What is machine learning development?
Machine learning development is the process of developing machine learning algorithms and models. This can encompass setting objectives, collecting and preparing data, choosing evaluation protocols, and developing the overall model. All of these attributes must be ‘developed’ for accurate algorithms and proper machine learning (ML). But machine learning development can go beyond individual algorithms to encompass the development of a full pipeline and management system. This part of development is often coupled with a machine learning development platform.
What is a Machine learning development platform?
A platform geared around machine learning development is a software or system that helps develop single algorithms as well as the full ML process for your organization. Going away from a monolithic architecture, a machine learning development structure allows for splitting and reusing independent workflow parts as microservices. This can include connecting to data storage along with preparing the data; securing and deploying models; and pipelining different, proven pieces of your overall models to scale your portfolio.
Why is a machine learning development platform so useful?
Machine learning models take time and resources to fully run. For most organizations, the time to get a custom model running from the ground up can take a full year, if not longer. Even when a model has similarities or a duplicated beginning, it can take data scientists extensive time to pull and update previous sections of code to match new projects. And that doesn’t even begin to touch on potential errors from incorrectly piecing apart full models. What about projects that have switched hands or programming languages? How does the new scientist parse through a previous worker’s thought process to get the same conclusions?
Machine learning development platforms can help to parse out, and individually store, important or repeatable sections of algorithms and models as they are being built. These stored sections can be used to pipeline ML workflows, meaning your engineers can take advantage of previous model sections and processes again and again, without any copy and paste. The storage also acts as a central repository for the code, so needed updates can be made in one place and unified across all instances.
Along with storing reusable code, a platform can store and connect full ML operations, allowing you to manage every stage of the machine learning lifecycle, together. See your lifecycle as a whole to unify your protocols, reduce friction between steps, and help developers, engineers, and data scientists easily find everything they need. A machine learning development system can let you control the connections in your lifecycle: Connect database storage right to the code that will clean it, connect clean data right to its intended model, and deploy it all in the same place.
Speed to deployment
All of these time-saving resources mean a machine learning platform can speed time to model deployment and provide return on investment faster. A development platform can auto-prepare your data for sooner use, spot simple code errors for sooner fix, and help run model training pipelines for sooner validation. Expedited models can be appreciated by all organizations, regardless of ML maturity.
Faster model deployment is just one way an ML development platform can save you time and resources. Beyond saving time in repetition and manual processes, a prebuilt system can save time in creation. Development platforms often include access to pre-built algorithms available for plug and play or ready for customization to your unique needs. Additionally, by working with an established platform, you save major time on creating the actual platform from scratch, which is no small feat.
Why not build your own machine learning development service?
This begs the question of expertise in the development system. Your data scientist’s are trained and ready to program algorithms, not to engineer infrastructure for their data. Their job alone gives them enough to worry about. Think about how much stronger your models could be or how many more models could be created if the resources your scientists spent on learning and perfecting MLOps were devoted purely to model creation. It’s a game of opportunity cost. Using an ML development platform lets your data scientists focus on optimizing the work they specialize in, which can lead to more accurate, powerful models for your organization.
In turn, letting experts in MLOps create your development management system gives your data scientists access to a robust platform that provides all the tools they need to excel. With a development platform, you can trust the infrastructure building to MLOps and DevOps engineers that have more experience and resources for creating a highly functional, scalable system. After all, it is their full time job.
Think about expertise like the postal system. When you choose to mail a letter or gift you customize everything that goes into your package; then you let the postal or shipping service of your choice handle the process of getting the package where it needs to go. While you could spend time searching for the best route to drive your package, and your energy actually moving the package where it needs to go, you could also trust a postal service–which has decades of experience planning routes and methodically moving packages– to move your package as cost- and time-efficient as possible.
With an important delivery, trusting the expertise and experience of a postal service is the most pragmatic approach. And your ML development system is no different. Using experts in machine learning development arrangement allows you to reach model deployment faster and more accurately, while spending your resources wisely.
Opting for a pre-built platform will also save you the headache of perfecting your infrastructure. Instead of worrying about your servers and potential GPU/CPU overages, work in the cloud and pay for what you use. Letting someone else handle the infrastructure can give you peace of mind that it will work everytime.
Paying by usage on a platform built by experts also gives you the ability to infinitely scale your machine learning portfolio. Build more models, faster than ever, without needing time to upgrade your personal system to the demand. With the growing machine learning market, time is crucial and may be the difference between you and your competitors finding and launching the next big idea in your market.
At the end of the day, there is a lot of thought that needs to go into building your own machine learning development platform for managing your models and operations.
What to look for in a machine learning development system
If you’ve decided to choose an expertly built service, you’ll want to ensure it provides everything you need now and in the future. Don’t settle for automation of tedious, manual tasks and lose out on customization, security, or scale. Your platform should work with your current processes, not go against the grain. That’s why at Algorithmia, we offer model APIs that let you use the language and framework of your choice and let you deploy on our platform, in the cloud, or even an on-prem, air-gapped network.
Algorithmia offers secure connections across all your data sources for collaborative model management. Use the language you are most comfortable with and take advantage of model versioning for comparison and repeated use. Beyond individual models, Algorithmia provides access controls and governance for your whole development and operations system. Remove silos and get full visibility into your ML workflows to analyze consumption and test for speed, accuracy, and drift.
Algorithmia provides everything your machine learning development needs, every step of the way, from start to finish. Integrate your services and develop on one unified platform. Get a demo today to learn how your data scientists can deliver data insights at DevOps speed.