Strategy & Tactics

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  1. Think backwards
  2. Build a data pipeline before a model
  3. Deliver actions over accuracy
  4. Modularise and abstract
  5. Brand the solution

Artificial Intelligence is great for complex problems where traditional approaches are difficult or impossible to implement such as long list of rules would be required or when the conditions are always changing.

Application Kaggle Competition
Translate a business question into a data question  
Think about how the model is going to be consumed  
Validate assumptions and methods  
Consider how a machine learning model can connect to a (existing) tech stack  
Determine data sources  
Extract Data; SQL queries (sometimes across multiple databases), third-party systems, web scraping, API’s or data from partners Download some data (probably one or several CSV files)
Transforming and cleaning data; removing erroneous data, outliers and handling missing values Perhaps do a little cleaning, or chances are the data set may already be clean enough
Exploratory data analysis & feature extraction  
Feature engineering (vast range of variables) Feature engineering (a finite number of variables)
Perform preprocessing such as converting categorical data into numerical data Perform preprocessing such as converting categorical data into numerical data
Version models, Feature selection, Hyperparameter Tuning, and Web Service Endpoints  
Model selection; best model that can be integrated into the existing tech stack with the least amount of engineering Model selection
Build the model Build the model
Set up training and deployment pipelines  
Train the model Train the model
Validate the model Validate the model
Test the model Test the model
Optimise the model until it is ‘good enough’ considering the business value; perform hyperparameter tuning and compare results Run the data through a variety of suitable models until you find the best one; perform hyperparameter tuning and compare results
Make experiments reproducible  
If new tech stack…  
- Review prior art and available libraries, services, scafolding tools  
- Implement and test tech stack  
Deploy the model  
Monitor the model performance in production  
Set up monitoring alert systems  
Retrain when/if necessary  
Mature to automatically retrain and redeploy models  

Why do you need an AI Framework and an AI Strategy?
Raj Ramesh The terms framework and strategy are often confusing. If your organization is ready to use AI in its digital transformation (and it better be because the future of the digital organization will have lots of AI), then it needs a great strategy. Here is discuss how you can think about AI strategy and AI frameworks.

So you want AI for your business. Where do you start?
Raj Ramesh AI is an important technology that will fundamentally shift how your business operates. But many companies are scrambling with AI without a focus. What’s a good way to think about it?

The 7 steps of machine learning
How can we tell if a drink is beer or wine? Machine learning, of course! In this episode of Cloud AI Adventures, Yufeng walks through the 7 steps involved in applied machine learning. The 7 Steps of Machine Learning article:

Nuts and Bolts of Applying Deep Learning (Andrew Ng)
The talks at the Deep Learning School on September 24/25, 2016 were amazing. I clipped out individual talks from the full live streams and provided links to each below in case that's useful for people who want to watch specific talks several times (like I do).

Strategies for Implementing AI and Machine Learning Techniques
Speaker: Daniel Protz, Founder and CEO, FlavorWiki. How can traditional F&B companies adopt new approaches while maintaining organisational support, comparability of data and continuity of product understanding? In the session Daniel outlined three change management approaches to stimulate teams, win organisational buy-in and secure budget for non-traditional tools using Big Data, Machine Learning and AI. What you will learn about: Use AI and Machine Learning to enhance existing systems and practices, Gain support for the use of such methods within their teams, Explain to management how investment in emerging tools can drive project ROI and increase competitiveness. Organised by IFST's Sensory Science Special Interest Group.

Your choice of Deep Net - Ep. 4 (Deep Learning SIMPLIFIED)
Deep Nets come in a large variety of structures and sizes, so how do you decide which kind to use? The answer depends on whether you are classifying objects or extracting features. Let’s take a look at your choices. Deep Learning TV on Facebook:

Lessons learned from 100 deep learning models
Summary of tips for getting higher accuracy on your CNN deep nets.

CS231n Lecture 12 - Deep Learning libraries
Overview of Caffe/Torch/Theano/TensorFlow

What is a Deep Learning Library? - Ep. 16 (Deep Learning SIMPLIFIED)
Deep Learning libraries provide pre-written, professional-quality code that you can use for your own projects. Given the complexity of deep net applications, reusing code is a wise choice for a developer. A library is a set of functions and modules that you can call through your own programs. Library code is typically created by highly-qualified software teams, and many libraries bring together large communities that support and extend the codebase. If you’re a developer, you’ve almost certainly used a library at one point in time. For a commercial-grade deep learning application, the best libraries are deeplearning4j, Torch, and Caffe. The library Theano is suited for educational, research, and scientific projects. Other available libraries include Deepmat and Neon.

