AI News, Choosing the correct ML Solution for you...

Choosing the correct ML Solution for you...

               Enterprise applications trending to adopt Machine Learning as their strategic implementation and performing machine learning deep analytics across multiple problem statements is becoming a common trend.

                 Machine learning packaged solutions like RStudio, H20, Anaconda, Turi are trying to improve in the space of accessing and storing data on distributed storage and trying to add capabilities for distributed multi thread / core /node execution on time consuming tasks like data preparation, feature extraction  and model creation.

A brief overview of Automatic Machine Learning solutions (AutoML)

In the process presented above, several blocks must be tuned to extract most of the predictive power of the data: First of all, you have to select relevant data that potentially explain the target you want to predict.

non-exhausive list of preprocessing steps is given below: Last but not least, you have to choose a machine learning algorithm depending on the kind of task you are facing: supervised or not, classification or regression, online or batch learning… Most machine learning algorithms need parameterization and even if some empirical strategies can help (this article provides guidelines for XGBoost), this optimization is complex and there is generally no deterministic way to find the optimal solution.

Indeed, we generally want to specify stopwords, truncate the document frequency range (i.e.remove from the dictionnary rare and/or frequent words), or even include n-grams (sequences of n consecutive words) into our dictionnary.

For this kind of batch supervised binary classification task, we are spoilt for choice: logistic regression, naive Bayes classifier, random forest, gradient boosting, neural nets… We probably want to try and compare some of these algorithms but here let say that we have chosen the random forest.

It may sound a bit abstract but let’s just say that each element A ∈ 𝚨 is a parameterization of the machine learning pipeline from the data processing to the model hyperparameters tuning.

The auto ML process aims at finding A* such that: where and are train and validation sets obtained via K-fold cross validation and is the loss for pipeline A trained on training dataset i and evaluated on validation dataset i.

Among the tons of metaheuristics, we can mention for instance: Each evaluation of the objective function we try to minimize can be very expensive as we have to train and evaluate a machine learning pipeline.

The figure 3 below (source) gives a simple example in one dimension (in this case, we want to maximize the function): In this example, the objective function f is approximated through a Gaussian Process regression model.

Building machine learning solutions that can withstand adversarial attacks

Explore the role of data and algorithms in improving security at the Platform Security and Cybersecurity sessions at Strata + Hadoop World San Jose, March 13-16, 2017.

Along the way, he has assembled and managed teams of data scientists and has had to grapple with issues like explainability and interpretability, ethics, insufficient amount of labeled data, and adversaries who target machine learning models.

As more companies deploy machine learning models into products, it’s important to remember there are many other factors that come into play aside from raw performance metrics.

I was interested in creating systems that can bootstrap from the small amount of ground truth that you provide and slowly build up a much more robust underlying machine learning system that can achieve the goal you are trying to accomplish.

For example, if you are trying to do a classification problem and you started off with a little bit of data that's ground truth, combine the ground truth that you gave with unlabeled data along with some person being in the loop to create a system that can scale over time and over data.

When I said minimally supervised, I meant that there is a person in the loop and having this person in the loop allows you to divide up the decisions into what the algorithm has to decide and what the person has to decide in such a way that you don't need a lot of ground truth.

I'm starting to be really interested in the idea of, how to build systems that are interpretable, that are explainable where you can have faith in the outcome of the system by inspecting something about the system that allows you to say, 'Hey, this was actually trustworthy result.' Parvez Ahammad will speak on recent Applications of machine learning to security at Strata + Hadoop World San Jose, March 13-16, 2017.

How to Apply Machine Learning to Business Problems

It’s easy to see the massive rise in popularity for venture investment, conferences, and business-related queries for “machine learning”

In this article, we’ll break down categories of business problems that are commonly handled by ML, and we’ll also provide actionable advice to begin a ML initiative with the right approach and perspective (even it’s the first such project you’ve undertaken at your company).

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:

If you have reams of business data from years ago, it may have no relevance today, particularly in fields where the basic business processes change drastically year-over-year, such as mobile eCommerce).

For example, if you run a door delivery service for pet supplies, and your app, prices, product offerings, and service areas have changed significantly over the last six months, you will need much more recent data to learn from than, say, a company selling homeowners’

While unsupervised learning (see glossary below) allows for a wide degree of applications in making sense of data without labels, it’s usually not advised for companies to “jump into”

One letter difference or one number difference could mean overpaying your bill by 10x the original amount (if the decimal was interpreted to be in the wrong place), or sending money to the wrong company (if an invoicing company name isn’t registered exactly).

As an interesting caveat, there is a San Francisco-based startup called which is aiming to use natural language processing and machine vision to real and pay bills, albeit it pulls humans into the loop before sending funds.

In order to gain additional perspective on the issue of “picking a business problem for machine learning”, we decided to reach out to our network of previous AI podcast interview guests for additional guidance for our business readers: Dr. Ben Waber —

CEO, Calculation Consulting: “The best problems are those in which there is a very large, historical data set that includes both rich features and some kind of direct feedback that can be used to build and algorithm that can be implemented and tested easily and will either decrease operational costs and /or increase revenue immediately.“

CEO, AGI Innovations Inc: (To begin, Peter quotes Dr. Robin Hanson, Professor at George Mason University: “Good CS expert says: Most firms that think they want advanced AI/ML really just need linear regression on cleaned-up data.”) “I think that most businesses don’t justify the investment in ML/DL (of course, ML means many things). Cutting edge stuff that everyone is talking about requires a lot of data and expertise, and is static – i.e.

This leads us into the second major section of this guide: In an off-mic conversation with Dr. Charles Martin (AI consultant in the Bay Area), he mentioned that many companies read about ML with enthusiasm and decide to “find some way to use it.”

Pick a business problem that matters immensely, and seems to have a high likelihood of being solved UBER’s Danny Lange has mentioned from stage that there is one thought process that’s highly likely to yield fruitful machine learning use case ideas: “If we only knew ____.”

could get in the way of developing an effective ML solution: Building a ML solution requires careful thinking and testing in selecting algorithms, selecting data, cleaning data, and testing in a live environment.

Data Science and BD&A, Computer Sciences Corporation: “The most common mistake that businesses make when using ML is that they think that an ML solution is a one-shot process: They send data to data scientists, and data scientists send back THE model.

Log everything, build storage and processing systems, ensure they are accessible, conduct deep analysis and as many experiments as you can on your product, build in intelligence into as much as your product as possible.

The consensus (in the limited number of quotes above, and from dozens of other conversations with business-minded data scientists) is that machine learning is not as much of a mere “tool”

Interested readers might benefit from our recent consensus of 26 machine learning / AI researchers where we asked: “Where should machine learning be applied first in business?” The infographic featured drives home many of the same points highlighted in this article.

The ultimate question for executives remains: When can we have (a) the resources required to invest in machine learning seriously, and (b) a legitimate use case that started from trying to find real business value, not from “trying to find a way to kinda use machine learning.”

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