Building Machine Learning Pipelines? – How to Set Yourself Up for Success
There are so many fantastic benefits machine learning can offer to a business today. Machine learning pipelines have impacted the technology industry tremendously.
However, in the internet-driven world, speed and accuracy are among the most important driving factors of success. A machine learning pipeline can give your business a boost and deliver more accurate and timely insights to fuel your decision-making process, among other things.
Having a properly-built pipeline for various ML models can help a business achieve its main objective – to gain complete control over those models. A properly-built, well-organized, and regularly-maintained machine learning pipeline allows for more efficient and flexible implementation.
When we say ML model, we refer to the model artifact that can be programmed and trained. Machine learning involves a wide variety of learning algorithms that are programmed to find and identify patterns in the training sets of data to perform two operations:
- Map the input data attributes to the target
- Output ML models that collect these patterns
An ML model can have many dependencies but what matters most is that the information is stored in a central repository to ensure that all the ML pipeline components are secured and available with all their essential features both online and offline for deployment.
Machine learning pipeline components
To build a machine learning pipeline, you must first determine the main sequence of components. These are a unique compilation of complex computations programmed to navigate data sent through the components.
The ML pipeline builder can use computation to manipulate both data and components. Many believe that pipelines are one-way flows, but they are wrong. Cyclic in nature, machine learning pipelines work both ways and can enable iteration needed for improving the scores of the ML algorithms, thus making your ML models fully scalable.
A typical machine learning pipeline would include the following processes:
- Data collection – gather and merge raw data to create a unified, organized framework for collecting data from multiple data sources.
- Data cleansing – automatically detect any errors, duplicates, missing values, and outliers to eliminate them and ensure you receive the most accurate predictions.
- Feature extraction (dimensionality reduction and labeling) – transform raw data into machine learning features that your pipeline needs to learn from the extracted information.
- Model validation – automatically test, train, and validate your ML models to evaluate your pipeline architecture to see how accurate your ML pipeline is.
- Visualization – once you have your model validated, start making predictions across all your users.
Data collection and cleaning are the primary functions of data management and the main tasks of any machine learning engineer. They are essential to interpreting data and recognizing meaningful patterns. However, the biggest challenge is to gather the correct data.
Its accessibility and quality are the biggest challenges businesses will encounter in the initial phases of building machine learning pipelines. If the benefits of your data management outweigh the costs of capturing and analysis, your machine learning pipeline is as efficient as it should be.
It should also allow you the ability to easily optimize value from all your data. One of the best tools for ensuring quick access to your data is Presto – the most popular SQL query engine that allows businesses to perform interactive querying of vast loads of data quickly.
While your machine learning pipeline should allow you to fully optimize value from all your data sources to provide quick access to the data in real-time, it should also allow you to move your data seamlessly at any scale. That’s where a solution like a managed Pulsar can be of fantastic assistance as it has the power to automate any data movement at any scale.
We recommend you consider using a data lake to ensure you have both quick access and the capacity to move data at any scale. It is a centralized data repository that allows businesses to store data, both structured and unstructured, at any scale while also enabling ad-hoc data analysis. You can use a data lake to apply multiple processing frameworks and analytics to the same data sets.
Use cases for ML pipelines
Since the lifespan and effectiveness of your ML pipeline depend on your ML models, their life cycle needs to be as adaptable to model monitoring and fine-tuning as possible. Since you’ll have new data coming in regularly, the ability to adjust your pipelines to the ever-increased data flows is paramount for ensuring the best outcomes.
Fortunately, modern neural networks are constantly being improved so that they can operate even with vague data sets or when you don’t have enough labeled training data. There is even a new ML model called meta-learner that can infer the values predicted by lower AI models that are built on reinforcement learning tasks, image classification, etc.
In case a meta-learner predicts the right model, it can contribute to optimizing the lower level AI model’s dataset tuning, hyperparameters, and architecture. There are also optimized frameworks, like Google’s Snorkel, that rely on versatile corporate knowledge resources like knowledge graphics, ontologies, and internal data models to generate training data for ML models at a network scale.
At the moment, businesses are having trouble deploying machine learning pipelines at full scale for their services and products due to so many challenges, such as:
- Talent searching
- Team building
- Data collection
- ML model selection
The best way to solve these problems is to ensure your organization is harvesting the full potential of machine learning and AI. To ensure this, you need to create service-specific frameworks and develop the right tools to aid the existing ML models.
If you build a machine learning pipeline for your company, you’ll be able to make continuous predictions by processing continuous streams of unprocessed data to make your data management system extra productive and up-to-date. It will also be fully ready for real-time optimization at scale.
You will be able to automate every step of your ML pipelines and make their full power readily accessible to your employees. You can even increase the productivity of your data science team by allowing them to focus on modeling while ensuring every team gets the information they need to tackle their daily tasks.