What are the most effective ways to manage AI software projects?

When managing AI software projects, start by defining the problem you aim to solve, envisioning it like a puzzle that needs solving. Clearly outline what success looks like, just as you'd set a goal to complete the puzzle within a certain timeframe. Break down the problem into manageable pieces, like separating puzzle pieces into groups, and determine how the AI can help solve each part. Establish criteria for success – it's akin to knowing when the puzzle is complete and fits together perfectly. By approaching your AI project as a puzzle with a clear picture of success, you'll guide your team in effectively managing and achieving the desired outcomes.

My approach involves identifying if the challenge lies in ML/DL "training" or "inference," which dictates platform and monitoring needs. Creating a detailed system diagram that outlines data flow, storage, and compute requirements, while including third-party integrations and team collaborations, is essential. Defining KPIs like accuracy, FPS, and visual impact in computer vision projects helps measure success. I use collaborative tools like Google Docs or Confluence for stakeholder engagement, enabling continuous feedback and enhanced understanding.

1 Identify a problem 2. Testing the Problem Solution Fit .What is minimum MVP 3. Preparing and Managing Data We start by dividing the available data in structured and unstructured forms. 4. Choosing the Right Algorithm Although, to keep the essence of the article, we won’t mention the technicalities of AI algorithms here, but what is important to know is that there are different types of algorithms, which vary on the basis of the learning 5. Training the Algorithms Once we have selected the algorithm we move on to training the model wherein we input data into the model, keeping the importance of model accuracy into consideration. 6. Deployment of the Project

Managing AI projects is like cooking a complicated dish. You need clear instructions to know what you're aiming for. You also need a team with different skills, like chefs and helpers, to work together. We use a flexible way of working called "agile" to keep improving our recipe. We try out different ingredients and methods to see what works best. We check our progress regularly to make sure everything is on track. We also keep an eye on any rules or guidelines we need to follow.

If you're developing regular software projects, the framework I found the most successful is Scrum. However AI projects usually differ from regular software projects. The most importan factor here is the research aspect. With AI projects you can stay in plateau for a while with little improvement. Then everything clicks and you take that multiple sprint roads just in days. So a following a framework firmly would not be ideal. You have to put emphasis on flexibility and patience.

The Data and the Models are vice versa in any Development of AI project. The main theme of AI model is training on Data. so aspect of Preprocessing and correlation of data is way more mandatory and considered as main aspect to do so. You have to look for duplicate values and drop them in first thing first. then deal with missing data to figure out the solution either you are ready for train or not. Look for any outliers in Data and figure out the aspect of Model accordingly. In Reinforcement Learning. You must consider it as possible condition to use updated data as a pipeline channel to fine tune model and that continuous retraining is mandatory to make it updated

The Cloud computing services and many aspect of such services like AWS, AZURE , Linode, GCP and many more are action to produce the desire action situation for model to connect as one link server to many users but in edge devices like a car where software must not to connect with Internet always. must be installed model in their devices & IOT tech services. The challenge inded is to find a best solution among them if you deploy the edge devices then tfLite version is considered as best option but if you just add that into any web area. you must has to host a docker profile as well. in AWS pipeline, you have to Build Docker using CodeBuild Services and call docker from ECR to enable it. Lambda function is recommended for Server-less case.

Dear AI, in order to help the community answer better your strange generated questions, try to include some clues beforehand… How an AI project would be different at such a point than it needs another way to be handled? As always, I recommend Extreme Programming and Continuous Delivery for any professional/commercial software that gets updated.