Is Your Machine Learning Project Struggling to Create the Insights You Need?
Expectations Exceed Reality
The current hype in AI leads many projects to set an unreasonable target or unclear success criteria leading them to over promise and under deliver.
Machine learning appears accessible because of the availability of many open source toolkits. But once you get started, it can be difficult to decipher why a particular model isn’t working, leading to wasted time on unsuccessful approaches.
The challenge is that there is no one-size-fits-all solution. The best approach often takes a lot of experimentation, and the most efficient path is dependent on understanding your data.
Applications in remote sensing are often challenged by the lack of detailed annotations and images from multiple satellites. Accommodating all these complexities into a single solution requires advanced research techniques.
There should be an easier path to driving impact with machine learning.
Ensure the success of your project with an experienced machine learning guide at your side.
Get the Most Out of Your Images
- Define clear goals and metrics of success
- Properly preprocess data for effective model training
- Iterate quickly to build momentum
- Get from a sufficient to an ideal solution to maximize impact
Ensure the Success of Your Project with a Guide
Like you, I care about driving impact. I can help you navigate this confusing AI journey. With 15 years of machine learning experience, I’ve seen models that fail for a particular task and those that succeed. I have many tools in my toolbox and the research experience to create novel algorithms for unique situations.
My specialty is problems for which there is no existing packaged solution. I make use of today's most powerful machine learning tools - TensorFlow, Keras, PyTorch, sklearn, and others - to help you create a new solution based on images and any other available data.
I have designed algorithms to estimate greenhouse gas emissions from sources on Earth and find rocks and craters on Mars. Together, we can generate new insights from your project too.
Heather D. Couture
Consultant & Researcher
Join weekly team meetings or planning sessions to provide feedback, suggestions, and links to helpful resources.
Take a deeper dive into a particular aspect of the project to guide a specific need or address problems that arise.
Lead the machine learning development for your team. I only provide this exclusive level of support to one client at a time.
Proof of concepts
Subject matter expert
The Power of Machine Learning
- Detect and count objects
- Identify anomalies
- Classify shapes and textures
- Find regions of interest in a
large image for closer review by expert
- Iteratively refine segmentation of an object
- Distinguish classes too complex for human experts
- Integrate other forms of data
Don’t Outsource, Insource - and Empower Your Team
While machine learning certainly can bring new insights, precision, and efficiency, it takes time to build the technology to do it. And the path to a successful solution is generally not linear.
Many companies outsource machine learning projects. While this can be successful for well-defined applications with a limited scope, high impact projects to fight climate change are different. These projects are often part of a company’s core technology, so they prefer to keep the intellectual property development in house. But they may not have navigated the unique complexities of a machine learning project before. I can help you build the technology and your team in house, ensuring a smoother path to success.
My goal is not just to get you to an ideal solution but to ensure your team also understands how it works so that they can modify it and adapt to new challenges that arise. I will be there to help you navigate these obstacles as needed, but I consider my role most successful if I have transferred this knowledge to your team.
Build an Interdisciplinary Team
I’ve worked with teams spanning many different disciplines and the best results tend to come from a combination of
- Remote sensing scientists and other domain experts who understand the data and how it is collected,
- Data scientists who gather and analyze it further,
- Machine learning engineers who train and analyze models, and
- Software engineers who bring all the pieces together and create a robust system.
Due to the interdisciplinary nature of teams, communication is key.
I can help you in building this team. We start by setting reasonable goals for the project and determine the resources needed. From initial models, we then assess possible next steps including gathering and labeling more data, cleaning data, extending models, and improving computational efficiency. More resources (human or computer) may be needed as the project progresses.
Organizations I've Worked With
The Challenges of Real World Remote Sensing Data
The process for machine learning is empirical and iterative - hypothesize a model, test it, and improve it.
While many talented machine learning engineers can create and train a model, they may be inexperienced with the challenges posed by real world data. The complexities of multi-band images and noisy or missing labels are a whole different ball game than clean benchmark data sets.
Machine learning engineers may also lack the experience to identify unique aspects of data that, with a customized model, can improve predictions.
I have studied rocks and craters on Mars and greenhouse gas emissions on Earth. I have created new multi-band and multi-modal models to address novel challenges.
The same may be beneficial for your project, but you won’t know it without looking from the right perspective.
Is Deep Learning the Best Approach?
Deep learning is a game changer for many applications. The power of deep learning comes from its ability to find patterns in complex data - even patterns beyond the limits of human perception. It is a new way to gain insights from data. Similar to how we learn from experience, deep learning performs a task repeatedly, tweaking how it does it each time to improve the outcome.
But there are also many situations - like limited training data or interpretability requirements - in which more traditional machine learning might be best. I am well-versed in both methodologies and will help you strategize as your project, your data, and your goals evolve.