What are custom pre-trained deep learning models?

Custom pre-trained deep learning models are models that have been trained on large datasets and fine-tuned for specific business applications. They provide ready-to-use solutions for various tasks such as image recognition, natural language processing, and predictive analytics.

How do pre-trained models benefit businesses?

Pre-trained models benefit businesses by providing high accuracy in predictions, saving time and costs associated with model training, and offering versatility for various applications. They also reduce the need for in-house deep learning expertise and can be easily scaled across different business units.

What types of business applications can use pre-trained models?

Pre-trained models can be used in a wide range of business applications, including customer segmentation, sales forecasting, inventory management, fraud detection, personalized marketing, and more.

Are pre-trained models always accurate?

While pre-trained models generally offer high accuracy, their performance may vary depending on how well the training data represents the specific business context. Fine-tuning the models to better align with the business requirements can help improve their accuracy.

What are the main challenges of using pre-trained models?

The main challenges include lack of specificity to the business needs, potential data privacy concerns, limited control over the model architecture, dependency on third-party providers, integration difficulties with existing systems, and potential biases in the model predictions.

How can businesses address data privacy concerns when using pre-trained models?

Businesses can address data privacy concerns by ensuring that the pre-trained models comply with relevant data protection regulations, using anonymized or synthetic data, and implementing robust data security measures.

Can pre-trained models be customized for specific business needs?

Yes, pre-trained models can be customized through a process called fine-tuning, which involves further training the model on a smaller, domain-specific dataset to better align with the specific business requirements.

What kind of expertise is needed to implement pre-trained models?

Implementing pre-trained models typically requires some knowledge of machine learning and data science. However, many pre-trained models come with user-friendly interfaces and comprehensive documentation, reducing the need for deep technical expertise.

How can businesses integrate pre-trained models into their existing systems?

Integrating pre-trained models into existing systems may require significant effort in terms of compatibility and integration. Businesses should work with data scientists and software engineers to ensure smooth integration and alignment with business processes.

What are the risks of relying on third-party pre-trained models?

Relying on third-party pre-trained models can create dependency on the providers for updates, maintenance, and support. If the provider discontinues the model or changes their service, it can impact business operations. Additionally, there may be concerns about data privacy and security.

How can businesses mitigate the bias in pre-trained models?

Businesses can mitigate bias by carefully selecting training data, continuously monitoring model predictions for biases, and using techniques like fairness constraints and bias correction algorithms to reduce bias in the model.

Are there any cost implications of using pre-trained models?

Using pre-trained models can significantly reduce the costs associated with developing a deep learning model from scratch. However, there may still be costs related to fine-tuning, integration, and potential licensing fees from third-party providers.

How do pre-trained models help in scalability?

Pre-trained models help in scalability by providing a consistent and reliable prediction mechanism that can be easily replicated across different business units or locations, ensuring uniform performance and insights.

What should businesses consider before implementing pre-trained models?

Before implementing pre-trained models, businesses should consider their specific requirements, potential integration challenges, data privacy and security concerns, the expertise available, and the potential biases in the models. Conducting a thorough evaluation and pilot testing can help in making an informed decision.

Custom Pre-Trained Deep Learning Predictions for Business

Description

Leverage Gravity Investments’ 20+ years in machine learning and artificial intelligence by taking our deep learning A.I predictions and adapting them to fit your investment philosophy and brand ethos.

This is a perfect add-on to your gsphere license as the predictions will be automatically integrated. Using an A.I generated return prediction has many advantages over legacy methods for generating Capital Market Assumptions (CMA) You will also have and access the pre-trained .pth pytorch model that you can query for other sources / uses. The results would be compatible with most any optimizer. We will host the model for you in Amazon AWS Sagemaker.

Our foundation in A.I predictions is trust. before any model can be evaluated as producing good predictions or any investment performance outcomes, we have to be able to trust it. This is much harder than it may appear. We have done the hard work that developed the process to ensure, monitor and safeguard trust, so that when your investors put their trust in you and you put your trust in us, that trust stands to be rewarded.

Here is how you are able to customize the prediction model:

Define the Investment Universe (up to 1000 U.S. listed securities)
Set a Time Horizon for the Predictions
Set The Objective Function (total return, risk adjusted return, alpha, omega etc.)
Set a Hurdle Rate
Set an investment style such as value, momentum, GARP, defensive, dividend.

Note: This is not for time horizons under one day.

As usual, you Chair the investment committee, we do the work. We will configure, tune, train and test the model. We will provide you with the Excel files with the results of the training. We also include our regular decile charts we use internally to evaluate our models. (see link below)

If the final pre-trained, out of sample, validation set does not beat the benchmark (default is average return of universe)- you do not pay.

We support stocks, ETF’s, currencies and futures. It can also be applied to mutual funds, but stocks is recommended.

To learn about our base model – just reach out. this page helps, but is probably dated: https://advisors.portfoliothinktank.com/ai/.

You do not need to know programming, python, data science, a.i (artificial intelligence) or machine learning to customize your own deep learning model.

Does it work? What are reasonable expectations? We can guide you. It may be instructive to learn that we switched from our quant models to the deep learning model in 2023 for our discretionary assets. It was a data driven decision.

Speaking of data; the machine learning models are feed by multiple data sources from some of the worlds most trustworthy sources such as the Federal Reserve, World Bank, NASDAQ,

our current feature count is about 500 of the most relevant, predictive investment metrics we have discovered and learned over decades of investment experience.

. This includes a cadre of macro-economic data, price and quant data and company filings including financial statements such as balance sheet, income statement, profit and loss and cash flows. We are regularly adding data and can do specific requests for clients as available. As our customer, you wont have to mess with any of this.

This price includes quarterly consults during which we can discuss and assimilate new data, research and features that we have developed in our core model.

We will offer this as a standalone, but you would be crazy, not to pair it with the optimization suite.

For > 1000 monthly inference requests or for more than 100 Million in Assets you will need an Enterprise agreement, but you can start here.