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How Machine Learning as a Service improves organizational productivity and reduces costs

By Tessa Jones, Calligo’s VP of Data Science, Research & Development & Peter Matson, Calligo’s Data Science Practice Lead.

More than tools & tech

85% of Machine Learning (ML) projects fail. This stark reminder from Gartner – despite more tools being available to businesses than ever. The thing is ML success is not just about tools and technology; it’s about how they’re put into production by experts. Plural. Machine Learning – that improves productivity and profitability by finding valuable insights buried deep in your company databases – needs a small army to leverage it. An entire MLOps collective – from platform engineers to software developers, business translators. And yes, Data Scientists.

The reason your ML endeavors are failing (or not thriving) and carry the risk of costing your business eye watering amounts is because one or two Data Science experts are not enough. You need a minimum of six unique skill sets.

ML overwhelm

If you’re reading this, you may be interested in building Machine Learning solutions, because you know they can reap huge rewards for your scaling business. But it can feel overwhelming – because you also know that ML is easy to get wrong, and can drain your budget. It’s a long-term commitment. And now we’ve told you will need a small army of capabilities.

But there is a new way – Machine Learning as a Service where you can hire an entire end-to-end Data Science team with the required technology, rather than recruiting one or two permanent positions and on-boarding new and expensive tech. Buying ‘people as a service’ essentially. Faster outcomes, decreased costs, greater value. More on this in a moment…

Machine Learning tools: offerings and shortcomings

There are more Machine Learning tools on the market than ever before. Suppliers like Google’s AutoML, Microsoft’s Azure, DataRobot, Dataiku and others have positioned themselves as off-the-shelf/‘out of the box’ solutions for all your Machine Learning needs. 

These kinds of solutions offer quick ways to gather insights from data. They can be excellent tools for building prototypes and frameworks to support deployment and ongoing management of models. They can also fully automate the model building process – which means organizations can have their hands on a deployable model within days. 

However…what is an optimal ML target? Is it statistically sound? How much compute do we need?’

Back in the box

Out of the box solutions can’t give you the answers. There’s no end-to-end service. And by that we mean, none of them allows you to buy ‘people as a service’. You load your data into their system, a model is built automatically and you’ll get access to the outputs. But you still need to do a lot to get those results  – build and maintain the model, integrate it into the business, keep it productionized and in a state that provides continuous value. These tools also can’t support your data security, privacy, or governance needs.

Cue the first Machine Learning as a Service of its kind

Now it’s time to introduce the six (people) roles and responsibilities you need to develop, deploy and monitor Machine Learning – to pave the way for long-term success:

1.      Business Translator: helps explain how to frame the business problem into an ML problem.

2.      Data Scientist: analyzes and processes the data, builds, and tests and monitors ML models.

3.      Data Engineer: manages how the data is collected, processed, and stored.

4.      Platform Engineer: builds and maintains specialized tools and infrastructure, integration with existing processes.

5.      ML Architect: develops blueprints and orchestration processes and identifies risks.

6.      Software Developer: builds and maintains robust and relatable interfaces for end users.

And then there’s a non-human no.7: the heavy-lifting technology. A full end-to-end tech stack to facilitate automation, optimize deployment – with ongoing monitoring.

Not ‘Jacks of all trades…’

These are all Masters of their own expertise. They are very purposefully not ‘Jacks of all trades’.  Rather, deep, unique skill sets that together form integral parts of the ML chain. Collectively this brings extraordinary business value.

And here’s the differentiator: rather than recruiting one, two, three Machine Learning experts – who can never possess all the skills listed above – you can hire six, for a fraction of the price. Like a timeshare apartment, you secure the property you want, for when you want it – without paying all year round for the luxury.

Minimal IT, no Data Scientists or ML technology

Let’s put Machine Learning as a Service (MLaaS) into an (anonymous) Calligo client context. An insurance provider uses ecommerce services to generate its customer base. And it allows end users to search for quoting estimates across many different providers. Initial quotes are generated based on the end user’s risk parameters – but the final pricing always needs to be dynamic and adaptable relative to their competitors.

The insurance provider had a small IT team and no Data Scientists. It had attempted using out of the box solutions that promised valuable price optimization modeling. But the output was too binary and did not recommend price changes. Put simply, it didn’t work. Cue Calligo’s MLaaS – that introduced a production ML model that continuously optimizes pricing, with minimal human requirements. Our client now has confidence in its pricing strategy and can see the end user characteristics that most impact price adjustments. The increased revenue potential was $3.1 million a year, generating great profit for the insurance specialists by targeting the right customers with the right price.

De-risking Machine Learning for businesses

Not only that, but its Machine Learning has now been de-risked. Making ML useful is a really hard task. There is a high risk of failure and if things go wrong, it’s expensive. Really expensive.

Machine Learning as a Service is the first of its kind because it simultaneously overcomes these six obstacles to adoption:

·      Cost

·      Usability

·      Technology

·      Security

·      Data privacy

·      Applied expertise

Human brains & smart machines

The blend of the human factor and machine is critical in delivering excellent Machine Learning solutions for your business. You no longer have to build your own (limited/ expensive) infrastructure or rely on off-the-shelf solutions that lack human input and monitoring.

As a reminder, Machine Learning:

-It is difficult to make useful.

-Carries a high risk of failure.

-Hugely costly if things go wrong.

Machine Learning as a Service is therefore the future. An extension of your team, your very own small army of ML experts – rented, not permanently recruited, deployed for the time you need to get your ML arsenal fired up and reeling in value and profitability. Learn more about our Machine Learning as a Service here, or get in touch to chat about how our ML experts can best support your business goals.