This 'quick start' introduction was created in part by learning from the production of our 3-hour on-demand course for O'Reilly Media Learning Platform. The course was authored by Charles D. Landau.
Accurate data acquisition is paramount in your model development process. TensorFlow offers a comprehensive suite of data transformation functions to ensure the proper formatting of your datasets. Utilizing the 'TensorFlow Transform' package, you can seamlessly ingest data and conduct essential transformations.
Once data is in the requisite format, you can embark on the training phase. TensorFlow Core equips us with the essential low-level tools for model development and training. Here, you have the flexibility to implement diverse neural network architectures, tailor the layers, and fine-tune hyperparameters to optimize model performance.
Upon the successful completion of training, optimization should become your focal point. You can employ advanced techniques like strip pruning and quantization to reduce model size and complexity. Furthermore, you'll leverage TensorFlow Lite to facilitate model deployment on resource-constrained devices, including mobile phones.
At this juncture, you'll transition to model deployment. TensorFlow Serving is a highly scalable model server, and is our platform of choice for production deployment. This involves configuring a REST API port, exporting the model to disk, handling dependencies, and executing server launch commands. To interact with your model, you can employ the 'request' package to make JSON requests and retrieve predictions.
The culmination of your model development journey necessitates a comprehensive integration strategy. From data understanding to model training and deployment, this intricate process entails a deep understanding of the data, rigorous model development, and meticulous deployment via a model server. TensorFlow empowers us with the indispensable tools to navigate this journey effectively, allowing us to create highly proficient machine learning models.
