Strategies on Increasing Machine Learning Model Production Efficiently
In a recent blog post from Dataiku, the leading data science, machine learning, and AI platform, Lynn Heidmann explored ways companies can develop higher numbers of machine learning models without necessarily hiring more data scientist resources. Here are highlights from Heidmann’s blog post.
Businesses that aim to develop machine learning models require data scientist resources to produce them. The required ratio of data scientists to the number of machine learning models produced is no longer a linear one due to the options available to businesses. This doesn’t mean, however, that the contributions of data scientists are trivial. In fact, their value in the process cannot be understated. The expertise in machine learning model creation, tuning, monitoring, and revision are vital in making the machine learning models interpretable, explainable and deployable. Businesses can balance the output of their data scientists cost-effectively by adopting the following strategies.
Delegate Prerequisite Activities to Data Analysts
Data scientist productivity can be boosted by delegating prerequisite activities such as data wrangling and preparation to data analysts. Doing so allows the data scientists to focus their efforts on developing the machine learning models and AI projects. Proper delegation of work between data scientists and data analysts is achievable if businesses utilize workflow tools that allow seamless collaboration. These tools must also be flexible enough not to limit data scientists and allow them to retain creativity.
Provide “Self-Service” options for less-complex projects
Some internal enterprise demands for machine learning models can be met by providing tools that other team members in the company can use to design and experiment. Implementing this concept of data democratization by implementing a “self-serve” analytics program allows data scientists to spend their time on more high-impact projects and at the same time still allow employees who need access to data or a custom report to get what they need.
Simplify Model Deployment
The value of machine learning models lies in speeding up essential but mechanical business processes (examples include real-time pricing, loan application approvals, or real-time fraud detection to name a few). That value can only be realized once the model is deployed into production, known as operationalization. It is important that this phase be made well defined and structured to avoid inefficient use of data scientist resources.
Providing data scientists the proper tools to speed up their machine learning models’ time-to-market will make more efficient use of their time. Allowing data scientists access to development tools that help make deployment more efficient and straightforward allows them to move onto the next development project.
Companies that need to produce machine learning models faster and at scale should learn about and utilize AutoML. AutoML or augmented analytics can introduce efficiencies into the machine learning process.
The strategies discussed by Heidmann in her blog post all have one thing in common: The need to have the correct tools to help data scientists be successful. Fusion Professionals are strategic partners with Dataiku in Australia and can help employ the best practices for your enterprises machine learning, AI and Data Analytics requirements
Many organisations don’t realise it, but in our current environment Data has become the main differentiator in the market. Most…MORE INFORMATION
Professional services, one of the fastest growing sectors of the Australian economy, covers a broad group of companies and organizations…MORE INFORMATION
We experience an increasing polarisation in our political landscape with tribalism becoming a real issue. This is partially to be…MORE INFORMATION
Oracle’s introduction of the self-driving, self-securing, and self-repairing Autonomous Database draws upon its decades of expertise in automating databases and…MORE INFORMATION
In a recent blog post from Dataiku, the leading data science, machine learning, and AI platform, Lynn Heidmann explored ways…MORE INFORMATION
“With Great Power Comes Great Responsibility” One of the biggest ongoing responsibilities that comes after commissioning an Exadata appliance is…MORE INFORMATION
According to Constellation Research, a little more than half of traditional Fortune 500 companies have disappeared due to the lack…MORE INFORMATION
Fusion Professionals has signed a partnership agreement with Dataiku, one of the world’s leading machine learning platforms that moves companies…MORE INFORMATION
Statistical language models apply probability distributions to a sequence of words. These models are finding increasing use as natural language…MORE INFORMATION
Challenges The Company, one of Australia’s largest and fastest growing Telco companies had 2 primary SharePoint environments that had different…MORE INFORMATION
Containerization allows applications to run on any machine- anytime, anywhere so long as they are compatible. By virtualizing your OS,…MORE INFORMATION
So you’ve finally decided that the cloud is safer than corporate data centers and digital assets and you’ve chosen to…MORE INFORMATION
Building a system that houses your organisation’s data can be daunting, especially now that data acquisition is growing rapidly. The…MORE INFORMATION
Human-to-machine communication has not yet been perfected, but enterprises are already beginning to integrate this groundbreaking technology into their operations,…MORE INFORMATION
Fusion Professionals has signed a partnership agreement with MapR Technologies, provider of the industry’s leading data platform for AI and…MORE INFORMATION
“Big data is at the foundation of all of the megatrends that are happening today, from social to mobile to…MORE INFORMATION
In recent years data volumes have been increasing dramatically. This has created major challenges for traditional analytics platforms in terms…MORE INFORMATION
With the increasing volumes of data that can be cost effectively stored in the cloud, comes increasing responsibility. The current…MORE INFORMATION
With the advancement of technology and abundance of data your business receives on a daily basis, companies are now in…MORE INFORMATION