Updated: Mar 26, 2019
1. Screen out your ideas
As a Director of Analytics you have constantly come up with new innovative ideas to solve business problems. You may have a budget for the next quarter and need to decide how to best allocate the given resources. By definition, new initiatives mean something you and your team may have not tackled before, so naturally there will be some risk associated with each initiative.
Our team of research scientists and engineers have exposure in a long range of projects and domains. We strive to stay at the forefront of Machine Learning and AI through rigorous, continuous research and training. We are also exposed to different business challenges from a variety of industries such as Health Care, Finance, Hedge Funds, and Insurance.
It is this continuous exposure of projects and research we bring to the table when screening out your ideas for optimal value while minimizing risk.
2. Data Exploratory Analysis (EDA)
Once you have decided on an initiatives to pursue, you start looking into the data set and are unsure what to make of it. The data is a mess, highly unstructured, and all over the place. We suggest to all of our clients to start with simple exploratory techniques before diving into more complicated model-building. Getting to know the data can give you actionable insights.
We always start new projects with EDA asking serious of basic questions such as, summarizing the data looking by into most common/least common features, creating categories, diving into categories. After the exploratory, putting together a summary with Executives in mind is a good idea: ask yourself, what should an Executive with a stake in the project see? What are top three-five graphs which nicely summaries the data?
3. Prototyping AI-products
Sometimes you can theorize about the benefits of a new potential product indefinitely. While it is important to paint the picture to key stakeholders about the benefits if such new shiny tool, there is no replacement for actually showing them (show don't tell). You should show off the tools as Proof-Of-Concept (POC) as soon as possible before spending big bucks in such a project. Bringing to life Minimum Viable Products quickly not only reduces the risk of failure of new projects but also brings in the whole team to the conversation. They can now take the product for a test ride. They can suggest feature importance and direct the product development based on value.
Byteflows specializes in building data-driven prototypes quickly. During your first month, we will provide resources to build a POC accompanied by an AI diagnostic report. Based on the results and findings from the first month, we will discuss priorities on feature implementation. Eventually, the goal is to help you and your team take over parts of the project, deploy the model to adjust to real time and we will move more into an advisory role.
Summary of key benefits
Build prototypes quickly to test new ideas and clearly evaluate the benefits.
Receive pragmatic guidance on timelines and approaches to achieve your goals within a budget range.
Get advice on internal team-building, when the project has been well defined, and ideally, a prototype or pilot has been built and tested.
Based on insights gathered deploy capital and resources into practical and realizable tasks.
Access to a versatile team which possesses the following pool of skills: + Machine Learning, Deep Learning (on GPU) + Data Architecture, Cloud Computing + Data Engineering + Computer Vision + Data Visualization + Text Analytics, NLP, Data Mining
A team with experience in full AI Product Development