AI development life cycle

Are you Ready to launch AI projects in your organization?

r&d company

r&d company. R&D centers in Israel. Fortune 500 customers, innovative startups, and tech companies. It is a leading R&D firm since 2007. There are a few key factors to consider when contemplating AI projects and their life cycle such as, how well is your team prepared in terms of job skills, and defining the deliverables you expect them to produce before transitioning to the next stage. By setting out expectations in advance you can assume greater and more efficient control of AI projects and help your team members focus on their responsibilities and goals.

An AI project can typically be divided into three main stages, each feeding into the next in a cyclical manner:

  • Project planning and data collection >
  • Development of the Machine Learning Model >
  • Model deployment and monitoring >>>

    A.2.0. Back to planning the next iteration of the project.

A key for a successful AI project is properly and accurately defining KPIs for the model to meet. Your team sets the desired performance and goal and how it to you expect it to affect your operation/business. Once KPIs are set, the project begins and progresses according to the steps mentioned above. Success is more likely to be achieved when all stakeholders strive to the same goal and therefore it is crucial to understand what each step of AI projects entail.

r&d company

A. Project planning and data collection

r&d company:

r&d company. R&D centers in Israel. Fortune 500 customers, innovative startups, and tech companies. It is a leading R&D firm since 2007.

Defining your requirements

The first step in any AI project is to define the requirements. This means clearly understanding the problem you are trying to solve and the goals that you want to achieve. We will work with you to identify the data that you will need, the types of models that you will need to build, and the metrics that you will use to evaluate the success of your project.

Data preparation

You’ve no doubt heard the phrase “garbage in, garbage out”. This goes double for AI solutions – Artificial Intelligence systems are capable of truly incredible pattern recognition and decision making, but it absolutely must be based on the availability of data. Specifically, varied, reliable, high-quality and relevant data.

At Ready, we have the experience to sift through your organizational data to identify the parameters which will provide your AI solution with the best possible feed – and suggest means of generating larger, and more relevant, data streams – as well as labeling it to make it easier for your AI model to identify and understand its significance.

B. Development of the Machine Learning Model

r&d company

Based on inputs from the requirements step, among the decisions we will make together at the design stage are what type of ML problem are we trying to solve?

  • Supervised? If that is the case, what type?
    • Binary (Is the man caught on the security camera holding a gun or not?)
    • Multiclass (What genre is this music – jazz, classic or alternative rock?)
    • Regression (How much will the sea level on the California coast rise over the next two decades given the inputted assumptions?)
  • Unsupervised?
    • Clustering? Are you trying to group similar observations? For example, product recommendation
    • Anomaly detection? For example, malware and intrusion detection
  • Reinforcement?
    • Episodic? For example, making a robot fetch items from a wearhouse
    • Continua? Autonomous driving where an agent constantly being measured on decision it makes according to the varying inputs from the environment
    • Multi-agent: Coordination of multiple robots working together in robotic warehouse.

Feature Engineering and selection.

Engineering the right features for your model is an incredibly complex and time-consuming process – but. In most cases, it is crucial to extract the best results from ML models. Think of features and the nutrition of your model. It has to have high qualifications for the model to remain healthy and perform as expected. In this stage, our team will help you encode your business expertise in a way AI can understand. Furthermore, it is just as important to understand what not to feed the model with. There are certain elements of the data that confuse AI model and reduces their performance, and our team will make sure to select only the relevant features and feed them to the model.

Model selection
What type of algorithm should we use? Should we go for linear models? tree-base boosting? Or maybe deep neural networks? Each algorithm comes with its own pros, and it is important to keep a bigger perspective in mind. For example, financial data is mostly tabular (as opposed to vision for example). Therefore, tree-based boosting provides high ROI. If you are looking to solve a time series problem, LSTMs may provide results you expect. Our team is equipped with the knowledge and experience to make sure decisions are made to achieve your goals. Quickly.

Does the model meet desired KPIs? Is it accurate enough to meet business needs? Does it capture the number of events you wish it to? Once you reach the predefined goalpost it is time to deploy the model. And we tell you that,r&d company. R&D centers in Israel. Fortune 500 customers, innovative startups, and tech companies. It is a leading R&D firm since 2007.

C. Deployment and Monitoring

r&d company – Ready group

It is time to plug your AI into your production environment and see how the ML model performs with real data. The KPIs you have defined will now be used again to define acceptance rates. In this step, we place alerts and preemptions that are visible over a real-time dashboard that monitors the health of the model we just deployed. In most cases, the distribution of the population the model serves changes over time and the model’s predictions drift over time. For example, when interest rates increase, the demand for credit drops, and therefore credit risk models need to be recalibrated to meet the same business goals.

Also, what actions should the model take in case of a malfunction? For example, a drone-based delivery service requires a geo-location understanding of each drone, What if a few drones are lost? Should the AI model that assigns routes to drones wait or ignore and send new drones? Our team will assist in identifying critical policies required to maintain your SLA for your customers even in the event of a malfunction.

Mind you, this is not fire and forget. To see how your model performs in the real world, constant supervision and monitoring are required to identify problems and areas requiring improvement and to take in customer and staff feedback in real time. The last is crucial, as you will be depending on both to continue to provide feedback in the next cycle of improvements – and humans, unlike machines, are rarely forgiving if they feel their input goes unheeded.

Thus, you need to be certain you have staff allocated to this manpower-intensive task – or a team of highly qualified professionals, such as those provided by Ready, to handle this challenge. And we tell you that,r&d company. R&D centers in Israel. Fortune 500 customers, innovative startups, and tech companies. It is a leading R&D firm since 2007.

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