Involvement Of ML From Start to Scale


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For a better understanding of how to apply machine learning to business, let’s speak briefly about the term itself. Until recently, computers could be used for solving business problems only if explicit rules were written for them. Complex “if-else” instructions development took time and significant effort. It was critical in rapidly changing fields: the rules became outdated before the computer system was ready to use.

Today, machine learning avoids explicit programming allows the machine to learn rules from large datasets.

The heart of machine learning systems is the algorithm. Some of them are quite specific, while others, like decision trees and neural networks, are very general. Today’s gargantuan amounts of data – along with modern compute power – allows for next generation effectiveness of algorithms and brand-new areas of application for machine learning.

94% AIIA Survey November, 2019 of AIIA respondents are engaged with ML integration partners.


1. Not having the data

Enterprise big data isn’t big data at all. The vast oceans of unstructured data outside of the stand-alone enterprise offer the opportunity for unlocking true insight. Structuring the most valuable in-house data while finding the most valuable external data is expensive and time consuming.

2. Not having a standard

The combination of ML technology coupled with current compute is in its infancy. Integration partners are offering solutions that are unproven at scale. Plug & Play has become Plug, Play & Learn.

3. Not having the talent

Even when you do find an expensive data scientist, recruiting the best talent is a challenge when your competition in the advanced tech industry.

4. Not showing your work

Advanced machine learning confounds data scientists- mostly because the path to the output cannot be shown. This is a problem for any enterprise and acutely an issue for highly regulated industry.

ALSO READ: AI and Bots In Banking & Finance


Knowing number of AI engineers to employ is also an enormous challenge, as talent availability is a significant constraint across the globe. O’Reilly’s Data Science Salary Survey found that the average base salary of a global data scientist was $90,000. And those are front line engineers. That salary quickly rises to $500K – $1M, per the New York Times (Metz 4/19/19) once you add some actual work and strategy experience to the person in question.



Machine learning is not a magical solution that applies to every single use case. So often companies embark on an AI journey without a clear understanding of the value it should bring to their business. As a result, many data science and machine learning projects don’t have clear KPIs and simply drain R&D budgets.

Machine learning has certain limitations, and it currently doesn’t fit into every business case of every domain.

An enterprise executive interested in enhancing existing business workflows through machine learning, needs to thoroughly understand the actual capabilities of this technology. Executives hoping to narrow the technological gap must be able to address artificial intelligence in an informed way. In other words, they need to understand not just where AI can boost innovation, insight, and decision making; lead to revenue growth; and capture of efficiencies —but also where AI can’t yet provide value.


  • Classification models are used to break down large datasets into meaningful subsets. The most straightforward examples are image recognition and natural language processing.
  • Regression models identify trends to make predictions. Sales forecasts that take into account thousands of factors from macroeconomic indicators to weather forecasts to political threats.


For illustrative purposes, it will be helpful to list a number of well-established business use-cases for machine learning so that you (the reader) can churn up your own application ideas:

It’s incredibly difficult to write a set of “rules” to allow machines to detect faces (consider all the different skin colors, angles of view, hair / facial hair, etc.), but an algorithm can be trained to detect faces, like those used at Facebook. Many tools for facial detection and recognition are open source.

Some spam filtering can be done by rules (IE: by overtly blocking IP addresses known explicitly for spam), but much of the filtering is contextual based on the inbox content relevant for each specific user. Lots of email volume and lots of user’s marking “spam” (labelling the data) makes for a good, supervised learning problem.

  • Product / music / movie recommendation

Each person’s preferences are different, and preferences change over time. Companies like Amazon, Netflix and Spotify use ratings and engagement from a huge volume of items (products, songs, etc.) to predict what any given user might want to buy, watch, or listen to next.

There is no single combination of sounds to specifically signal human speech, and individual pronunciations differ widely – machine learning can identify patterns of speech and help to convert speech to text. Nuance Communications (maker of Dragon Dictation) is among the better-known speech recognition companies today.

Derived from “chat robot”, “chatbots” allow for highly engaging, conversational experiences, through voice and text, that can be customized and used on mobile devices, web browsers, and on popular chat platforms such as Facebook Messenger, or Slack. Chatbots can be built to respond to either voice or text in the language native to the user. You can embed customized chatbots in everyday workflows, to engage with your employee workforce or consumer engagements.

  • Real-time bidding (online advertising)

Facebook and Google could never write specific “rules” to determine which ads a given type of user is most likely to click on. Machine learnings helps to identify patterns in user behaviour and determine which individual advertisements are most likely to be relevant to which individual user.

  • Credit card purchase fraud detection

ALSO READ: 2022 Report On Unstructured Data

Like email spam filters, only a small portion of fraud detection can be done using concrete rules. New fraud methods are constantly being used, and systems must adapt to detect these patterns in real time, coaxing out the common signals associated with fraud.


Research institutions and tech companies have made massive progress in certain areas of machine learning, including computer vision, speech recognition, and natural language processing. Still, this technology is not a silver bullet. For now, though, most of the news is coming from the suppliers of ML technologies. And many new uses are only in the experimental phase. Few products are on the market or are likely to arrive there soon to drive immediate and widespread adoption. As a result, analysts remain divided as to the potential of ML: some have formed a rosy consensus about ML’s potential while others remain cautious about its true economic benefit. This lack of agreement is visible in the large variance of current market forecasts, which range from the hundreds of millions to couples of billions. Given the size of investment being poured into ML, acting quickly is important but preparing to act is paramount.

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