It only takes a quick look around to see that the use of machine learning (ML) is more prevalent across industries than ever before! Taking into consideration the growing variety of use cases where ML is applicable as well as ongoing developments in the technology and its associated techniques, it’s very important for both veteran users as well as new adopters of ML to constantly experiment and fine-tune their model processes in order to best utilize talent and reach tangible business value.
Dataiku’s suite of dedicated capabilities enables you to efficiently build as well as continuously evaluate your ML models using the latest techniques and state-of-the-art features like AutoML. Check out the video below to learn more about the powerful ML capabilities of Dataiku and watch them in action or keep reading for a quick overview.
Delivering More Models With AutoML
Getting both data scientists as well as analysts out of monotonous work and into the value-add, rewarding projects that they want to work on is a great aspiration but, without the support of adequate tools, it’s just a pipe dream. Here’s where the accessibility of AutoML opens up a whole new world, allowing people with diverse skill levels and varying expertise to collaboratively and successfully work with models.
All in an easy-to-use, straightforward interface, Dataiku AutoML provides algorithms from leading frameworks for prediction, clustering, time series forecasting, and computer vision tasks to help people across teams to access meaningful insights from data.
Transparency is a key to successful operationalization which is why Dataiku augments the model development process with a guided methodology, built-in guardrails, and white-box explainability so both data and domain experts alike can build and compare multiple production-ready models. With the additional visibility and access provided by Dataiku, more models can be safely deployed into production environments.
Feature engineering is the utilization of domain knowledge to transform raw data to improve model performance and accuracy. Feature engineering involves constructing variables (known as features) from existing data so that the input dataset will be compatible with machine learning (ML) algorithm requirements.
In Dataiku’s feature store, data users of all types can discover feature sets and easily import them into their projects, expediting the entire feature engineering process.
Additionally, AutoML in Dataiku provides automatic feature generation and reduction techniques. You can use AutoML to apply handling strategies for feature selection, missing values, variable encoding, and rescaling based on data type. Then you are able to either accept the default settings or quickly modify and customize your set-up.
Dataiku lets you expand projects using the techniques and languages best suited for your team. Advanced data scientists can extend the visual ML interface by adding a custom algorithm using Python, or programmatically develop models using Python, R, Scala, Julia, PySpark, and other languages. Note that, no matter where a model is developed or expanded, Dataiku will remain your core platform for central deployment, monitoring, and governance.
Plus, to ensure that any external efforts are captured and interpretable across different teams, Dataiku captures the details of these experiments and automatically produces model comparisons and explainability reports for sharing.
Model Validation and Evaluation
The long-term success of ML projects relies on teams' ability to deliver reliable, accurate models with explainable results. Dataiku provides numerous features for validating and evaluating models, from design to deployment.
Data scientists can take advantage of k-fold cross tests, automatic diagnostics, and model assertions for sanity checks during the experimentation phase. In addition to these properties, interactive performance and interpretation reports are also accessible including fairness analysis, what-if analysis, and stress tests.
Time Series Analysis and Forecasting
With Dataiku’s visual interface, business teams and data science teams can develop, deploy, and maintain statistical models and or deep learning forecasting models in Dataiku.
Dataiku provides an array of tools for time-series exploration and statistical analysis and preparation tasks like resampling, imputations, and extrema & interval extraction.
Visual and Code-Based Deep Learning
Using deep learning in data projects and business applications has never been easier than it is with Dataiku’s familiar framework and experience for model design, deployment, and governance.
Define custom deep learning architectures with Keras and TensorFlow, or use pretrained models, transfer learning, and no-code interfaces for computer vision tasks such as image classification and object detection.
Scale With Managed Spark on Kubernetes
Have large computation or model training jobs? Not a problem. You can automatically and efficiently scale workloads with on-demand, elastic resources powered by Spark and Kubernetes on your cloud of choice using Dataiku.
Simplify data scientists’ tasks with pre-configured and fully managed clusters that reduce the complexity of containerized infrastructure. This way, data science teams can place time and resources into work that will create a larger impact.
As displayed in the video and explained through the overview above, it will benefit organizations greatly to take advantage of Dataiku’s dedicated capabilities, including the advanced AutoML feature, which enables efficient ML model building and continuous evaluation. By leveraging Dataiku's state-of-the-art features and techniques, organizations can begin to effectively harness talent and unlock tangible business value.