AI News, Enabling the AI-Driven Enterprise
- On Thursday, June 7, 2018
- By Read More
Enabling the AI-Driven Enterprise
So our dedicated Customer Facing Data Scientists (CFDS) and our DataRobot University courses teach business executives how to meld their domain expertise with machine learning fundamentals to choose good projects that will have real business impact.
You'll leave this seminar able to identify business challenges well suited for machine learning, with fully defined predictive analytics projects your team can implement now to improve operational results.
Why Most AI Projects Fail
He has worked for Intel/Micron, a hedge fund as a quant, and recently helped build out HireVue's (a Sequoia company) data science team and AI product.
Ben has filed eight patents ranging from machine learning to nanotechnology, and appreciates the differences between IP and trade secrets.
Bottom-tier innovation verticals like HR, multi-level marketing, entertainment, fashion, medical, supply chain (anyone else we should throw under the bus?) are even starting to talk about it.
Fortune 1500 companies are throwing out multi-million dollar data leadership positions to lead their data teams to success.
Instead of focusing on such vague projects, the question we ask to cut through the crap is: 'Which projects have the largest impact on your revenue and KPIs?'
You can't afford for your first AI project to be a failure, or that could set you back significantly behind your competition.It would be better for you to postpone jumping into AI than to fail on your first attempt.
The majority of data scientists can't speak dollars and cents.For being amazing data geniuses, they don't know much when it comes to running a business.
Your job isnotto educate your executives on data science methods or complicated jargon.They are literally drowning with priorities focused on keeping the company growing, hitting revenue quarter goals, appeasing investors, and making sure you have a damn job.
Following lean startup principles, what is the least amount of time or effort you can spend to find out if you are going to fail on a particular project?
I was involved with a fantastic AI project using deep siamese nets only to find out two months later the customer wasn't willing to pay for it.
I have seen companies hire new college grads for compensation discounts or out of necessity ('Nobody good wants to work for us, sorry join the club!').
So you are telling me you have internal data science talent who have been sheltered from the upside-down world that exists outside of your organization's bubble?
If they haven't mixed with the surrounding data science community/job market [data battlefield] how are you ensuring you are using the latest and greatest?
He will also be in Mountain View, CA November 1-4 and in LA November 19-21, and would be happy to connect over data science or machine learning topics -- feel free to reach out to schedule a conversation!
Advance your career with machine learning
A Business Analyst's Journey into Machine Learning through Kaggle
Instead, I stand on the shoulders of giants — that is, the data scientists and engineers who built the DataRobot machine learning platform.
As Product Manager and Business Analyst at DataRobot, I operate at the nexus of three areas: working with users (my personal passion), working with builders (typically engineers), and working with business interests (C-suite executives).
My challenge — what will help the product team at DataRobot understand and relate to a typical DataRobot end user’s experience?
(I’ll explain the oops in the lessons learned, below.) I then create a blend/ensemble model using my top model (XGBoost) along with the top two TensorFlow models.
The business problem this competition is trying to solve is to help Red Hat identify which customers have the most potential business value for Red Hat’s business, based on their characteristics and activities.
Instead of following DataRobot’s fundamental flow of testing model results starting with a small sample size and then iteratively running the better performing models at a higher sample size percentage, I went all in and ran these at a 100% sample size.
Because I ran this model using all the data from the initial dataset in one big bang, I wasn’t able to test the model against data that the model hadn’t seen before.
Third, I didn’t take advantage of another of DataRobot’s great strengths -- the tools to visually identify important features I could have used to build better models and, as a result, provide better business insights.
DataRobot has some neat visualizations which helped the business analyst in me understand the data I was working with while the fiery competitor in me could, in the same tool, build and tweak models.
For example, a really simple and interesting visualization I used showed the distribution of data points across the days of the week (0 is Sunday, 6 is Saturday).
It automatically tested hundreds of predictive models, found the optimal one for my Kaggle data, and vaulted me, in a matter of hours, to the top 2% of a renowned data science competition.
I started this exercise to better understand my target end user and came away feeling really good about making space for you up on the shoulders of giants.
Machine Learning Life Cycle
Here is a step-by-step example of how a hospital might use machine learning to improve both patient outcomes and ROI: The machine learning life cycle is important because it delineates the role of every person in a company in data science initiatives, ranging from business to engineering.
It makes data exploration and model building much easier and more accessible, allowing those who understand the business problem behind the data science project to rapidly build and test dozens of models in a fraction of the time it would take using traditional methods.
Additionally, DataRobot includes built-in tools, including its unique Prediction Explanations feature, that increase model interpretability and make it easier to communicate model insights, making it easier to communicate the value of machine learning to users throughout your organization.
- On Wednesday, January 16, 2019
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Machine Learning Demo-1
Machine Learning Demo-1.