Is The Pace Of AI Development Slowing Down?
Got Data Science Platform, Visualization, and MLOps Tools, yet struggling with scaling AI? Feature Engineering holds the key to faster development!
Data science, analytics, and BI leaders in disparate industries such as financial services, retail, and manufacturing have been spending heavily on AI tools, upgrading data infrastructure, augmenting BI with ML. Your organization may already have an AI Center of Excellence (CoE) to support LoB’s where the teams are building predictive applications that can predict churn, detect fraud, and forecast inventory. Yet, for the vast majority of enterprise customers, the AI development has been slow, AI initiatives have not scaled according to expectations. What can you do to scale AI development, accelerate adoption and propel innovation?
You need to step back, analyze the data science process and focus on three core buckets in the development workflow – Data Preparation, Feature Engineering, and Machine Learning. More specifically, answer three critical questions:
- Who in the organization will prepare data and get data ready for advanced analytics?
- Who is responsible for Feature Engineering – generating features, evaluating features, and selecting the best features? Do they have the required skills and experience?
- Who will take prepared data, features and generate ML models to solve business problems
Let’s drill down on these three areas and understand the process, the people involved, and the outcome of each of these three steps:
Data preparation – aka data wrangling – is the process of cleaning, structuring, and enriching raw data into the desired format to make it ready for AI and ML. It is the first step in any AI and ML workflow before ML algorithms can process data to make predictions. The available data may come in raw form or stored in an unsuitable format for ML. The datasets may have missing or invalid values that could lead to inaccurate or misleading outcomes. Or, the data may lack useful business context and require some preprocessing or enrichment. Data preparation can often be one of the most time-consuming parts of the AI process. Data analysts, architects, and data engineers are responsible for this part of the process. They use various tools for preparing data, such as Trifacta, Paxata, Alteryx Designer, etc. The prepared data is then ready for AI and stored in analytics data marts.
The next step in the data science workflow requires providing high-quality input data containing relevant business hypotheses and historical patterns, aka Feature Engineering (FE). FE is the process of applying domain knowledge to extract analytical representations from raw data, making it ready for machine learning. It involves using business knowledge, mathematics, and statistics to transform data into a format that can be directly consumed by machine learning models. It starts from many tables spread across disparate databases that are then joined, aggregated, and combined into a single flat table using statistical transformations and/or relational operations. Feature Engineering is critical because if we provide wrong hypotheses as an input, ML cannot make accurate predictions. The quality of any provided hypothesis is vital for the success of an ML model. Practical FE is far more complicated than simple transformation exercises such as One-Hot Encoding (transform categorical values into binary indicators to utilize ML algorithms). To implement FE, you need to write hundreds or even thousands of SQL-like queries, performing a lot of data manipulation, as well as a multitude of statistical transformations.
Feature engineering requires technical knowledge but, more importantly, domain knowledge. The data science team builds features by working with domain experts, testing hypotheses, creating and evaluating ML models, and repeating the process until the results become acceptable for businesses. This continuous build-test-rework process slows down the AI development workflow. Despite all the tools available for data preparation and AutoML, the feature engineering part continues to be the most challenging, iterative, time-consuming, and resource-intensive.
The ML stage is all about developing the best machine learning models to address specific business problems. It involves analyzing multiple machine learning algorithms and identifying the algorithm that delivers the best accuracy. Production and operationalization is the final stage that puts the data science pipeline into production to provide business values to the business team.
AutoML tools have solved this third essential step with automation. Several ML platforms offer the ability to automatically build ML models and deploy them at the touch of a button. So while the data preparation and ML issues have viable solutions, the feature engineering part remains shrouded in mystery.
Since FE is the most human-dependent and time-consuming part of AI/ML workflow, FE automation has vast potential to change the traditional data science process. It can significantly lower skill barriers beyond ML automation alone, eliminating hundreds or even thousands of manually-crafted SQL queries, and ramp-up the data science project’s speed even without a full light of domain knowledge. FE automation also augments your data insights and delivers “unknown- unknowns” based on the ability to explore millions of feature hypotheses just in hours enhancing your ability to think outside the box and upending strategy and innovation .
There are many reasons why your AI strategy won’t scale, but there is a high likelihood that you are spending too much time on data preparation and feature engineering. Automation can solve both of these problems. FE automation is the critical area that can simplify AI/ML for enterprises, enable more people, such as BI analysts or data engineers, to execute AI/ML projects and make enterprise AI/ML more scalable and agile. Automated FE also accelerates the work of experienced data scientists making them more productive. They can leverage automated feature building, reduce the amount of time spent on repetitive tasks and quickly get to the building and validation of ML models for complex business problems. With FE automation, there is no need to hire more data scientists. You can scale AI development with existing staff and address all the predictive analytics and digital transformation initiatives in the pipeline.
The solution to traditional (manual) feature engineering is feature engineering as a service – a SaaS product that can fit into your existing data architecture, ingest data from data lakes or data marts, and automatically produce relevant features in a feature store.
Interested in automated feature engineering? Book a demo and check out how you can automatically build and evaluate millions of AI Features in a fraction of the time.