  Provide safer and cheaper services with greater

customer value

III. Implementation - Opportunities

… with that in place, the sky‘s the limit for your business.

Thank you!

ContactHivemind Technologies AG

Sechtemer Str. 550968 Köln

Tel. +49 221 29218 400www.hivemindtechnologies.com