![]() You can simplify the process somewhat by using Amazon Sagemaker’s deployment functionality or an automated machine learning tool which has built-in deployment functionality. It is often a challenge to integrate your algorithm into existing systems and business processes. You start achieving business outcomes only after implementation though this is often the hardest step. H2O’s Driverless AI is a great option which I have personally tried. Moreover, their simplicity provides a form of risk management to your company by ‘idiot-proofing’ the process. ![]() These tools can speed up your development time significantly. If you want to see results fast I suggest looking into automated machine learning software. There is a lot of information on the web to help you with this step. The machine learning algorithms you will choose to use will depend on your specific objective. My personal favorites are Amazon Web Services (AWS) for its broad offerings and Microsoft Azure for its simplicity. Cloud technology allows you to experiment and scale quickly and efficiently. If you are not subject to regulatory limitations, I suggest making use of cloud technology. A simple pipeline with very little specialized software is a practical approach to dealing with the huge pile of technical debt that many organizations face. The tech should be kept as simple as possible. Only after you have your champion should you hire engineers. Your first hire will serve as the champion of the project and will be responsible for its outcomes. While some understanding of machine learning is needed, your first hire does not need a Ph.D. The business person will provide context to your problem and ensure that whatever you build delivers value to the business. Your very first hire should be a business person who will run the project. You are definitely going to need technically talented engineers to build it but they should not be your first hires. You will need a team with diverse technical and business skills. Let me start by identifying the key components of a successful machine learning project and then I will briefly describe each step in more detail.Ī machine learning project can be complex and daunting. I am hoping to give you a brief ‘how-to’ guide to getting the most value out of your project. You are now probably considering how you can bring your idea to reality. A high-level overview of managing a machine learning project in your company.Īlright, so you have identified a problem where machine learning is the appropriate solution.
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