Ali IssaKey Takeaways from the MLOps Data Lifecycle in Production CourseAs I revisit the Machine Learning Data Lifecycle in Production course (a refresher before diving into the third course), I’d like to share…Jan 15Jan 15
Ali IssaFrom Raw Data to Model Efficiency: Mastering Feature Engineering and SelectionIn the world of machine learning, your model is only as good as the data it learns from. Transforming raw data into a structured…Dec 6, 2024Dec 6, 2024
Ali IssaData Collection, Labeling, and Streamlined Data PipelinesHLP (Human Level Preference)May 7, 20241May 7, 20241
Ali IssaMastering Data Preparation: A Comprehensive GuideWe all know that data is the most crucial element in training an AI model. Recently, we noticed the importance of this when smaller…Apr 25, 2024Apr 25, 2024
Ali IssaOptimizing MLOps: Harnessing the Power of Data-Centric StrategiesSkewed datasetsApr 3, 2024Apr 3, 2024
Ali IssaAnalysing Errors: A Comprehensive AnalysisFor any AI project you’re working on, it’s crucial to identify the most relevant metrics for your use case. This will allow for a more…Mar 28, 2024Mar 28, 2024
Ali Issa𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥𝐬🔴 When we plan to deploy a model, we need to consider the metrics that will allow us to understand and improve the model’s performance.Feb 27, 2024Feb 27, 2024
Ali IssaExploring the Dynamics of Machine Learning Lifecycle: Understanding Data Drift and Concept DriftI’ve been reading about the machine learning project lifecycle. Mainly, for a given ML project, we start by doing the following:Feb 20, 2024Feb 20, 2024
Ali IssaDeployment Patterns in a ML projectI recently encountered some deployment patterns that I’d like to share:Feb 7, 20241Feb 7, 20241