Comparing machine learning models in scikit-learn
We've learned how to train different machine learning models and make predictions, but how do we actually choose which model is "best"? We'll cover the train/test split process for model evaluation, which allows you to avoid "overfitting" by estimating how well a model is likely to perform on new data. We'll use that same process to locate optimal tuning parameters for a K-Nearest Neighbors (KNN) model, and then we'll re-train our model so that it's ready to make real predictions.

Approaching (almost) Any Machine Learning Problem | by Abhishek Thakur | Kaggle Days Dubai | Kaggle
Abhishek Thakur is the chief data scientist at building state-of-the-art chatbots primarily for banking and insurance industries. His passion lies in solving difficult world problems through data science. He is the co-organizer of the Berlin Machine Learning Meetup and not long ago was ranked no. 3 worldwide on the Data Science Platform Kaggle. He is a Competitions and Discussions Grandmaster. Abhishek did his Bachelors in Electronics Engineering from India and moved to Germany for pursuing MSc from University of Bonn, Germany with a focus on image processing and computer vision. He dropped out of PhD in 2015 and since then has been working in industries. Kaggle Days Dubai was held May 30 - April 1 2019 as a part of AI Everything. Participants came to meet, learn and code with Kaggle Grandmasters, and compete in a full-day offline competition. This edition is presented by LogicAI with sponsorship from Kaggle and Google Cloud. Kaggle Days are a global series of offline events for seasoned data scientists and Kagglers created by LogicAI and Kaggle.

Building Robust Machine Learning Models
Modern machine learning libraries make model building look deceptively easy. An unnecessary emphasis (admittedly, annoying to the speaker) on tools like R, Python, SparkML, and techniques like deep learning is prevalent. Relying on tools and techniques while ignoring the fundamentals is the wrong approach to model building. Real-world machine learning requires hard work, discipline and rigor. Development of robust models requires due diligence during data acquisition phase and an obsession with data quality. Feature engineering, choice of evaluation metrics and an understanding of the model bias/variance trade-off is often more important than the choice of tools. Experienced machine learning engineers spend most of their time dealing with data-related issues, model evaluation and parameter tuning while spending only a fraction of their time in actual model building. This is the 80/20 rule. Unlike most talks these days, this talk is not about deep learning. We will ignore the hype and strictly focus on fundamentals of building robust machine learning models.

Guide to choose right deep Learning framework for your AI project
Rishikesh As world rolling around Artificial Intelligence (AI), demand for the AI-based product seen exponential growth, so the AI research. Deep learning algorithms and techniques are widely used for research and development of these products. Good news is that year by year Deep Learning has seen its glory in the release of many open source frameworks which ease the pain to develop and implement these algorithms. As there are many deep learning frameworks out there and it can lead to confusion as to which one is better for your task. And choosing a deep learning framework for an AI project is as important as choosing a programming language to code product, Data science project coupled with the right deep learning framework has truly amplified the overall productivity. In this talk, I will discuss the common points which help developers to understand which framework will be the perfect fit for solving given business challenges. Also, we will look into some of the most widely used frameworks and comparing with standard benchmarks. Following deep learning/machine learning frameworks will be discussed: 1. Tensorflow, 2. PyTorch, 3. **Chainer** and/or **MXNET** Key Highlight of this talk: define key points to judge any deep learning framework. hardware dependencies. anatomy of widely used open source frameworks. comparison of above-mentioned frameworks as per defined key points. Who is the audience? Anyone who inspired to code deep learning algorithms. pyconza2018 python

How to Realize Your AI Strategy?
Raj Ramesh Knowing about AI is one thing, but integrating it into the business could be a lot more challenging because there are many factors to consider. Here I explore some of the major things to consider.


