Machine learning cannot work without scalable systems. Scalable systems have been made possible by the ever-growing data that has had humongous growth since the millennium.
Before the year 2000, scalable systems were built by two types of tasks.
- Compute-heavy, which used supercomputers, MPI, and PVM to perform heavy tasks.
- Data-heavy, which started with algebra relational databases in the ’70s and was aimed at reducing data redundancy.
Machine learning is a large dataset and uses both compute heavy and data-heavy to make a scalable machine learning solutions. The whole idea behind machine learning scalability is taking a code and engaging a number of computers to make it more efficient. The most parts of scalability come from the algorithmic side.
Scalable Machine Learning Algorithms
Scalable machine learning algorithms are used to train classifiers like logistic regression. After each data set has been put into consideration in every repeated process, the predicted error is then computed, and then with respect to the parameters, the gradient is taken.
Ways of Fast Learning Algorithms
There are two ways that are followed:
- Start with a slow algorithm and speed it up.
- Build a primarily fast algorithm
By and large, ‘scalable’ means a humongous learning algorithm that can handle large amounts of data without consuming resources like memory.
Data processing tools like Spark, Hadoop, and stratosphere make scaling easier and faster especially in areas of feature extraction or data pre-processing. Another area where there is a huge potential of speeding up other components using algorithms is in parallelization when using cross-validation, in particular, to model selection, in this case, a large number of models are trained to find a combination with the best performance.
Need for Scalability
Scalability is happening in tandem with the growing demand for massive data for training models and high processing power. In this regard, infrastructure needs to be put in place to facilitate its development. There is a need for these three reasons.
- With a large reach of the internet comes an increase in data and fast internet speeds which are the basis for data models to learn from.
- Advances in technology, better fabrication techniques, and cheaper storage options are needed for high processing powers to conduct high computation tasks.
- DevOps increase the ability to deliver service and application at high velocity.
Importance of Scalability in Machine Learning
There are a few things to consider when considering Machine learning; speed, time, and cost. Scaling performs computations and handles huge sets of data with an aim of getting the most efficient, time-saving, and cost-effective way possible. Its benefits are evident in the following:
|Productivity||By having a pipeline that implements training, deployments and evaluations will avail more space for more creativity. This creates room for more experiments and better results.|
|Cost Reduction||Scaling makes use of available resources in the most acceptable way possible and makes a trade-off between accuracy and marginal cost.|
|Modularity, Composition, and Portability||Other teams can borrow the benefits of the results of the trained model and training.|
|Reduce Human Involvement||The whole point is to computerize every process to a completely automated product where a human can relax and let the machine do the rest.|
Building a Scalable Machine Learning Model
The steps involved are explained below:
1. Select a framework and language
There are many languages to choose from when constructing your machine learning model with its advantages and disadvantages. Whether Java or Python, go with the one that suits you best.
The task of choosing a framework is another thing to consider. They range from Keras, Pytorch, Caffe, and TensorFlow.
Other factors to consider when choosing a framework are; performance, third-party integrations, community support, use-case, and so on but the most important thing that you need to keep in mind is the level of abstraction. Here, it depends on the complexity and novelty of the solution in mind.
2. Identify processors
Using the right processor is a factor that will affect the developing process immensely. You can choose from CPUs, GPUs, ASICs, and TPUs. Each of these has a limitation except ASICs and TPUs. CPUs and GPUs both suffer from von Neumann bottleneck syndrome which can easily turn to bottlenecks due to the sequential processing nature.
3. Data collection and warehousing
This step is time-consuming because it has human involvement due to activities like feature selection, cleaning, and labeling. Although the use of Variation Autoencoders, GANs, and Autoregressive models require heavy computation, it has helped expand data and reduce labeling.
4. Input pipeline
Input and output hardware are crucial in the construction of machine learning in scale. Input hardware needs to be optimized to avoid hardware accelerators that cause bottlenecks. It consists of three steps:
- Extraction – it involves reading the resource and extracts data from disk, peer networks et cetera.
- Transformation – resizing, crop, rotate et cetera. Its resources come from CPU
- Loading – creates a bridge between the working memory of the transformed data and the training model. Its source is GPU and ASICs.
5. Model training
A simple learning experiment consists of data feeding of the input, forward pass, computing and correction with the aim of minimizing loss. Thereafter architectures are evaluated to get the best results.
Three main stages of training machine learning models are:
6. Explore the data
Machine learning requires a lot of data to learn from, the more data you have the better the results. But without quality data, machine learning will be scrappy and won’t work at all. Therefore, data needs to be prepared, cleaned, and most importantly labeled. Avoid confusing and ambiguous data entries.
7. Data analysis to identify patterns
You need to experiment with a few algorithms in order to get the most desired outcome. Even though machine learning does most of the work, human interaction is involved in the process of picking the right algorithm, of applying it, configuring it, and finally testing.
There are several platforms to choose from in these cases you can apply Microsoft, Google, IBM, Amazon, or frameworks like Torch, Caffe, and TensorFlow.
Finally, you have a machine learning algorithm that will analyze your data and learn from it.
8. Make predictions
Once you have a machine learning trained model, you can deploy it in the software application you’re building, upload, and host it on a cloud service or deploy it into a web platform. The results/predictions can vary depending on which algorithm you choose to use.
Scalability, as we have seen plays a major role in making machine learning more efficient. With different tools algorithms and software being developed the future of machine learning is very bright. You will see more advanced machine learning systems and more sophistication in the application of Artificial intelligence.
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