Efficient machine learning (ML) development and deployment are a competitive advantage for most enterprises. Collecting data and managing the cycle of model creation, deployment, and debugging within a company’s solution stack can create unexpected circumstances and challenges.
Decision-makers and data scientists use a data labeling platform to speed up project production and spend hours analyzing datasets, looking for better algorithms, training new models and then passing the torch over to data engineers to run these models in production.
In this phase, data scientists may overlook problems that may arise, and it only worsens because data engineers have no background on how the models are structured. Many applications in the world of ML don’t actually scale to production because of these unforeseen challenges.
Furthermore, small companies that don’t have data engineers usually result in poor construction of deployment pipelines and update and reliability issues of ML models in production. If your company suffers from this scenario, here’s a short guide for deploying ML models faster and creating a new competitive advantage.
Shorten the ML development cycle
The first essential consideration in data science is the quick turnaround in the model production, driving value for the enterprise. The complexity of the process usually slows down data scientists during the development phase. As a result, success in the deployment is further pushed back.
Using an appropriate tech stack that supports speed in the model development is one critical factor to look into. A good ML platform makes everything easy, from creation up to deployment.
When planning to amend the ML development cycle using an ML platform, there are features to look for and prioritize, such as:
- Results visualization for informed developments
- A push code from a control management system to a production
- Increasing the visibility of the ML code to the entire team (pull, branch, and fork)
- Working from a GUI or a command line
- Output loops back to the process
Heighten team visibility and collaboration
As the data science team grows, complexity also heaps up together with the combination of tools used. It’s ideal for the workflow to have a tech stack that enhances team productivity and collaboration.
You may use a source control and orchestration system, a typical traditional software collaboration technique. By doing this, you can manage the model versions and algorithms accordingly. The team gets access to increased visibility and push codes as a part of a simple and automated workflow.
Studies suggest that for a team to achieve better results, it should consider collaboration technologies as teammates and not just tools.
Enhance failure identification
Another strategy that increases the velocity in ML deployment is the enhancement of fault identification. In the ML world, identifying faults is a challenging task as there are multiple technologies data scientists use to deliver a model into production.
In this regard, utilizing continuous integration and continuous delivery workflow helps automate the process, making the system more resilient and more straightforward in determining failure. A good platform can provide real-time fault reports in the user interface and the control management system.
Improve the ability to reproduce the model
When it comes to ML workflows, one major headache is reproducibility. Reproducibility is the ability to copy an ML workflow that produces the same results as the original. Lack of reproducibility holds back a model from a successful deployment.
There are excellent platforms that contribute significantly to the acceleration of ML workflows. These can help the team establish efficiency by fully managing codes and keeping correct versions when fully utilized.
Consider the cost of training models
Lastly, building and training an ML model isn’t cheap. According to phData, the bare minimum cost for model deployment and maintenance of a model is around $60,000USD over the first 5 years. The costs can get you out of a good position without a proper solution and platform.
A good platform can help allocate the exact amount of compute costs required for training a model. A precise compute cost provides visibility and accuracy of the model’s actual training cost. In addition, following best practices in the machine learning industry today ensures that you don’t end up in a dead-end situation because of unforeseen costs.
For a long-lasting business transformation, it’s necessary to explain the importance of machine learning’s good foundation to your partners and stakeholders. You may discuss the costs of development and deployment over the years.
A roadmap can also be a great illustration of proper planning for your ML program. The key is to select partners who understand the importance of your program and will help you sell your vision to stakeholders.
Successful implementation of a platform and best strategies in an ML development environment requires increasing efficiency and speed in the development, enhancing team collaboration, successful failure identification, improving workflow reproducibility, and lowering proper allocation of costs.