Business Case

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Artificial Intelligence: Building the Business Case for AI (CXOTalk #246)
Artificial intelligence can make companies dramatically more efficient, but investing in the technology can come with risks and complications. Tiger Tyagarajan, CEO of Genpact, tells Michael Krigsman of CXOTalk about the best strategies for buying AI to improve your business.

Building a Business Case for your Machine Learning Idea
This presentation will discuss building a business model for your machine learning idea. Our presenter, Neeti Gupta, will provide a 10-step checklist with examples for the audience to build their own business model.

Business Strategy/Consulting

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How to Apply AI in Business
Raj Ramesh AI is a cool technology, but how do you use it effectively in your business operations. Solving the AI problem is only a small part of the whole solution, and you have to consider other things as well.

In this video, I walk through an example of using an AI system to automate a parking lot business operations

How to Insert AI into business processes and what are the critical factors to consider?
Raj Ramesh Today many businesses are trying to transform to adapt to the new realities of the world. Future business models are often dependent on available technology. For example, the Uber business model is made possible because of smartphones. Technology will be a major component of those future state of any business. One of the main technologies is artificial intelligence (AI) and businesses that master AI will establish themselves in a leading and competitive position. Many studies have indicated that the faster the business integrates AI, the faster it will move forward. This means that if you are late to the game, it is almost impossible to catch up. So the question to ask is how do you establish that lead? Having gone through large scale multi-billion dollar transformations myself, and also being deeply familiar with AI, I offer some secrets on successful AI-based transformations.

Meet Sheldon, the Future of Consulting
By Emmanuel Jusserand, Partner at Accenture Digital Strategy

How AI is Changing Consulting
Harvard Business Review Facebook Live Presentation

AI for Business: How Should We Frame It?
Boston Consulting Group BCG Partner and Managing Director Philipp Gerbert presents on the benefits of AI to a group of senior leaders at MIT. Welcome to the official YouTube channel for The Boston Consulting Group. Since 1963, BCG’s experts have been helping businesses, governments, and non-profit organizations build lasting advantage. As a leading management consulting firm, BCG empowers clients to make the changes needed to seize the competitive advantage and win. The independent spirit handed down from Bruce Henderson, BCG's founder—always challenging the status quo—has given the firm the courage to look beyond the obvious in pursuit of lasting solutions for our clients. Subscribe to BCG’s YouTube channel: Visit us at

How do you develop an Artificial Intelligence Strategy for a business?
Bernard Marr In this video I explain in very simple terms how a business can develop an AI or machine learning. If you would like more information on this topic, please feel free to visit my website and sign up for content updates! I write articles every week on various different topics such as Big Data, Artificial Intelligence & Machine Learning.

The Future of Consulting
Recently did a Q&A jam session with CMC-Manitoba on the future of management consulting (one of my favourite topics). Like most industries, we are also being disrupted from all sides --- the proliferation of the gig economy, automation, start-ups, the commoditization of information, etc. One of the ways to remain competitive is to hire and train "next-generation consultants". Biography: Shawn Kanungo is a strategist and keynote speaker who operates at the intersection of creativity, business and technology. He has been recognized nationally and globally for his work in the innovation space after 12 years working at Deloitte. Shawn’s mandate at the firm was to help corporate executives to better understand and plan for the opportunities and threats associated with disruptive innovation. Now as General Partner of Queen & Rook Capital, he is focused on applying exponential technologies and new business models to mature businesses. Shawn is a practitioner who has worked hand-to-hand with hundreds of organizations on their journey to digital transformation. He has adopted the concepts of behavioural economics, user-centered design, crowdsourcing, artificial intelligence, drones and film to help create world class client experiences. His work and interviews have been featured in Forbes, The Globe & Mail, The Guardian, CBC and CTV. In 2016, he was recognized as Avenue Magazine's Top 40 Under 40. He has spoken at TEDx in 2017, and named to Inc's 100 Most Innovative Leadership Speakers in 2018. His podcast, The Remix with Shawn & Wang, can be currently found on iTunes, Google Play and Stitcher. Website:

How AI revolutionises business strategy
For centuries, the decisions made in a company were the responsibility of the top managers. But when firms harness AI and big data, algorithms can make many more decisions in the same time, and probably better ones. Kenneth Cukier explores how this affects the ways that companies are organized and how they compete and set strategy (as opposed to just execution) as he outlines what is happening now and what to expect in the future.