As cloud costs rise, organizations are increasingly looking for ways to save money while expanding their AI initiatives. Artificial intelligence (AI) is a crucial component of business success and growth, but it often comes with significant resource demands and a sizable portion of the IT budget. The current approach to handling data pipelines and infrastructure in AI is messy, complicated, and inefficient. However, this also means that there is ample room for improvement and the opportunity to streamline processes for greater efficiency.
AI’s Heavy Resource Usage
AI systems require a surprisingly large amount of computing resources. They require sufficient RAM for handling complex data structures, CPU power for performing mathematical transformations (especially in matrix mathematics), network bandwidth for moving data before and after transformation, and storage space to store detailed data. Additionally, there is a shortage of skilled data engineers, whose services tend to be costly. Data scientists’ rapid iterations and experiments often divert data engineering resources toward building new data extracts and translating feature engineering pipelines from Python to more scalable languages like SQL or Spark. These iterations and experiments can be time-consuming, even though many of the results are ultimately discarded in favor of better ideas.
Feature engineering, a critical process in AI, involves transforming raw data into a format suitable for machine learning algorithms. This process entails intricate calculations and data transformations, often resulting in the movement of data across the network. However, unlike other aspects of AI, feature engineering processes today are predominantly manual and inefficient.
Inefficient Caching
Recalculating feature values is not only demanding in terms of resources but is also a costly endeavor. Caching, on the other hand, presents a solution to mitigate these costs. By storing frequently accessed feature values and intermediate results, and retrieving them when needed, caching reduces the strain on resources and eliminates unnecessary calculations. It’s like having a readily available storage space that saves time, money, and effort.
However, it’s important to note that while caching offers cost-reduction opportunities, it can also introduce challenges of its own. Building and maintaining a robust caching system can be expensive and complex. Intelligent decision-making is required to determine what feature values to cache and how often to refresh them. Optimizing the caching strategy becomes essential to strike the right balance between efficiency and resource utilization.
To harness the power of optimization and automation, organizations can adopt intelligent caching tools. These tools make informed decisions about which feature values to cache and when to refresh them, resulting in cost savings, smoother workflows, reduced latency, and optimized AI systems.
Spaghetti SQL
In the realm of AI projects, extracting and joining data in a time-aware manner is a crucial yet intricate task. This process holds the key to success, as accurate predictions and insights depend on gathering and combining data while considering time-related factors. However, one common challenge that arises is dealing with “spaghetti” SQL scripts. Although functional, these scripts are often cumbersome to maintain and optimize, leading to a proliferation of features without reusability. The complexity of the codebase increases, hindering scalability and flexibility. Making changes or enhancements becomes challenging, as they may introduce errors or inefficiencies.
Moreover, there is significant inefficiency in the double-processing of feature engineering. Initially, data scientists write code in Python, leveraging their expertise in experimentation and iteration. However, this code is then handed over to data engineers, who rewrite it in SQL or Spark to ensure scalability. This redundant process not only consumes additional time and effort but also increases the risk of errors and inconsistencies.
To address these challenges, there is a need for automatically generated and optimized SQL or Spark code. Such a solution brings significant benefits. Firstly, it reduces compute resources by generating efficient SQL queries. Executing these queries on scalable cloud data platforms can help streamline the data extraction and joining processes, resulting in cost savings and improved performance. Secondly, generating SQL code eliminates the manual translation step, reducing the risk of errors and inconsistencies. It frees up data engineers’ time, allowing them to focus on more critical tasks rather than rewriting code.
Feature Explosions
Feature explosions, or the creation of numerous features in AI pipelines, are another challenge that affects cost and efficiency. Data scientists often generate a multitude of features, many of which are either duplicates or never used in the final models. As models evolve and are retrained, redundant features can accumulate and clutter the pipeline. These unused or duplicated features contribute to increased storage requirements, longer processing times, and higher costs for computing resources.
To mitigate feature explosions, organizations can implement self-organizing AI catalogs with built-in governance measures. These catalogs enable data scientists to manage features more effectively, promote reusability, and proactively avoid redundancies. By fostering collaboration and providing a centralized repository for features, these catalogs ensure that data scientists can leverage existing features instead of reinventing the wheel.
Moreover, proactively avoiding duplicates and near duplicates is crucial. A self-organizing AI catalog can step in by recommending existing features to data scientists, helping them steer clear of redundant efforts and encouraging the reuse of valuable insights already available within the system. Creating features in a low-code or no-code way can help reduce redundancy and promote reuse as well.
Self-organizing AI catalogs automatically clean up wasteful dormant features, reducing resource usage and costs.
Unnecessary Data Movements
Traditionally, the data science process entails moving vast amounts of detailed data from secure warehouses to less secure machine learning (ML) environments on local machines. Once in these local environments, the data is transformed using Python into features suitable for machine learning algorithms. The results are then sent back to the database, ready to be consumed by other systems. However, it’s important to note that much of this data movement is actually unnecessary.
This is where feature engineering platforms come into play. These platforms allow data scientists to declare feature transformations in Python, directly on their local machines, without the need to move the data itself. By processing feature materializations within the database, these platforms take advantage of shared resources, optimizing efficiency and reducing unnecessary data movement.
Additionally, feature engineering platforms help limit the volume of materialized data that can be accessed outside of the database. Instead of working with the entire dataset, data scientists can focus on accessing small samples of data, providing a balance between efficiency and the need for valuable insights and diagnostics.
Minimizing data movements is not just best practice for data security, it also saves you money.
Manual Pipelines
In the realm of data science, a frustrating cycle of double processing often occurs, resulting in inefficiencies and wasted resources. Data scientists and data engineers find themselves performing redundant tasks, with data engineers translating Python scripts into SQL or Spark, consuming valuable time and diverting attention from critical responsibilities such as ensuring system stability.
AI projects compound this complexity by requiring separate data pipelines for training and production. This duplication intensifies challenges, increasing the likelihood of errors and inefficiencies in the codebase. Manual code creation becomes a breeding ground for mistakes, necessitating thorough testing.
Intelligent feature engineering platforms offer a solution. By streamlining workflows and automating manual tasks, these platforms free up valuable time for data engineers to focus on essential responsibilities. They simplify the testing process by automating workflows and generating code, reducing errors and testing requirements.
By eliminating double processing, abstracting complexity, automating workflows, and simplifying testing, these platforms streamline the development cycle, boosting efficiency for data teams, and saving you money.
In conclusion, organizations can effectively reduce AI infrastructure costs without compromising the success of their projects. By identifying and replacing inefficient processes, minimizing unnecessary data movement, adopting modern feature engineering platforms, and focusing on automation and optimization, businesses can streamline their operations and drive better outcomes. Embracing these improvements not only results in cost savings but also enables organizations to unlock the full potential of AI while efficiently managing resources and budgets.
