How to Start a Machine Learning Project in a Company How to Start a Machine Learning Project in a Company
Leandro is a speaker for ODSC East 2020. Be sure to check out his talk, “How to Apply Machine Learning in... How to Start a Machine Learning Project in a Company

Leandro is a speaker for ODSC East 2020. Be sure to check out his talk, “How to Apply Machine Learning in Your Company Using Design Thinking and Canvas,” there! In this talk he discusses how to start a machine learning project in a company.

We are living in an unpredictable moment in human history, being able to teach computers to “think” and to make decisions. It was something only in sci-fi a short time ago. Today it is possible to do it with just a few clicks and is almost totally free.

The reason for this is the increase of internet connection speed and the split between processing and storage costs, also known as Cloud Computing.

According to Gartner, 38% of companies in 2025 will be driven by algorithms to make business decisions, built based on historical data generating a huge differential between their players.

Well-known companies such as Amazon, Google, Airbnb, Netflix, and Tesla are examples of full utilization of machine learning in their business. Going back in time, as electricity did replacing vapor machines.

As soon as you are convinced that machine learning is not a buzzword anymore, you may ask me: How can I start a successful project in my company to take advantage of all this potential without wasting money, focused on generating more value for my business?

This is not an easy task, but there are a few steps to follow that will greatly reduce the probability of wasting money and losing your investment, or sometimes, identifying  early that it is not the right moment to start, given for you what you have to do before to be prepared to enjoy the wave

The first step is to understand what machine learning is, where you can apply it, and what benefits to expect in return. You do not need to learn a new programming language or remember your calculus classes. Knowing what is possible or not is enough for a while. Prediction, Forecasting, Classification, Categorization, Time Series, Video, Voice and Text recognition are some possible examples of how to apply ML to your business.

Another important step is to identify where to apply it in your business. Which areas will be the best to apply machine learning models? Common departments are marketing, customer services, sales, etc. For example, in customer service, many businesses start with customer clustering or next best action recommendation.

Another aspect is to identify if there is enough data to be the raw material to learn. If we can teach computers to “think” and to suggest actions, it is because we have enough historical data to learn, which is a good starting point.

I have been working on projects in USA and Brazil where customers have the clear idea of their business needs and how to apply machine learning models, but they don’t have enough quality data to build good models at that time. Either they are starting a new operation or they don’t have qualified data at that moment, meaning they need to complete “homework” before (cleaning, data quality, transforming, etc) implementation.

Once you have historical data as raw material, it is time to discover which business metrics will be used to measure the model’s effectiveness. Profit increase, cost reduction, and customer satisfaction are some examples to follow to identify the return of investment on the machine learning initiative.

A lot of people ask me if it is necessary to build a data lake or buy external information to start a machine learning project. In general, I have answered that is not necessary because it is possible to find specific needs like customer clustering or product recommendation, using your existing data exported from your CRM system, you can create a very effective model, able to give you a quick return on your investment.

Now, how can this be done in a short time and how can you identify if your investment in a machine learning project is feasible? I invite you to attend my speech at ODSC this April where I will share my experience in machine learning projects and I will show you a proven method based on Design Sprint and Machine Learning Canvas to start to use immediately in your company, reducing risks, discovering bottlenecks and predicting your return on investment in a practical way, always focusing on to generating value for your business.

Leandro C. Lopes

Leandro Lopes has helped companies like Roche, Ambev, Rabobank in Brazil and USA identify where and how to apply Machine Learning effectively by applying the L3 – Learn, Lean, Lead – Design Thinking and Machine Learning Canvas methodology

Postgraduate in Economics from the Fundação Getúlio Vargas (FGV) and Distributed Systems from the Federal University of Rio de Janeiro (COPPE-UFRJ). He is director of innovation at L3, speaker, and researcher on the impact of artificial intelligence on human relations.

In the last 10 years, it has won several international and national awards for the results achieved.



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